Datasets:

ftopal

/

huggingface-models-processed

like 0

# w2v with news

{}

Aviora/news2vec

2022-03-02T23:29:04+00:00

TAGS #region-us

# w2v with news

text-generation

transformers

# Eren Yeager DialoGPT Model

{"tags": ["conversational"]}

Awsaf/DialoGPT-medium-eren

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Eren Yeager DialoGPT Model

text-generation

transformers

# Eren Yeager Model

{"tags": ["conversational"]}

Awsaf/large-eren

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Eren Yeager Model

text-generation

transformers

# Rick DialoGPT Model

{"tags": ["conversational"]}

Axcel/DialoGPT-small-rick

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Rick DialoGPT Model

# ResNet This ResNet18 model was translated from the ONNX ResNetv1 model found at https://github.com/onnx/models/tree/main/vision/classification/resnet into Axon using [AxonOnnx](https://github.com/elixir-nx/axon_onnx) The following description is copied from the relevant description at the ONNX repository. ## Use cases These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches. ## Description Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity. ## Model ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. ### Input All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image. ### Preprocessing The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing. ### Output The model outputs image scores for each of the 1000 classes of ImageNet. ### Postprocessing The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check [imagenet_postprocess.py](../imagenet_postprocess.py) for code. ## Dataset Dataset used for train and validation: [ImageNet (ILSVRC2012)](http://www.image-net.org/challenges/LSVRC/2012/). Check [imagenet_prep](../imagenet_prep.md) for guidelines on preparing the dataset. ## References * **ResNetv1** [Deep residual learning for image recognition](https://arxiv.org/abs/1512.03385) He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. * **ONNX source model** [onnx/models vision/classification/resnet resnet18-v1-7.onnx](https://github.com/onnx/models/tree/main/vision/classification/resnet/README)

{"license": "apache-2.0", "tags": ["Axon", "Elixir"], "datasets": ["ImageNet"]}

Axon/resnet18-v1

2022-03-02T23:29:04+00:00

TAGS #Axon #Elixir #dataset-ImageNet #arxiv-1512.03385 #license-apache-2.0 #region-us

# ResNet This ResNet18 model was translated from the ONNX ResNetv1 model found at URL into Axon using AxonOnnx The following description is copied from the relevant description at the ONNX repository. ## Use cases These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches. ## Description Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity. ## Model ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. ### Input All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image. ### Preprocessing The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing. ### Output The model outputs image scores for each of the 1000 classes of ImageNet. ### Postprocessing The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check imagenet_postprocess.py for code. ## Dataset Dataset used for train and validation: ImageNet (ILSVRC2012). Check imagenet_prep for guidelines on preparing the dataset. ## References * ResNetv1 Deep residual learning for image recognition He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. * ONNX source model onnx/models vision/classification/resnet URL

# ResNet This ResNet34 model was translated from the ONNX ResNetv1 model found at https://github.com/onnx/models/tree/main/vision/classification/resnet into Axon using [AxonOnnx](https://github.com/elixir-nx/axon_onnx) The following description is copied from the relevant description at the ONNX repository. ## Use cases These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches. ## Description Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity. ## Model ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. ### Input All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image. ### Preprocessing The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing. ### Output The model outputs image scores for each of the 1000 classes of ImageNet. ### Postprocessing The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check [imagenet_postprocess.py](../imagenet_postprocess.py) for code. ## Dataset Dataset used for train and validation: [ImageNet (ILSVRC2012)](http://www.image-net.org/challenges/LSVRC/2012/). Check [imagenet_prep](../imagenet_prep.md) for guidelines on preparing the dataset. ## References * **ResNetv1** [Deep residual learning for image recognition](https://arxiv.org/abs/1512.03385) He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. * **ONNX source model** [onnx/models vision/classification/resnet resnet34-v1-7.onnx](https://github.com/onnx/models/tree/main/vision/classification/resnet/README)

{"license": "apache-2.0", "tags": ["Axon", "Elixir"], "datasets": ["ImageNet"]}

Axon/resnet34-v1

2022-03-02T23:29:04+00:00

TAGS #Axon #Elixir #dataset-ImageNet #arxiv-1512.03385 #license-apache-2.0 #region-us

# ResNet This ResNet34 model was translated from the ONNX ResNetv1 model found at URL into Axon using AxonOnnx The following description is copied from the relevant description at the ONNX repository. ## Use cases These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches. ## Description Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity. ## Model ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. ### Input All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image. ### Preprocessing The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing. ### Output The model outputs image scores for each of the 1000 classes of ImageNet. ### Postprocessing The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check imagenet_postprocess.py for code. ## Dataset Dataset used for train and validation: ImageNet (ILSVRC2012). Check imagenet_prep for guidelines on preparing the dataset. ## References * ResNetv1 Deep residual learning for image recognition He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. * ONNX source model onnx/models vision/classification/resnet URL

# ResNet This ResNet50 model was translated from the ONNX ResNetv1 model found at https://github.com/onnx/models/tree/main/vision/classification/resnet into Axon using [AxonOnnx](https://github.com/elixir-nx/axon_onnx) The following description is copied from the relevant description at the ONNX repository. ## Use cases These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches. ## Description Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity. ## Model ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. ### Input All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image. ### Preprocessing The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing. ### Output The model outputs image scores for each of the 1000 classes of ImageNet. ### Postprocessing The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check [imagenet_postprocess.py](../imagenet_postprocess.py) for code. ## Dataset Dataset used for train and validation: [ImageNet (ILSVRC2012)](http://www.image-net.org/challenges/LSVRC/2012/). Check [imagenet_prep](../imagenet_prep.md) for guidelines on preparing the dataset. ## References * **ResNetv1** [Deep residual learning for image recognition](https://arxiv.org/abs/1512.03385) He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. * **ONNX source model** [onnx/models vision/classification/resnet resnet50-v1-7.onnx](https://github.com/onnx/models/tree/main/vision/classification/resnet/README)

{"license": "apache-2.0", "tags": ["Axon", "Elixir"], "datasets": ["ImageNet"]}

Axon/resnet50-v1

2022-03-02T23:29:04+00:00

TAGS #Axon #Elixir #dataset-ImageNet #arxiv-1512.03385 #license-apache-2.0 #region-us

# ResNet This ResNet50 model was translated from the ONNX ResNetv1 model found at URL into Axon using AxonOnnx The following description is copied from the relevant description at the ONNX repository. ## Use cases These ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required. ImageNet trained models are often used as the base layers for a transfer learning approach to training a model in your domain. Transfer learning can significantly reduce the processing necessary to train an accurate model in your domain. This model was published here with the expectation that it would be useful to the Elixir community for transfer learning and other similar approaches. ## Description Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity. ## Model ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v1 uses post-activation for the residual blocks. ### Input All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image. ### Preprocessing The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. The transformation should preferably happen at preprocessing. ### Output The model outputs image scores for each of the 1000 classes of ImageNet. ### Postprocessing The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check imagenet_postprocess.py for code. ## Dataset Dataset used for train and validation: ImageNet (ILSVRC2012). Check imagenet_prep for guidelines on preparing the dataset. ## References * ResNetv1 Deep residual learning for image recognition He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. * ONNX source model onnx/models vision/classification/resnet URL

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # albert_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "albert_bert_summarization_cnn_dailymail", "results": []}]}

Ayham/albert_bert_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# albert_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # albert_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "albert_distilgpt2_summarization_cnn_dailymail", "results": []}]}

Ayham/albert_distilgpt2_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# albert_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # albert_gpt2_Full_summarization_cnndm This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "albert_gpt2_Full_summarization_cnndm", "results": []}]}

Ayham/albert_gpt2_Full_summarization_cnndm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# albert_gpt2_Full_summarization_cnndm This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # albert_large_gpt2_summarization_cnndm This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "albert_large_gpt2_summarization_cnndm", "results": []}]}

Ayham/albert_gpt2_summarization_cnndm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# albert_large_gpt2_summarization_cnndm This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # albert_gpt2_summarization_xsum This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "albert_gpt2_summarization_xsum", "results": []}]}

Ayham/albert_gpt2_summarization_xsum

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-xsum #autotrain_compatible #endpoints_compatible #region-us

# albert_gpt2_summarization_xsum This model is a fine-tuned version of [](URL on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # albert_roberta_new_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "albert_roberta_new_summarization_cnn_dailymail", "results": []}]}

Ayham/albert_roberta_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# albert_roberta_new_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "bert_bert_summarization_cnn_dailymail", "results": []}]}

Ayham/bert_bert_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# bert_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "bert_distilgpt2_summarization_cnn_dailymail", "results": []}]}

Ayham/bert_distilgpt2_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# bert_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_gpt2_summarization_cnndm This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "bert_gpt2_summarization_cnndm", "results": []}]}

Ayham/bert_gpt2_summarization_cnndm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# bert_gpt2_summarization_cnndm This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_gpt2_summarization_cnndm_new This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "bert_gpt2_summarization_cnndm_new", "results": []}]}

Ayham/bert_gpt2_summarization_cnndm_new

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# bert_gpt2_summarization_cnndm_new This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_gpt2_summarization_xsum This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "bert_gpt2_summarization_xsum", "results": []}]}

Ayham/bert_gpt2_summarization_xsum

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-xsum #autotrain_compatible #endpoints_compatible #region-us

# bert_gpt2_summarization_xsum This model is a fine-tuned version of [](URL on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_roberta_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "bert_roberta_summarization_cnn_dailymail", "results": []}]}

Ayham/bert_roberta_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# bert_roberta_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bertgpt2_cnn This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "bertgpt2_cnn", "results": []}]}

Ayham/bertgpt2_cnn

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# bertgpt2_cnn This model is a fine-tuned version of [](URL on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "distilbert_bert_summarization_cnn_dailymail", "results": []}]}

Ayham/distilbert_bert_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# distilbert_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "distilbert_distilgpt2_summarization_cnn_dailymail", "results": []}]}

Ayham/distilbert_distilgpt2_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# distilbert_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_gpt2_summarization_cnndm This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "distilbert_gpt2_summarization_cnndm", "results": []}]}

Ayham/distilbert_gpt2_summarization_cnndm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# distilbert_gpt2_summarization_cnndm This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_gpt2_summarization_xsum This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "distilbert_gpt2_summarization_xsum", "results": []}]}

Ayham/distilbert_gpt2_summarization_xsum

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-xsum #autotrain_compatible #endpoints_compatible #region-us

# distilbert_gpt2_summarization_xsum This model is a fine-tuned version of [](URL on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_roberta_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "distilbert_roberta_summarization_cnn_dailymail", "results": []}]}

Ayham/distilbert_roberta_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# distilbert_roberta_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ernie_gpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "ernie_gpt2_summarization_cnn_dailymail", "results": []}]}

Ayham/ernie_gpt2_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# ernie_gpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "roberta_bert_summarization_cnn_dailymail", "results": []}]}

Ayham/roberta_bert_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# roberta_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "roberta_distilgpt2_summarization_cnn_dailymail", "results": []}]}

Ayham/roberta_distilgpt2_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# roberta_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_gpt2_new_max64_summarization_cnndm This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "roberta_gpt2_new_max64_summarization_cnndm", "results": []}]}

Ayham/roberta_gpt2_new_max64_summarization_cnndm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# roberta_gpt2_new_max64_summarization_cnndm This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_gpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description This model uses RoBerta encoder and GPT2 decoder and fine-tuned on the summarization task. It got Rouge scores as follows: Rouge1= 35.886 Rouge2= 16.292 RougeL= 23.499 ## Intended uses & limitations To use its API: from transformers import RobertaTokenizerFast, GPT2Tokenizer, EncoderDecoderModel model = EncoderDecoderModel.from_pretrained("Ayham/roberta_gpt2_summarization_cnn_dailymail") input_tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') output_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") article = """Your Input Text""" input_ids = input_tokenizer(article, return_tensors="pt").input_ids output_ids = model.generate(input_ids) print(output_tokenizer.decode(output_ids[0], skip_special_tokens=True)) More information needed More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "roberta_gpt2_summarization_cnn_dailymail", "results": []}]}

Ayham/roberta_gpt2_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# roberta_gpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description This model uses RoBerta encoder and GPT2 decoder and fine-tuned on the summarization task. It got Rouge scores as follows: Rouge1= 35.886 Rouge2= 16.292 RougeL= 23.499 ## Intended uses & limitations To use its API: from transformers import RobertaTokenizerFast, GPT2Tokenizer, EncoderDecoderModel model = EncoderDecoderModel.from_pretrained("Ayham/roberta_gpt2_summarization_cnn_dailymail") input_tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') output_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") article = """Your Input Text""" input_ids = input_tokenizer(article, return_tensors="pt").input_ids output_ids = model.generate(input_ids) print(output_tokenizer.decode(output_ids[0], skip_special_tokens=True)) More information needed More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_gpt2_summarization_xsum This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "roberta_gpt2_summarization_xsum", "results": []}]}

Ayham/roberta_gpt2_summarization_xsum

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-xsum #autotrain_compatible #endpoints_compatible #region-us

# roberta_gpt2_summarization_xsum This model is a fine-tuned version of [](URL on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_roberta_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "roberta_roberta_summarization_cnn_dailymail", "results": []}]}

Ayham/roberta_roberta_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# roberta_roberta_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robertagpt2_cnn This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "robertagpt2_cnn", "results": []}]}

Ayham/robertagpt2_cnn

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# robertagpt2_cnn This model is a fine-tuned version of [](URL on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robertagpt2_xsum This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "robertagpt2_xsum", "results": []}]}

Ayham/robertagpt2_xsum

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# robertagpt2_xsum This model is a fine-tuned version of [](URL on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robertagpt2_xsum2 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "robertagpt2_xsum2", "results": []}]}

Ayham/robertagpt2_xsum2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# robertagpt2_xsum2 This model is a fine-tuned version of [](URL on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robertagpt2_xsum4 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "robertagpt2_xsum4", "results": []}]}

Ayham/robertagpt2_xsum4

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# robertagpt2_xsum4 This model is a fine-tuned version of [](URL on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmroberta_gpt2_summarization_xsum This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "xlmroberta_gpt2_summarization_xsum", "results": []}]}

Ayham/xlmroberta_gpt2_summarization_xsum

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-xsum #autotrain_compatible #endpoints_compatible #region-us

# xlmroberta_gpt2_summarization_xsum This model is a fine-tuned version of [](URL on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmroberta_large_gpt2_summarization_cnndm This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "xlmroberta_large_gpt2_summarization_cnndm", "results": []}]}

Ayham/xlmroberta_large_gpt2_summarization_cnndm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# xlmroberta_large_gpt2_summarization_cnndm This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnet_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "xlnet_bert_summarization_cnn_dailymail", "results": []}]}

Ayham/xlnet_bert_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# xlnet_bert_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnet_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "xlnet_distilgpt2_summarization_cnn_dailymail", "results": []}]}

Ayham/xlnet_distilgpt2_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# xlnet_distilgpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnet_gpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "xlnet_gpt2_summarization_cnn_dailymail", "results": []}]}

Ayham/xlnet_gpt2_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# xlnet_gpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnet_gpt2_summarization_xsum This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "xlnet_gpt2_summarization_xsum", "results": []}]}

Ayham/xlnet_gpt2_summarization_xsum

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-xsum #autotrain_compatible #endpoints_compatible #region-us

# xlnet_gpt2_summarization_xsum This model is a fine-tuned version of [](URL on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnet_gpt_xsum This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "xlnet_gpt_xsum", "results": []}]}

Ayham/xlnet_gpt_xsum

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# xlnet_gpt_xsum This model is a fine-tuned version of [](URL on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnet_roberta_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "xlnet_roberta_summarization_cnn_dailymail", "results": []}]}

Ayham/xlnet_roberta_summarization_cnn_dailymail

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us

# xlnet_roberta_summarization_cnn_dailymail This model is a fine-tuned version of [](URL on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnetgpt2_xsum7 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "xlnetgpt2_xsum7", "results": []}]}

Ayham/xlnetgpt2_xsum7

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# xlnetgpt2_xsum7 This model is a fine-tuned version of [](URL on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

text-generation

transformers

#Ayjayo

{"tags": ["conversational"]}

Ayjayo/DialoGPT-medium-AyjayoAI

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#Ayjayo

fill-mask

transformers

在2.5亿的中文语料上，进行mobie_bert进行预训练。在单卡-A100下迭代100万 steps,训练15天。

{"license": "apache-2.0"}

Ayou/chinese_mobile_bert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #mobilebert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

在2.5亿的中文语料上，进行mobie_bert进行预训练。在单卡-A100下迭代100万 steps,训练15天。

text-generation

transformers

#DialoGPT medium model (Harry Potter 1-3)

{"tags": ["conversational"]}

Ayran/DialoGPT-medium-harry-potter-1-through-3

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#DialoGPT medium model (Harry Potter 1-3)

text-generation

transformers

#DialoGPT medium model (Based on Harry Potter 1 through 4 plus 6, 18 epochs)

{"tags": ["conversational"]}

Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6-e18

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#DialoGPT medium model (Based on Harry Potter 1 through 4 plus 6, 18 epochs)

text-generation

transformers

#DialoGPT medium model (Harry Potter 1 through 4 plus 6)

{"tags": ["conversational"]}

Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#DialoGPT medium model (Harry Potter 1 through 4 plus 6)

text-generation

transformers

# Gandalf DialoGPT Model

{"tags": ["conversational"]}

Ayran/DialoGPT-small-gandalf

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Gandalf DialoGPT Model

text-generation

transformers

# Harry Potter DialoGPT small Model (Movies 1 through 3)

{"tags": ["conversational"]}

Ayran/DialoGPT-small-harry-potter-1-through-3

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Harry Potter DialoGPT small Model (Movies 1 through 3)

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-club-inductions-21-nlp-ALBERT This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "ai-club-inductions-21-nlp-ALBERT", "results": []}]}

AyushPJ/ai-club-inductions-21-nlp-ALBERT

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #albert #question-answering #generated_from_trainer #endpoints_compatible #region-us

# ai-club-inductions-21-nlp-ALBERT This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-club-inductions-21-nlp-ELECTRA-base-squad This model is the deepset/electra-base-squad2 pre-trained model trained on data from AI Inductions 21 NLP competition (https://www.kaggle.com/c/ai-inductions-21-nlp) for extractive QA. ## Model description More information needed ## Intended uses & limitations AI Inductions 21 NLP competition ## Training and evaluation data AI Inductions 21 NLP competition data ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - max_length = 512 - doc_stride = 384 - learning_rate: 2e-05 - weight_decay=0.01 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "ai-club-inductions-21-nlp-ELECTRA-base-squad", "results": []}]}

AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us

# ai-club-inductions-21-nlp-ELECTRA-base-squad This model is the deepset/electra-base-squad2 pre-trained model trained on data from AI Inductions 21 NLP competition (URL for extractive QA. ## Model description More information needed ## Intended uses & limitations AI Inductions 21 NLP competition ## Training and evaluation data AI Inductions 21 NLP competition data ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - max_length = 512 - doc_stride = 384 - learning_rate: 2e-05 - weight_decay=0.01 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-club-inductions-21-nlp-XLNet This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "ai-club-inductions-21-nlp-XLNet", "results": []}]}

AyushPJ/ai-club-inductions-21-nlp-XLNet

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlnet #question-answering #generated_from_trainer #endpoints_compatible #region-us

# ai-club-inductions-21-nlp-XLNet This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-club-inductions-21-nlp-distilBERT This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cu110 - Datasets 1.14.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "ai-club-inductions-21-nlp-distilBERT", "results": []}]}

AyushPJ/ai-club-inductions-21-nlp-distilBERT

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #question-answering #generated_from_trainer #endpoints_compatible #region-us

# ai-club-inductions-21-nlp-distilBERT This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cu110 - Datasets 1.14.0 - Tokenizers 0.10.3

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-club-inductions-21-nlp-roBERTa-base-squad-v2 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "ai-club-inductions-21-nlp-roBERTa-base-squad-v2", "results": []}]}

AyushPJ/ai-club-inductions-21-nlp-roBERTa-base-squad-v2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us

# ai-club-inductions-21-nlp-roBERTa-base-squad-v2 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-club-inductions-21-nlp-roBERTa This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "ai-club-inductions-21-nlp-roBERTa", "results": []}]}

AyushPJ/ai-club-inductions-21-nlp-roBERTa

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us

# ai-club-inductions-21-nlp-roBERTa This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test-squad-trained-finetuned-squad This model was trained from scratch on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cu110 - Datasets 1.13.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "test-squad-trained-finetuned-squad", "results": []}]}

AyushPJ/test-squad-trained-finetuned-squad

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us

# test-squad-trained-finetuned-squad This model was trained from scratch on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cu110 - Datasets 1.13.3 - Tokenizers 0.10.3

text-generation

transformers

# Echidona DialoGPT-Medium Model

{"tags": ["conversational"]}

Azuris/DialoGPT-medium-envy

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Echidona DialoGPT-Medium Model

text-generation

transformers

# Echidona DialoGPT Model

{"tags": ["conversational"]}

Azuris/DialoGPT-small-envy

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Echidona DialoGPT Model

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7371 - Matthews Correlation: 0.5481 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5298 | 1.0 | 535 | 0.5333 | 0.4142 | | 0.3619 | 2.0 | 1070 | 0.5174 | 0.5019 | | 0.2449 | 3.0 | 1605 | 0.6394 | 0.4921 | | 0.1856 | 4.0 | 2140 | 0.7371 | 0.5481 | | 0.133 | 5.0 | 2675 | 0.8600 | 0.5327 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5481326292844919, "name": "Matthews Correlation"}]}]}]}

BAHIJA/distilbert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-cola ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.7371 * Matthews Correlation: 0.5481 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.17.0 * Pytorch 1.10.0+cu111 * Datasets 1.18.4 * Tokenizers 0.11.6

text2text-generation

transformers

[Paper](https://hlt.bme.hu/en/publ/foszt2oszt) We publish an abstractive summarizer for Hungarian, an encoder-decoder model initialized with [huBERT](huggingface.co/SZTAKI-HLT/hubert-base-cc), and fine-tuned on the [ELTE.DH](https://elte-dh.hu/) corpus of former Hungarian news portals. The model produces fluent output in the correct topic, but it hallucinates frequently. Our quantitative evaluation on automatic and human transcripts of news (with automatic and human-made punctuation, [Tündik et al. (2019)](https://www.isca-speech.org/archive/interspeech_2019/tundik19_interspeech.html), [Tündik and Szaszák (2019)](https://www.isca-speech.org/archive/interspeech_2019/szaszak19_interspeech.html)) shows that the model is robust with respect to errors in either automatic speech recognition or automatic punctuation restoration. In fine-tuning and inference, we followed [a jupyter notebook by Patrick von Platen](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb). Most hyper-parameters are the same as those by von Platen, but we found it advantageous to change the minimum length of the summary to 8 word- pieces (instead of 56), and the number of beams in beam search to 5 (instead of 4). Our model was fine-tuned on a server of the [SZTAKI-HLT](hlt.bme.hu/) group, which kindly provided access to it.

{"language": "hu", "metrics": "rouge"}

BME-TMIT/foszt2oszt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #encoder-decoder #text2text-generation #hu #autotrain_compatible #endpoints_compatible #region-us

Paper We publish an abstractive summarizer for Hungarian, an encoder-decoder model initialized with huBERT, and fine-tuned on the ELTE.DH corpus of former Hungarian news portals. The model produces fluent output in the correct topic, but it hallucinates frequently. Our quantitative evaluation on automatic and human transcripts of news (with automatic and human-made punctuation, Tündik et al. (2019), Tündik and Szaszák (2019)) shows that the model is robust with respect to errors in either automatic speech recognition or automatic punctuation restoration. In fine-tuning and inference, we followed a jupyter notebook by Patrick von Platen. Most hyper-parameters are the same as those by von Platen, but we found it advantageous to change the minimum length of the summary to 8 word- pieces (instead of 56), and the number of beams in beam search to 5 (instead of 4). Our model was fine-tuned on a server of the SZTAKI-HLT group, which kindly provided access to it.

fill-mask

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/RoBERTalex # Spanish Legal-domain RoBERTa There are few models trained for the Spanish language. Some of the models have been trained with a low resource, unclean corpora. The ones derived from the Spanish National Plan for Language Technologies are proficient solving several tasks and have been trained using large scale clean corpora. However, the Spanish Legal domain language could be think of an independent language on its own. We therefore created a Spanish Legal model from scratch trained exclusively on legal corpora. ## Citing ``` @misc{gutierrezfandino2021legal, title={Spanish Legalese Language Model and Corpora}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Aitor Gonzalez-Agirre and Marta Villegas}, year={2021}, eprint={2110.12201}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` For more information visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-legal-es) ## Funding This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.

{"language": ["es"], "license": "apache-2.0", "tags": ["legal", "spanish"], "datasets": ["legal_ES", "temu_legal"], "metrics": ["ppl"], "widget": [{"text": "La ley fue <mask> finalmente."}, {"text": "El Tribunal <mask> desestim\u00f3 el recurso de amparo."}, {"text": "Hay base legal dentro del marco <mask> actual."}]}

BSC-LT/RoBERTalex

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #legal #spanish #es #dataset-legal_ES #dataset-temu_legal #arxiv-2110.12201 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL # Spanish Legal-domain RoBERTa There are few models trained for the Spanish language. Some of the models have been trained with a low resource, unclean corpora. The ones derived from the Spanish National Plan for Language Technologies are proficient solving several tasks and have been trained using large scale clean corpora. However, the Spanish Legal domain language could be think of an independent language on its own. We therefore created a Spanish Legal model from scratch trained exclusively on legal corpora. ## Citing For more information visit our GitHub repository ## Funding This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.

text-generation

transformers

# GPT2-large trained with data from National Library of Spain (BNE) ## Model Description GPT2-large-bne is a transformer-based model for the Spanish language. It is based on the [GPT-2](http://www.persagen.com/files/misc/radford2019language.pdf) model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. ## Training corpora and preprocessing The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019. To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text. Some of the statistics of the corpus: | Corpora | Number of documents | Number of tokens | Size (GB) | |---------|---------------------|------------------|-----------| | BNE | 201,080,084 | 135,733,450,668 | 570GB | ## Tokenization and pre-training The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [GPT-2](http://www.persagen.com/files/misc/radford2019language.pdf) model with a vocabulary size of 50,262 tokens. The GPT2-large-bne pre-training consists of an autoregressive language model training that follows the approach of the GPT-2. The training lasted a total of 10 days with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM. ## Evaluation and results For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne"], "datasets": ["bne"], "metrics": ["ppl"]}

BSC-LT/gpt2-large-bne

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #national library of spain #spanish #bne #es #dataset-bne #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

GPT2-large trained with data from National Library of Spain (BNE) ================================================================= Model Description ----------------- GPT2-large-bne is a transformer-based model for the Spanish language. It is based on the GPT-2 model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Training corpora and preprocessing ---------------------------------- The National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019. To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text. Some of the statistics of the corpus: Tokenization and pre-training ----------------------------- The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original GPT-2 model with a vocabulary size of 50,262 tokens. The GPT2-large-bne pre-training consists of an autoregressive language model training that follows the approach of the GPT-2. The training lasted a total of 10 days with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM. Evaluation and results ---------------------- For evaluation details visit our GitHub repository. Citing ------ Check out our paper for all the details: URL

fill-mask

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es # Biomedical-clinical language model for Spanish Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official [repository](https://github.com/PlanTL-SANIDAD/lm-biomedical-clinical-es) and read our [preprint](https://arxiv.org/abs/2109.03570) "_Carrino, C. P., Armengol-Estapé, J., Gutiérrez-Fandiño, A., Llop-Palao, J., Pàmies, M., Gonzalez-Agirre, A., & Villegas, M. (2021). Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario._". ## Tokenization and model pretraining This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a **biomedical-clinical** corpus in Spanish collected from several sources (see next section). The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2) used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences. ## Training corpora and preprocessing The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus collected from more than 278K clinical documents and notes. To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus uncleaned. Essentially, the cleaning operations used are: - data parsing in different formats - sentence splitting - language detection - filtering of ill-formed sentences - deduplication of repetitive contents - keep the original document boundaries Then, the biomedical corpora are concatenated and further global deduplication among the biomedical corpora have been applied. Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of more than 1B tokens. The table below shows some basic statistics of the individual cleaned corpora: | Name | No. tokens | Description | |-----------------------------------------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Medical crawler](https://zenodo.org/record/4561970) | 745,705,946 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. | | Clinical cases misc. | 102,855,267 | A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document. | | Clinical notes/documents | 91,250,080 | Collection of more than 278K clinical documents, including discharge reports, clinical course notes and X-ray reports, for a total of 91M tokens. | | [Scielo](https://github.com/PlanTL-SANIDAD/SciELO-Spain-Crawler) | 60,007,289 | Publications written in Spanish crawled from the Spanish SciELO server in 2017. | | [BARR2_background](https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2) | 24,516,442 | Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines. | | Wikipedia_life_sciences | 13,890,501 | Wikipedia articles crawled 04/01/2021 with the [Wikipedia API python library](https://pypi.org/project/Wikipedia-API/) starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content. | | Patents | 13,463,387 | Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P". | | [EMEA](http://opus.nlpl.eu/download.php?f=EMEA/v3/moses/en-es.txt.zip) | 5,377,448 | Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency. | | [mespen_Medline](https://zenodo.org/record/3562536#.YTt1fH2xXbR) | 4,166,077 | Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources are aggregated from the MedlinePlus source. | | PubMed | 1,858,966 | Open-access articles from the PubMed repository crawled in 2017. | ## Evaluation and results The model has been evaluated on the Named Entity Recognition (NER) using the following datasets: - [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/). - [CANTEMIST](https://zenodo.org/record/3978041#.YTt5qH2xXbQ): is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ). - ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables. The evaluation results are compared against the [mBERT](https://huggingface.co/bert-base-multilingual-cased) and [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) models: | F1 - Precision - Recall | roberta-base-biomedical-clinical-es | mBERT | BETO | |---------------------------|----------------------------|-------------------------------|-------------------------| | PharmaCoNER | **90.04** - **88.92** - **91.18** | 87.46 - 86.50 - 88.46 | 88.18 - 87.12 - 89.28 | | CANTEMIST | **83.34** - **81.48** - **85.30** | 82.61 - 81.12 - 84.15 | 82.42 - 80.91 - 84.00 | | ICTUSnet | **88.08** - **84.92** - **91.50** | 86.75 - 83.53 - 90.23 | 85.95 - 83.10 - 89.02 | ## Intended uses & limitations The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section) However, the is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification. ## Cite If you use our models, please cite our latest preprint: ```bibtex @misc{carrino2021biomedical, title={Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario}, author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Asier Gutiérrez-Fandiño and Joan Llop-Palao and Marc Pàmies and Aitor Gonzalez-Agirre and Marta Villegas}, year={2021}, eprint={2109.03570}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` If you use our Medical Crawler corpus, please cite the preprint: ```bibtex @misc{carrino2021spanish, title={Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models}, author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Ona de Gibert Bonet and Asier Gutiérrez-Fandiño and Aitor Gonzalez-Agirre and Martin Krallinger and Marta Villegas}, year={2021}, eprint={2109.07765}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` --- --- ## How to use ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-biomedical-es") model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-biomedical-es") from transformers import pipeline unmasker = pipeline('fill-mask', model="BSC-TeMU/roberta-base-biomedical-es") unmasker("El único antecedente personal a reseñar era la <mask> arterial.") ``` ``` # Output [ { "sequence": " El único antecedente personal a reseñar era la hipertensión arterial.", "score": 0.9855039715766907, "token": 3529, "token_str": " hipertensión" }, { "sequence": " El único antecedente personal a reseñar era la diabetes arterial.", "score": 0.0039140828885138035, "token": 1945, "token_str": " diabetes" }, { "sequence": " El único antecedente personal a reseñar era la hipotensión arterial.", "score": 0.002484665485098958, "token": 11483, "token_str": " hipotensión" }, { "sequence": " El único antecedente personal a reseñar era la Hipertensión arterial.", "score": 0.0023484621196985245, "token": 12238, "token_str": " Hipertensión" }, { "sequence": " El único antecedente personal a reseñar era la presión arterial.", "score": 0.0008009297889657319, "token": 2267, "token_str": " presión" } ] ```

{"language": ["es"], "license": "apache-2.0", "tags": ["biomedical", "clinical", "spanish"], "metrics": ["ppl"], "widget": [{"text": "El \u00fanico antecedente personal a rese\u00f1ar era la <mask> arterial."}, {"text": "Las radiolog\u00edas \u00f3seas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."}, {"text": "En el <mask> toraco-abd\u00f3mino-p\u00e9lvico no se encontraron hallazgos patol\u00f3gicos de inter\u00e9s."}]}

BSC-LT/roberta-base-biomedical-clinical-es

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #biomedical #clinical #spanish #es #arxiv-2109.03570 #arxiv-2109.07765 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL Biomedical-clinical language model for Spanish ============================================== Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official repository and read our preprint "*Carrino, C. P., Armengol-Estapé, J., Gutiérrez-Fandiño, A., Llop-Palao, J., Pàmies, M., Gonzalez-Agirre, A., & Villegas, M. (2021). Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario.*". Tokenization and model pretraining ---------------------------------- This model is a RoBERTa-based model trained on a biomedical-clinical corpus in Spanish collected from several sources (see next section). The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences. Training corpora and preprocessing ---------------------------------- The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus collected from more than 278K clinical documents and notes. To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus uncleaned. Essentially, the cleaning operations used are: * data parsing in different formats + sentence splitting + language detection + filtering of ill-formed sentences + deduplication of repetitive contents + keep the original document boundaries Then, the biomedical corpora are concatenated and further global deduplication among the biomedical corpora have been applied. Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of more than 1B tokens. The table below shows some basic statistics of the individual cleaned corpora: Name: Medical crawler, No. tokens: 745,705,946, Description: Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. Name: Clinical cases misc., No. tokens: 102,855,267, Description: A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document. Name: Clinical notes/documents, No. tokens: 91,250,080, Description: Collection of more than 278K clinical documents, including discharge reports, clinical course notes and X-ray reports, for a total of 91M tokens. Name: Scielo, No. tokens: 60,007,289, Description: Publications written in Spanish crawled from the Spanish SciELO server in 2017. Name: BARR2\_background, No. tokens: 24,516,442, Description: Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines. Name: Wikipedia\_life\_sciences, No. tokens: 13,890,501, Description: Wikipedia articles crawled 04/01/2021 with the Wikipedia API python library starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content. Name: Patents, No. tokens: 13,463,387, Description: Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P". Name: EMEA, No. tokens: 5,377,448, Description: Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency. Name: mespen\_Medline, No. tokens: 4,166,077, Description: Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources are aggregated from the MedlinePlus source. Name: PubMed, No. tokens: 1,858,966, Description: Open-access articles from the PubMed repository crawled in 2017. Evaluation and results ---------------------- The model has been evaluated on the Named Entity Recognition (NER) using the following datasets: * PharmaCoNER: is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: URL * CANTEMIST: is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: URL * ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables. The evaluation results are compared against the mBERT and BETO models: Intended uses & limitations --------------------------- The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section) However, the is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification. Cite ---- If you use our models, please cite our latest preprint: If you use our Medical Crawler corpus, please cite the preprint: --- --- How to use ----------

fill-mask

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es # Biomedical language model for Spanish Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official [repository](https://github.com/PlanTL-SANIDAD/lm-biomedical-clinical-es) and read our [preprint](https://arxiv.org/abs/2109.03570) "_Carrino, C. P., Armengol-Estapé, J., Gutiérrez-Fandiño, A., Llop-Palao, J., Pàmies, M., Gonzalez-Agirre, A., & Villegas, M. (2021). Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario._". ## Tokenization and model pretraining This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a **biomedical** corpus in Spanish collected from several sources (see next section). The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2) used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences. ## Training corpora and preprocessing The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers. To obtain a high-quality training corpus, a cleaning pipeline with the following operations has been applied: - data parsing in different formats - sentence splitting - language detection - filtering of ill-formed sentences - deduplication of repetitive contents - keep the original document boundaries Finally, the corpora are concatenated and further global deduplication among the corpora have been applied. The result is a medium-size biomedical corpus for Spanish composed of about 963M tokens. The table below shows some basic statistics of the individual cleaned corpora: | Name | No. tokens | Description | |-----------------------------------------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Medical crawler](https://zenodo.org/record/4561970) | 745,705,946 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. | | Clinical cases misc. | 102,855,267 | A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document. | | [Scielo](https://github.com/PlanTL-SANIDAD/SciELO-Spain-Crawler) | 60,007,289 | Publications written in Spanish crawled from the Spanish SciELO server in 2017. | | [BARR2_background](https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2) | 24,516,442 | Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines. | | Wikipedia_life_sciences | 13,890,501 | Wikipedia articles crawled 04/01/2021 with the [Wikipedia API python library](https://pypi.org/project/Wikipedia-API/) starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content. | | Patents | 13,463,387 | Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P". | | [EMEA](http://opus.nlpl.eu/download.php?f=EMEA/v3/moses/en-es.txt.zip) | 5,377,448 | Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency. | | [mespen_Medline](https://zenodo.org/record/3562536#.YTt1fH2xXbR) | 4,166,077 | Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources are aggregated from the MedlinePlus source. | | PubMed | 1,858,966 | Open-access articles from the PubMed repository crawled in 2017. | ## Evaluation and results The model has been evaluated on the Named Entity Recognition (NER) using the following datasets: - [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/). - [CANTEMIST](https://zenodo.org/record/3978041#.YTt5qH2xXbQ): is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ). - ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables. The evaluation results are compared against the [mBERT](https://huggingface.co/bert-base-multilingual-cased) and [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) models: | F1 - Precision - Recall | roberta-base-biomedical-es | mBERT | BETO | |---------------------------|----------------------------|-------------------------------|-------------------------| | PharmaCoNER | **89.48** - **87.85** - **91.18** | 87.46 - 86.50 - 88.46 | 88.18 - 87.12 - 89.28 | | CANTEMIST | **83.87** - **81.70** - **86.17** | 82.61 - 81.12 - 84.15 | 82.42 - 80.91 - 84.00 | | ICTUSnet | **88.12** - **85.56** - **90.83** | 86.75 - 83.53 - 90.23 | 85.95 - 83.10 - 89.02 | ## Intended uses & limitations The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section) However, the is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification. ## Cite If you use our models, please cite our latest preprint: ```bibtex @misc{carrino2021biomedical, title={Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario}, author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Asier Gutiérrez-Fandiño and Joan Llop-Palao and Marc Pàmies and Aitor Gonzalez-Agirre and Marta Villegas}, year={2021}, eprint={2109.03570}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` If you use our Medical Crawler corpus, please cite the preprint: ```bibtex @misc{carrino2021spanish, title={Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models}, author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Ona de Gibert Bonet and Asier Gutiérrez-Fandiño and Aitor Gonzalez-Agirre and Martin Krallinger and Marta Villegas}, year={2021}, eprint={2109.07765}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` --- ## How to use ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-biomedical-es") model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-biomedical-es") from transformers import pipeline unmasker = pipeline('fill-mask', model="BSC-TeMU/roberta-base-biomedical-es") unmasker("El único antecedente personal a reseñar era la <mask> arterial.") ``` ``` # Output [ { "sequence": " El único antecedente personal a reseñar era la hipertensión arterial.", "score": 0.9855039715766907, "token": 3529, "token_str": " hipertensión" }, { "sequence": " El único antecedente personal a reseñar era la diabetes arterial.", "score": 0.0039140828885138035, "token": 1945, "token_str": " diabetes" }, { "sequence": " El único antecedente personal a reseñar era la hipotensión arterial.", "score": 0.002484665485098958, "token": 11483, "token_str": " hipotensión" }, { "sequence": " El único antecedente personal a reseñar era la Hipertensión arterial.", "score": 0.0023484621196985245, "token": 12238, "token_str": " Hipertensión" }, { "sequence": " El único antecedente personal a reseñar era la presión arterial.", "score": 0.0008009297889657319, "token": 2267, "token_str": " presión" } ] ```

{"language": ["es"], "license": "apache-2.0", "tags": ["biomedical", "spanish"], "metrics": ["ppl"], "widget": [{"text": "El \u00fanico antecedente personal a rese\u00f1ar era la <mask> arterial."}, {"text": "Las radiolog\u00edas \u00f3seas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."}, {"text": "En el <mask> toraco-abd\u00f3mino-p\u00e9lvico no se encontraron hallazgos patol\u00f3gicos de inter\u00e9s."}]}

BSC-LT/roberta-base-biomedical-es

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #biomedical #spanish #es #arxiv-2109.03570 #arxiv-2109.07765 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL Biomedical language model for Spanish ===================================== Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official repository and read our preprint "*Carrino, C. P., Armengol-Estapé, J., Gutiérrez-Fandiño, A., Llop-Palao, J., Pàmies, M., Gonzalez-Agirre, A., & Villegas, M. (2021). Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario.*". Tokenization and model pretraining ---------------------------------- This model is a RoBERTa-based model trained on a biomedical corpus in Spanish collected from several sources (see next section). The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences. Training corpora and preprocessing ---------------------------------- The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers. To obtain a high-quality training corpus, a cleaning pipeline with the following operations has been applied: * data parsing in different formats + sentence splitting + language detection + filtering of ill-formed sentences + deduplication of repetitive contents + keep the original document boundaries Finally, the corpora are concatenated and further global deduplication among the corpora have been applied. The result is a medium-size biomedical corpus for Spanish composed of about 963M tokens. The table below shows some basic statistics of the individual cleaned corpora: Name: Medical crawler, No. tokens: 745,705,946, Description: Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. Name: Clinical cases misc., No. tokens: 102,855,267, Description: A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document. Name: Scielo, No. tokens: 60,007,289, Description: Publications written in Spanish crawled from the Spanish SciELO server in 2017. Name: BARR2\_background, No. tokens: 24,516,442, Description: Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines. Name: Wikipedia\_life\_sciences, No. tokens: 13,890,501, Description: Wikipedia articles crawled 04/01/2021 with the Wikipedia API python library starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content. Name: Patents, No. tokens: 13,463,387, Description: Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P". Name: EMEA, No. tokens: 5,377,448, Description: Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency. Name: mespen\_Medline, No. tokens: 4,166,077, Description: Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources are aggregated from the MedlinePlus source. Name: PubMed, No. tokens: 1,858,966, Description: Open-access articles from the PubMed repository crawled in 2017. Evaluation and results ---------------------- The model has been evaluated on the Named Entity Recognition (NER) using the following datasets: * PharmaCoNER: is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: URL * CANTEMIST: is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: URL * ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables. The evaluation results are compared against the mBERT and BETO models: Intended uses & limitations --------------------------- The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section) However, the is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification. Cite ---- If you use our models, please cite our latest preprint: If you use our Medical Crawler corpus, please cite the preprint: --- How to use ----------

token-classification

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne ## Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1). **IMPORTANT ABOUT THIS MODEL:** We modified the dataset to make this model more robust to general Spanish input. In the Spanish language all the name entities are capitalized, as this dataset has been elaborated by experts, it is totally correct in terms of Spanish language. We randomly took some entities and we lower-cased some of them for the model to learn not only that the named entities are capitalized, but also the structure of a sentence that should contain a named entity. For instance: "My name is [placeholder]", this [placeholder] should be a named entity even though it is not written capitalized. The model trained on the original capitel dataset can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne-capitel-ner Examples: This model: - "Me llamo asier y vivo en barcelona todo el año." → "Me llamo {as:S-PER}{ier:S-PER} y vivo en {barcelona:S-LOC} todo el año." - "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el {par:B-LOC}{k:I-LOC} {gü:E-LOC}{ell:E-LOC} tras salir del {barcelona:B-ORG} {super:I-ORG}{com:I-ORG}{pu:I-ORG}{ting:I-ORG} {cen:E-ORG}{ter:E-ORG}." Model trained on original data: - "Me llamo asier y vivo en barcelona todo el año." → "Me llamo asier y vivo en barcelona todo el año." (nothing) - "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." (nothing) ## Evaluation and results F1 Score: 0.8867 For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "ner"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "inference": {"parameters": {"aggregation_strategy": "first"}}}

BSC-LT/roberta-base-bne-capitel-ner-plus

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #token-classification #national library of spain #spanish #bne #capitel #ner #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Original pre-trained model can be found here: URL ## Dataset The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 1). IMPORTANT ABOUT THIS MODEL: We modified the dataset to make this model more robust to general Spanish input. In the Spanish language all the name entities are capitalized, as this dataset has been elaborated by experts, it is totally correct in terms of Spanish language. We randomly took some entities and we lower-cased some of them for the model to learn not only that the named entities are capitalized, but also the structure of a sentence that should contain a named entity. For instance: "My name is [placeholder]", this [placeholder] should be a named entity even though it is not written capitalized. The model trained on the original capitel dataset can be found here: URL Examples: This model: - "Me llamo asier y vivo en barcelona todo el año." → "Me llamo {as:S-PER}{ier:S-PER} y vivo en {barcelona:S-LOC} todo el año." - "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el {par:B-LOC}{k:I-LOC} {gü:E-LOC}{ell:E-LOC} tras salir del {barcelona:B-ORG} {super:I-ORG}{com:I-ORG}{pu:I-ORG}{ting:I-ORG} {cen:E-ORG}{ter:E-ORG}." Model trained on original data: - "Me llamo asier y vivo en barcelona todo el año." → "Me llamo asier y vivo en barcelona todo el año." (nothing) - "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." (nothing) ## Evaluation and results F1 Score: 0.8867 For evaluation details visit our GitHub repository. ## Citing Check out our paper for all the details: URL

token-classification

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne ## Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1). ## Evaluation and results F1 Score: 0.8960 For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "ner"], "datasets": ["bne", "capitel"], "metrics": ["f1"]}

BSC-LT/roberta-base-bne-capitel-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #token-classification #national library of spain #spanish #bne #capitel #ner #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Original pre-trained model can be found here: URL ## Dataset The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 1). ## Evaluation and results F1 Score: 0.8960 For evaluation details visit our GitHub repository. ## Citing Check out our paper for all the details: URL

token-classification

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-pos # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne ## Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2). ## Evaluation and results F1 Score: 0.9846 (average of 5 runs). For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "pos"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "widget": [{"text": "Festival de San Sebasti\u00e1n: Johnny Depp recibir\u00e1 el premio Donostia en pleno rifirrafe judicial con Amber Heard"}, {"text": "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto."}, {"text": "Gracias a los datos de la BNE, se ha podido lograr este modelo del lenguaje."}, {"text": "El Tribunal Superior de Justicia se pronunci\u00f3 ayer: \"Hay base legal dentro del marco jur\u00eddico actual\"."}]}

BSC-LT/roberta-base-bne-capitel-pos

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #token-classification #national library of spain #spanish #bne #capitel #pos #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Original pre-trained model can be found here: URL ## Dataset The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 2). ## Evaluation and results F1 Score: 0.9846 (average of 5 runs). For evaluation details visit our GitHub repository. ## Citing Check out our paper for all the details: URL

question-answering

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac # Spanish RoBERTa-base trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne ## Dataset The dataset used is the [SQAC corpus](https://huggingface.co/datasets/BSC-TeMU/SQAC). ## Evaluation and results F1 Score: 0.7923 (average of 5 runs). For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "qa", "question answering"], "datasets": ["BSC-TeMU/SQAC"], "metrics": ["f1"]}

BSC-LT/roberta-base-bne-sqac

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #question-answering #national library of spain #spanish #bne #qa #question answering #es #dataset-BSC-TeMU/SQAC #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL # Spanish RoBERTa-base trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Original pre-trained model can be found here: URL ## Dataset The dataset used is the SQAC corpus. ## Evaluation and results F1 Score: 0.7923 (average of 5 runs). For evaluation details visit our GitHub repository. ## Citing Check out our paper for all the details: URL

fill-mask

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne # RoBERTa base trained with data from National Library of Spain (BNE) ## Model Description RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. ## Training corpora and preprocessing The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019. To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text. Some of the statistics of the corpus: | Corpora | Number of documents | Number of tokens | Size (GB) | |---------|---------------------|------------------|-----------| | BNE | 201,080,084 | 135,733,450,668 | 570GB | ## Tokenization and pre-training The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-base-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 48 hours with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM. ## Evaluation and results For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne"], "datasets": ["bne"], "metrics": ["ppl"], "widget": [{"text": "Este a\u00f1o las campanadas de La Sexta las presentar\u00e1 <mask>."}, {"text": "David Broncano es un presentador de La <mask>."}, {"text": "Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."}, {"text": "Hay base legal dentro del marco <mask> actual."}]}

BSC-LT/roberta-base-bne

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #national library of spain #spanish #bne #es #dataset-bne #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL RoBERTa base trained with data from National Library of Spain (BNE) =================================================================== Model Description ----------------- RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Training corpora and preprocessing ---------------------------------- The National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019. To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text. Some of the statistics of the corpus: Tokenization and pre-training ----------------------------- The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 50,262 tokens. The RoBERTa-base-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 48 hours with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM. Evaluation and results ---------------------- For evaluation details visit our GitHub repository. Citing ------ Check out our paper for all the details: URL

fill-mask

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca # BERTa: RoBERTa-based Catalan language model ## BibTeX citation If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` ## Model description BERTa is a transformer-based masked language model for the Catalan language. It is based on the [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) base model and has been trained on a medium-size corpus collected from publicly available corpora and crawlers. ## Training corpora and preprocessing The training corpus consists of several corpora gathered from web crawling and public corpora. The publicly available corpora are: 1. the Catalan part of the [DOGC](http://opus.nlpl.eu/DOGC-v2.php) corpus, a set of documents from the Official Gazette of the Catalan Government 2. the [Catalan Open Subtitles](http://opus.nlpl.eu/download.php?f=OpenSubtitles/v2018/mono/OpenSubtitles.raw.ca.gz), a collection of translated movie subtitles 3. the non-shuffled version of the Catalan part of the [OSCAR](https://traces1.inria.fr/oscar/) corpus \\\\cite{suarez2019asynchronous}, a collection of monolingual corpora, filtered from [Common Crawl](https://commoncrawl.org/about/) 4. The [CaWac](http://nlp.ffzg.hr/resources/corpora/cawac/) corpus, a web corpus of Catalan built from the .cat top-level-domain in late 2013 the non-deduplicated version 5. the [Catalan Wikipedia articles](https://ftp.acc.umu.se/mirror/wikimedia.org/dumps/cawiki/20200801/) downloaded on 18-08-2020. The crawled corpora are: 6. The Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains 7. the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government 8. the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the [Catalan News Agency](https://www.acn.cat/) To obtain a high-quality training corpus, each corpus have preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process, we keep document boundaries are kept. Finally, the corpora are concatenated and further global deduplication among the corpora is applied. The final training corpus consists of about 1,8B tokens. ## Tokenization and pretraining The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2) used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The BERTa pretraining consists of a masked language model training that follows the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM. ## Evaluation ## CLUB benchmark The BERTa model has been fine-tuned on the downstream tasks of the Catalan Language Understanding Evaluation benchmark (CLUB), that has been created along with the model. It contains the following tasks and their related datasets: 1. Part-of-Speech Tagging (POS) Catalan-Ancora: from the [Universal Dependencies treebank](https://github.com/UniversalDependencies/UD_Catalan-AnCora) of the well-known Ancora corpus 2. Named Entity Recognition (NER) **[AnCora Catalan 2.0.0](https://zenodo.org/record/4762031#.YKaFjqGxWUk)**: extracted named entities from the original [Ancora](https://doi.org/10.5281/zenodo.4762030) version, filtering out some unconventional ones, like book titles, and transcribed them into a standard CONLL-IOB format 3. Text Classification (TC) **[TeCla](https://doi.org/10.5281/zenodo.4627197)**: consisting of 137k news pieces from the Catalan News Agency ([ACN](https://www.acn.cat/)) corpus 4. Semantic Textual Similarity (STS) **[Catalan semantic textual similarity](https://doi.org/10.5281/zenodo.4529183)**: consisting of more than 3000 sentence pairs, annotated with the semantic similarity between them, scraped from the [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349) 5. Question Answering (QA): **[ViquiQuAD](https://doi.org/10.5281/zenodo.4562344)**: consisting of more than 15,000 questions outsourced from Catalan Wikipedia randomly chosen from a set of 596 articles that were originally written in Catalan. **[XQuAD](https://doi.org/10.5281/zenodo.4526223)**: the Catalan translation of XQuAD, a multilingual collection of manual translations of 1,190 question-answer pairs from English Wikipedia used only as a _test set_ Here are the train/dev/test splits of the datasets: | Task (Dataset) | Total | Train | Dev | Test | |:--|:--|:--|:--|:--| | NER (Ancora) |13,581 | 10,628 | 1,427 | 1,526 | | POS (Ancora)| 16,678 | 13,123 | 1,709 | 1,846 | | STS | 3,073 | 2,073 | 500 | 500 | | TC (TeCla) | 137,775 | 110,203 | 13,786 | 13,786| | QA (ViquiQuAD) | 14,239 | 11,255 | 1,492 | 1,429 | _The fine-tuning on downstream tasks have been performed with the HuggingFace [**Transformers**](https://github.com/huggingface/transformers) library_ ## Results Below the evaluation results on the CLUB tasks compared with the multilingual mBERT, XLM-RoBERTa models and the Catalan WikiBERT-ca model | Task | NER (F1) | POS (F1) | STS (Pearson) | TC (accuracy) | QA (ViquiQuAD) (F1/EM) | QA (XQuAD) (F1/EM) | | ------------|:-------------:| -----:|:------|:-------|:------|:----| | BERTa | **88.13** | **98.97** | **79.73** | **74.16** | **86.97/72.29** | **68.89/48.87** | | mBERT | 86.38 | 98.82 | 76.34 | 70.56 | 86.97/72.22 | 67.15/46.51 | | XLM-RoBERTa | 87.66 | 98.89 | 75.40 | 71.68 | 85.50/70.47 | 67.10/46.42 | | WikiBERT-ca | 77.66 | 97.60 | 77.18 | 73.22 | 85.45/70.75 | 65.21/36.60 | ## Intended uses & limitations The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section) However, the is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification or Named Entity Recognition. --- ## Using BERTa ## Load model and tokenizer ``` python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-ca-cased") model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-ca-cased") ``` ## Fill Mask task Below, an example of how to use the masked language modelling task with a pipeline. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='BSC-TeMU/roberta-base-ca-cased') >>> unmasker("Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada " "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, " "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa," "i pel nord-oest per la serralada de Collserola " "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela " "la línia de costa encaixant la ciutat en un perímetre molt definit.") [ { "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada " "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, " "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa," "i pel nord-oest per la serralada de Collserola " "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela " "la línia de costa encaixant la ciutat en un perímetre molt definit.", "score": 0.4177263379096985, "token": 734, "token_str": " Barcelona" }, { "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada " "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, " "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa," "i pel nord-oest per la serralada de Collserola " "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela " "la línia de costa encaixant la ciutat en un perímetre molt definit.", "score": 0.10696165263652802, "token": 3849, "token_str": " Badalona" }, { "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada " "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, " "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa," "i pel nord-oest per la serralada de Collserola " "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela " "la línia de costa encaixant la ciutat en un perímetre molt definit.", "score": 0.08135009557008743, "token": 19349, "token_str": " Collserola" }, { "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada " "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, " "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa," "i pel nord-oest per la serralada de Collserola " "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela " "la línia de costa encaixant la ciutat en un perímetre molt definit.", "score": 0.07330769300460815, "token": 4974, "token_str": " Terrassa" }, { "sequence": " Situada a la costa de la mar Mediterrània, <mask> s'assenta en una plana formada " "entre els deltes de les desembocadures dels rius Llobregat, al sud-oest, " "i Besòs, al nord-est, i limitada pel sud-est per la línia de costa," "i pel nord-oest per la serralada de Collserola " "(amb el cim del Tibidabo, 516,2 m, com a punt més alt) que segueix paral·lela " "la línia de costa encaixant la ciutat en un perímetre molt definit.", "score": 0.03317456692457199, "token": 14333, "token_str": " Gavà" } ] ``` This model was originally published as [bsc/roberta-base-ca-cased](https://huggingface.co/bsc/roberta-base-ca-cased).

{"language": "ca", "license": "apache-2.0", "tags": ["masked-lm", "BERTa", "catalan"], "widget": [{"text": "El Catal\u00e0 \u00e9s una llengua molt <mask>."}, {"text": "Salvador Dal\u00ed va viure a <mask>."}, {"text": "La Costa Brava t\u00e9 les millors <mask> d'Espanya."}, {"text": "El cacaolat \u00e9s un batut de <mask>."}, {"text": "<mask> \u00e9s la capital de la Garrotxa."}, {"text": "Vaig al <mask> a buscar bolets."}, {"text": "Antoni Gaud\u00ed vas ser un <mask> molt important per la ciutat."}, {"text": "Catalunya \u00e9s una refer\u00e8ncia en <mask> a nivell europeu."}]}

BSC-LT/roberta-base-ca

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #masked-lm #BERTa #catalan #ca #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL BERTa: RoBERTa-based Catalan language model =========================================== BibTeX citation --------------- If you use any of these resources (datasets or models) in your work, please cite our latest paper: Model description ----------------- BERTa is a transformer-based masked language model for the Catalan language. It is based on the RoBERTA base model and has been trained on a medium-size corpus collected from publicly available corpora and crawlers. Training corpora and preprocessing ---------------------------------- The training corpus consists of several corpora gathered from web crawling and public corpora. The publicly available corpora are: 1. the Catalan part of the DOGC corpus, a set of documents from the Official Gazette of the Catalan Government 2. the Catalan Open Subtitles, a collection of translated movie subtitles 3. the non-shuffled version of the Catalan part of the OSCAR corpus \\cite{suarez2019asynchronous}, a collection of monolingual corpora, filtered from Common Crawl 4. The CaWac corpus, a web corpus of Catalan built from the .cat top-level-domain in late 2013 the non-deduplicated version 5. the Catalan Wikipedia articles downloaded on 18-08-2020. The crawled corpora are: 6. The Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains 7. the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government 8. the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the Catalan News Agency To obtain a high-quality training corpus, each corpus have preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process, we keep document boundaries are kept. Finally, the corpora are concatenated and further global deduplication among the corpora is applied. The final training corpus consists of about 1,8B tokens. Tokenization and pretraining ---------------------------- The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 52,000 tokens. The BERTa pretraining consists of a masked language model training that follows the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM. Evaluation ---------- CLUB benchmark -------------- The BERTa model has been fine-tuned on the downstream tasks of the Catalan Language Understanding Evaluation benchmark (CLUB), that has been created along with the model. It contains the following tasks and their related datasets: 1. Part-of-Speech Tagging (POS) Catalan-Ancora: from the Universal Dependencies treebank of the well-known Ancora corpus 2. Named Entity Recognition (NER) AnCora Catalan 2.0.0: extracted named entities from the original Ancora version, filtering out some unconventional ones, like book titles, and transcribed them into a standard CONLL-IOB format 3. Text Classification (TC) TeCla: consisting of 137k news pieces from the Catalan News Agency (ACN) corpus 4. Semantic Textual Similarity (STS) Catalan semantic textual similarity: consisting of more than 3000 sentence pairs, annotated with the semantic similarity between them, scraped from the Catalan Textual Corpus 5. Question Answering (QA): ViquiQuAD: consisting of more than 15,000 questions outsourced from Catalan Wikipedia randomly chosen from a set of 596 articles that were originally written in Catalan. XQuAD: the Catalan translation of XQuAD, a multilingual collection of manual translations of 1,190 question-answer pairs from English Wikipedia used only as a *test set* Here are the train/dev/test splits of the datasets: *The fine-tuning on downstream tasks have been performed with the HuggingFace Transformers library* Results ------- Below the evaluation results on the CLUB tasks compared with the multilingual mBERT, XLM-RoBERTa models and the Catalan WikiBERT-ca model Intended uses & limitations --------------------------- The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section) However, the is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification or Named Entity Recognition. --- Using BERTa ----------- Load model and tokenizer ------------------------ Fill Mask task -------------- Below, an example of how to use the masked language modelling task with a pipeline. This model was originally published as bsc/roberta-base-ca-cased.

token-classification

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-capitel-ner # Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne ## Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1). ## Evaluation and results F1 Score: 0.8998 For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "ner"], "datasets": ["bne", "capitel"], "metrics": ["f1"]}

BSC-LT/roberta-large-bne-capitel-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #token-classification #national library of spain #spanish #bne #capitel #ner #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL # Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Original pre-trained model can be found here: URL ## Dataset The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 1). ## Evaluation and results F1 Score: 0.8998 For evaluation details visit our GitHub repository. ## Citing Check out our paper for all the details: URL

token-classification

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-capitel-pos # Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne ## Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2). ## Evaluation and results F1 Score: 0.9851 (average of 5 runs). For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "pos"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "widget": [{"text": "Festival de San Sebasti\u00e1n: Johnny Depp recibir\u00e1 el premio Donostia en pleno rifirrafe judicial con Amber Heard"}, {"text": "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto."}, {"text": "Gracias a los datos de la BNE, se ha podido lograr este modelo del lenguaje."}, {"text": "El Tribunal Superior de Justicia se pronunci\u00f3 ayer: \"Hay base legal dentro del marco jur\u00eddico actual\"."}]}

BSC-LT/roberta-large-bne-capitel-pos

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #token-classification #national library of spain #spanish #bne #capitel #pos #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL # Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Original pre-trained model can be found here: URL ## Dataset The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 2). ## Evaluation and results F1 Score: 0.9851 (average of 5 runs). For evaluation details visit our GitHub repository. ## Citing Check out our paper for all the details: URL

question-answering

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-sqac # Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset. RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne ## Dataset The dataset used is the [SQAC corpus](https://huggingface.co/datasets/BSC-TeMU/SQAC). ## Evaluation and results F1 Score: 0.7993 (average of 5 runs). For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "qa", "question answering"], "datasets": ["BSC-TeMU/SQAC"], "metrics": ["f1"]}

BSC-LT/roberta-large-bne-sqac

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #question-answering #national library of spain #spanish #bne #qa #question answering #es #dataset-BSC-TeMU/SQAC #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL # Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset. RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Original pre-trained model can be found here: URL ## Dataset The dataset used is the SQAC corpus. ## Evaluation and results F1 Score: 0.7993 (average of 5 runs). For evaluation details visit our GitHub repository. ## Citing Check out our paper for all the details: URL

fill-mask

transformers

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne # RoBERTa large trained with data from National Library of Spain (BNE) ## Model Description RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. ## Training corpora and preprocessing The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019. To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text. Some of the statistics of the corpus: | Corpora | Number of documents | Number of tokens | Size (GB) | |---------|---------------------|------------------|-----------| | BNE | 201,080,084 | 135,733,450,668 | 570GB | ## Tokenization and pre-training The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-large-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM. ## Evaluation and results For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne"], "datasets": ["bne"], "metrics": ["ppl"], "widget": [{"text": "Este a\u00f1o las campanadas de La Sexta las <mask> Pedroche y Chicote."}, {"text": "El artista Antonio Orozco es un colaborador de La <mask>."}, {"text": "Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."}, {"text": "Hay base legal dentro del marco <mask> actual."}]}

BSC-LT/roberta-large-bne

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #fill-mask #national library of spain #spanish #bne #es #dataset-bne #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL RoBERTa large trained with data from National Library of Spain (BNE) ==================================================================== Model Description ----------------- RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019. Training corpora and preprocessing ---------------------------------- The National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019. To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text. Some of the statistics of the corpus: Tokenization and pre-training ----------------------------- The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 50,262 tokens. The RoBERTa-large-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM. Evaluation and results ---------------------- For evaluation details visit our GitHub repository. Citing ------ Check out our paper for all the details: URL

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4877 - Wer: 0.4895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6615 | 4.0 | 500 | 1.7423 | 1.0723 | | 0.8519 | 8.0 | 1000 | 0.4877 | 0.4895 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}

BSen/wav2vec2-base-timit-demo-colab

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us

wav2vec2-base-timit-demo-colab ============================== This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4877 * Wer: 0.4895 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 10 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.0+cu111 * Datasets 1.13.3 * Tokenizers 0.10.3

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]}

BSen/wav2vec2-large-xls-r-300m-turkish-colab

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us

# wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

text-generation

transformers

# Rick DialoGPT Model

{"tags": ["conversational"]}

BW/TEST

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Rick DialoGPT Model

text2text-generation

transformers

[](https://paperswithcode.com/sota/relation-extraction-on-nyt?p=rebel-relation-extraction-by-end-to-end) [](https://paperswithcode.com/sota/relation-extraction-on-conll04?p=rebel-relation-extraction-by-end-to-end) [](https://paperswithcode.com/sota/joint-entity-and-relation-extraction-on-3?p=rebel-relation-extraction-by-end-to-end) [](https://paperswithcode.com/sota/relation-extraction-on-ade-corpus?p=rebel-relation-extraction-by-end-to-end) [](https://paperswithcode.com/sota/relation-extraction-on-re-tacred?p=rebel-relation-extraction-by-end-to-end) ## Multilingual update! Check [mREBEL](https://huggingface.co/Babelscape/mrebel-large), a multilingual version covering more relation types, languages and including entity types. # REBEL <img src="https://i.ibb.co/qsLzNqS/hf-rebel.png" width="30" alt="hf-rebel" border="0" style="display:inline; white-space:nowrap;">: Relation Extraction By End-to-end Language generation This is the model card for the Findings of EMNLP 2021 paper [REBEL: Relation Extraction By End-to-end Language generation](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). We present a new linearization approach and a reframing of Relation Extraction as a seq2seq task. The paper can be found [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). If you use the code, please reference this work in your paper: @inproceedings{huguet-cabot-navigli-2021-rebel-relation, title = "{REBEL}: Relation Extraction By End-to-end Language generation", author = "Huguet Cabot, Pere-Llu{\'\i}s and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.204", pages = "2370--2381", abstract = "Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model{'}s flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.", } The original repository for the paper can be found [here](https://github.com/Babelscape/rebel) Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the [Spaces demo](https://huggingface.co/spaces/Babelscape/rebel-demo). ## Pipeline usage ```python from transformers import pipeline triplet_extractor = pipeline('text2text-generation', model='Babelscape/rebel-large', tokenizer='Babelscape/rebel-large') # We need to use the tokenizer manually since we need special tokens. extracted_text = triplet_extractor.tokenizer.batch_decode([triplet_extractor("Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic", return_tensors=True, return_text=False)[0]["generated_token_ids"]]) print(extracted_text[0]) # Function to parse the generated text and extract the triplets def extract_triplets(text): triplets = [] relation, subject, relation, object_ = '', '', '', '' text = text.strip() current = 'x' for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split(): if token == "<triplet>": current = 't' if relation != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) relation = '' subject = '' elif token == "<subj>": current = 's' if relation != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) object_ = '' elif token == "<obj>": current = 'o' relation = '' else: if current == 't': subject += ' ' + token elif current == 's': object_ += ' ' + token elif current == 'o': relation += ' ' + token if subject != '' and relation != '' and object_ != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) return triplets extracted_triplets = extract_triplets(extracted_text[0]) print(extracted_triplets) ``` ## Model and Tokenizer using transformers ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer def extract_triplets(text): triplets = [] relation, subject, relation, object_ = '', '', '', '' text = text.strip() current = 'x' for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split(): if token == "<triplet>": current = 't' if relation != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) relation = '' subject = '' elif token == "<subj>": current = 's' if relation != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) object_ = '' elif token == "<obj>": current = 'o' relation = '' else: if current == 't': subject += ' ' + token elif current == 's': object_ += ' ' + token elif current == 'o': relation += ' ' + token if subject != '' and relation != '' and object_ != '': triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) return triplets # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("Babelscape/rebel-large") model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/rebel-large") gen_kwargs = { "max_length": 256, "length_penalty": 0, "num_beams": 3, "num_return_sequences": 3, } # Text to extract triplets from text = 'Punta Cana is a resort town in the municipality of Higüey, in La Altagracia Province, the easternmost province of the Dominican Republic.' # Tokenizer text model_inputs = tokenizer(text, max_length=256, padding=True, truncation=True, return_tensors = 'pt') # Generate generated_tokens = model.generate( model_inputs["input_ids"].to(model.device), attention_mask=model_inputs["attention_mask"].to(model.device), **gen_kwargs, ) # Extract text decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False) # Extract triplets for idx, sentence in enumerate(decoded_preds): print(f'Prediction triplets sentence {idx}') print(extract_triplets(sentence)) ```

{"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["seq2seq", "relation-extraction"], "datasets": ["Babelscape/rebel-dataset"], "widget": [{"text": "Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic"}], "model-index": [{"name": "REBEL", "results": [{"task": {"type": "Relation-Extraction", "name": "Relation Extraction"}, "dataset": {"name": "CoNLL04", "type": "CoNLL04"}, "metrics": [{"type": "re+ macro f1", "value": 76.65, "name": "RE+ Macro F1"}]}, {"task": {"type": "Relation-Extraction", "name": "Relation Extraction"}, "dataset": {"name": "NYT", "type": "NYT"}, "metrics": [{"type": "f1", "value": 93.4, "name": "F1"}]}]}]}

Babelscape/rebel-large

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #bart #text2text-generation #seq2seq #relation-extraction #en #dataset-Babelscape/rebel-dataset #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us

![PWC](URL ![PWC](URL ![PWC](URL ![PWC](URL ![PWC](URL ## Multilingual update! Check mREBEL, a multilingual version covering more relation types, languages and including entity types. # REBEL <img src="https://i.URL width="30" alt="hf-rebel" border="0" style="display:inline; white-space:nowrap;">: Relation Extraction By End-to-end Language generation This is the model card for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By End-to-end Language generation. We present a new linearization approach and a reframing of Relation Extraction as a seq2seq task. The paper can be found here. If you use the code, please reference this work in your paper: @inproceedings{huguet-cabot-navigli-2021-rebel-relation, title = "{REBEL}: Relation Extraction By End-to-end Language generation", author = "Huguet Cabot, Pere-Llu{\'\i}s and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "URL pages = "2370--2381", abstract = "Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model{'}s flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.", } The original repository for the paper can be found here Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the Spaces demo. ## Pipeline usage ## Model and Tokenizer using transformers

token-classification

transformers

# WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER This is the model card for the EMNLP 2021 paper [WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER](https://aclanthology.org/2021.findings-emnlp.215/). We fine-tuned a multilingual language model (mBERT) for 3 epochs on our [WikiNEuRal dataset](https://huggingface.co/datasets/Babelscape/wikineural) for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. **If you use the model, please reference this work in your paper**: ```bibtex @inproceedings{tedeschi-etal-2021-wikineural-combined, title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", author = "Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\`o} and Cecconi, Francesco and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.215", pages = "2521--2533", abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", } ``` The original repository for the paper can be found at [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural). ## How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Babelscape/wikineural-multilingual-ner") model = AutoModelForTokenClassification.from_pretrained("Babelscape/wikineural-multilingual-ner") nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True) example = "My name is Wolfgang and I live in Berlin" ner_results = nlp(example) print(ner_results) ``` ## Limitations and bias This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL). ## Licensing Information Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents and models belongs to the original copyright holders.

{"language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru", "multilingual"], "license": ["cc-by-nc-sa-4.0"], "tags": ["named-entity-recognition", "sequence-tagger-model"], "datasets": ["Babelscape/wikineural"], "annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "widget": [{"text": "My name is Wolfgang and I live in Berlin."}, {"text": "George Washington went to Washington."}, {"text": "Mi nombre es Sarah y vivo en Londres."}, {"text": "\u041c\u0435\u043d\u044f \u0437\u043e\u0432\u0443\u0442 \u0421\u0438\u043c\u043e\u043d\u0430, \u0438 \u044f \u0436\u0438\u0432\u0443 \u0432 \u0420\u0438\u043c\u0435."}], "pretty_name": "wikineural-dataset", "source_datasets": ["original"], "task_categories": ["structure-prediction"], "task_ids": ["named-entity-recognition"]}

Babelscape/wikineural-multilingual-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #safetensors #bert #token-classification #named-entity-recognition #sequence-tagger-model #de #en #es #fr #it #nl #pl #pt #ru #multilingual #dataset-Babelscape/wikineural #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER This is the model card for the EMNLP 2021 paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. We fine-tuned a multilingual language model (mBERT) for 3 epochs on our WikiNEuRal dataset for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. If you use the model, please reference this work in your paper: The original repository for the paper can be found at URL ## How to use You can use this model with Transformers *pipeline* for NER. ## Limitations and bias This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL). ## Licensing Information Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents and models belongs to the original copyright holders.

text-generation

transformers

#Anika Bot

{"tags": ["conversational"]}

Backedman/DialoGPT-small-Anika

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#Anika Bot

automatic-speech-recognition

transformers

Dataset used for training: - Name: Common Voice - Language: Indonesian [id] - Version: 6.1 Test WER: 19.3 % Contact: [email protected]

{"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "bahasa-indonesia"], "datasets": ["common_voice_id_6.1"]}

Bagus/wav2vec2-large-xlsr-bahasa-indonesia

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #bahasa-indonesia #el #dataset-common_voice_id_6.1 #license-apache-2.0 #endpoints_compatible #has_space #region-us

Dataset used for training: - Name: Common Voice - Language: Indonesian [id] - Version: 6.1 Test WER: 19.3 % Contact: bagus@URL

audio-classification

transformers

~~~ # requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !git clone https://github.com/m3hrdadfi/soxan cd soxan ~~~ # prediction ~~~ import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor import librosa import IPython.display as ipd import numpy as np import pandas as pd ~~~ ~~~ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_name_or_path = "Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition" config = AutoConfig.from_pretrained(model_name_or_path) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path) sampling_rate = feature_extractor.sampling_rate model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device) ~~~ ~~~ def speech_file_to_array_fn(path, sampling_rate): speech_array, _sampling_rate = torchaudio.load(path) resampler = torchaudio.transforms.Resample(_sampling_rate) speech = resampler(speech_array).squeeze().numpy() return speech def predict(path, sampling_rate): speech = speech_file_to_array_fn(path, sampling_rate) inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) inputs = {key: inputs[key].to(device) for key in inputs} with torch.no_grad(): logits = model(**inputs).logits scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0] outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)] return outputs ~~~ # prediction ~~~ # path for a sample path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav' outputs = predict(path, sampling_rate) ~~~ ~~~ [{'Emotion': 'anger', 'Score': '98.3%'}, {'Emotion': 'disgust', 'Score': '0.0%'}, {'Emotion': 'fear', 'Score': '0.4%'}, {'Emotion': 'happiness', 'Score': '0.7%'}, {'Emotion': 'sadness', 'Score': '0.5%'}] ~~~

{"language": "el", "license": "apache-2.0", "tags": ["audio", "audio-classification", "speech"], "datasets": ["aesdd"]}

Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #audio #audio-classification #speech #el #dataset-aesdd #license-apache-2.0 #endpoints_compatible #region-us

~~~ # requirement packages !pip install git+URL !pip install git+URL !pip install torchaudio !pip install librosa !git clone URL cd soxan ~~~ # prediction ~~~ import torch import URL as nn import URL.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor import librosa import IPython.display as ipd import numpy as np import pandas as pd ~~~ ~~~ device = URL("cuda" if URL.is_available() else "cpu") model_name_or_path = "Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition" config = AutoConfig.from_pretrained(model_name_or_path) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path) sampling_rate = feature_extractor.sampling_rate model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device) ~~~ ~~~ def speech_file_to_array_fn(path, sampling_rate): speech_array, _sampling_rate = URL(path) resampler = torchaudio.transforms.Resample(_sampling_rate) speech = resampler(speech_array).squeeze().numpy() return speech def predict(path, sampling_rate): speech = speech_file_to_array_fn(path, sampling_rate) inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) inputs = {key: inputs[key].to(device) for key in inputs} with torch.no_grad(): logits = model(inputs).logits scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0] outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)] return outputs ~~~ # prediction ~~~ # path for a sample path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav' outputs = predict(path, sampling_rate) ~~~ ~~~ [{'Emotion': 'anger', 'Score': '98.3%'}, {'Emotion': 'disgust', 'Score': '0.0%'}, {'Emotion': 'fear', 'Score': '0.4%'}, {'Emotion': 'happiness', 'Score': '0.7%'}, {'Emotion': 'sadness', 'Score': '0.5%'}] ~~~

audio-classification

transformers

This is for (private) DEMO only.

{"language": "ja", "tags": ["audio", "audio-classification", "speech", "speech-emotion-recognition"], "datasets": ["jtes"]}

Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #wav2vec2 #audio-classification #audio #speech #speech-emotion-recognition #ja #dataset-jtes #endpoints_compatible #has_space #region-us

This is for (private) DEMO only.

text-generation

transformers

Harry Potter DialoGPT Model

{"tags": ["conversational"]}

BalajiSathesh/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Harry Potter DialoGPT Model

text2text-generation

transformers

**Dataset** ToTTo is an open-domain English Table-to-Text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table, a set of highlighted table cells, page title and section title as inputs, it produces a one-sentence description summarising the key details from the inputs. This dataset can be taken from hugging face (https://huggingface.co/datasets/totto). **Model** The pre-trained Text-to-Text "t5-base" model is fine-tuned with the Table-to-Text ToTTo dataset(downstream task) for the complete train dataset split of around 120,761 examples. During the fine-tuning process for this downstream task, BertScore metric was used as an evaluation metric instead of the standard BLEU metric.

{}

Barkavi/t5base_totto

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Dataset ToTTo is an open-domain English Table-to-Text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table, a set of highlighted table cells, page title and section title as inputs, it produces a one-sentence description summarising the key details from the inputs. This dataset can be taken from hugging face (URL Model The pre-trained Text-to-Text "t5-base" model is fine-tuned with the Table-to-Text ToTTo dataset(downstream task) for the complete train dataset split of around 120,761 examples. During the fine-tuning process for this downstream task, BertScore metric was used as an evaluation metric instead of the standard BLEU metric.

fill-mask

transformers

# Hello hugging face

{"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]}

Barytes/hellohf

2022-03-02T23:29:04+00:00

TAGS #transformers #tf #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Hello hugging face

text-generation

transformers

# Bella Swan DialoGPT model

{"tags": ["conversational"]}

Batsy24/DialoGPT-medium-Twilight_BellaBot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Bella Swan DialoGPT model

text-generation

transformers

# Twilight Edward DialoGPT Model

{"tags": ["conversational"]}

Batsy24/DialoGPT-small-Twilight_EdBot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Twilight Edward DialoGPT Model

question-answering

transformers

# DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results | | Exact Match | F1 | |------------------|-------------|------| | DistilBERT paper | 79.1 | 86.9 | | Ours | 78.4 | 86.5 | The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

{"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"}

BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

DistilBERT with a second step of distillation ============================================= Model description ----------------- This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: 'distilbert-base-uncased' * Teacher: 'lewtun/bert-base-uncased-finetuned-squad-v1' Training data ------------- This model was trained on the SQuAD v1.1 dataset which can be obtained from the 'datasets' library as follows: Training procedure ------------------ Eval results ------------ Exact Match: DistilBERT paper, F1: 79.1 Exact Match: Ours, F1: 78.4 The scores were calculated using the 'squad' metric from 'datasets'. ### BibTeX entry and citation info

fill-mask

transformers

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # dummy-model This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dummy-model", "results": []}]}

BatuhanYilmaz/dummy-model

2022-03-02T23:29:04+00:00

TAGS #transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us

# dummy-model This model is a fine-tuned version of camembert-base on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [./checkpoint-10500](https://huggingface.co/./checkpoint-10500) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.7540 - Wer: 0.4647 - Cer: 0.1318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.999,0.9999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 120.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:------:|:-----:|:------:|:---------------:|:------:| | 1.0779 | 4.59 | 500 | 0.2354 | 0.8260 | 0.7395 | | 0.7573 | 9.17 | 1000 | 0.2100 | 0.7544 | 0.6960 | | 0.8225 | 13.76 | 1500 | 0.2021 | 0.6867 | 0.6672 | | 0.621 | 18.35 | 2000 | 0.1874 | 0.6824 | 0.6209 | | 0.6362 | 22.94 | 2500 | 0.1904 | 0.6712 | 0.6286 | | 0.624 | 27.52 | 3000 | 0.1820 | 0.6940 | 0.6116 | | 0.4781 | 32.11 | 3500 | 0.1735 | 0.6966 | 0.5989 | | 0.5685 | 36.7 | 4000 | 0.1769 | 0.6742 | 0.5971 | | 0.4384 | 41.28 | 4500 | 0.1767 | 0.6904 | 0.5999 | | 0.5509 | 45.87 | 5000 | 0.1692 | 0.6734 | 0.5641 | | 0.3665 | 50.46 | 5500 | 0.1680 | 0.7018 | 0.5662 | | 0.3914 | 55.05 | 6000 | 0.1631 | 0.7121 | 0.5552 | | 0.2467 | 59.63 | 6500 | 0.1563 | 0.6657 | 0.5374 | | 0.2576 | 64.22 | 7000 | 0.1554 | 0.6920 | 0.5316 | | 0.2711 | 68.81 | 7500 | 0.1495 | 0.6900 | 0.5176 | | 0.2626 | 73.39 | 8000 | 0.1454 | 0.6843 | 0.5043 | | 0.1377 | 77.98 | 8500 | 0.1470 | 0.7383 | 0.5101 | | 0.2005 | 82.57 | 9000 | 0.1430 | 0.7228 | 0.5045 | | 0.1355 | 87.16 | 9500 | 0.1375 | 0.7231 | 0.4869 | | 0.0431 | 91.74 | 10000 | 0.1350 | 0.7397 | 0.4749 | | 0.0586 | 96.33 | 10500 | 0.1339 | 0.7360 | 0.4754 | | 0.0896 | 100.92 | 11000 | 0.7187 | 0.4885 | 0.1398 | | 0.183 | 105.5 | 11500 | 0.7310 | 0.4838 | 0.1392 | | 0.0963 | 110.09 | 12000 | 0.7643 | 0.4759 | 0.1362 | | 0.0437 | 114.68 | 12500 | 0.7525 | 0.4641 | 0.1328 | | 0.1122 | 119.27 | 13000 | 0.7535 | 0.4651 | 0.1317 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["tr"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}

Baybars/wav2vec2-xls-r-1b-turkish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us

This model is a fine-tuned version of ./checkpoint-10500 on the COMMON\_VOICE - TR dataset. It achieves the following results on the evaluation set: * Loss: 0.7540 * Wer: 0.4647 * Cer: 0.1318 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0002 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.999,0.9999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 120.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.4164 - Wer: 0.3098 - Cer: 0.0764 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Language Model N-gram language model is trained by [mpoyraz](https://huggingface.co/mpoyraz/wav2vec2-xls-r-300m-cv7-turkish) on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 0.6356 | 9.09 | 500 | 0.5055 | 0.5536 | 0.1381 | | 0.3847 | 18.18 | 1000 | 0.4002 | 0.4247 | 0.1065 | | 0.3377 | 27.27 | 1500 | 0.4193 | 0.4167 | 0.1078 | | 0.2175 | 36.36 | 2000 | 0.4351 | 0.3861 | 0.0974 | | 0.2074 | 45.45 | 2500 | 0.3962 | 0.3622 | 0.0916 | | 0.159 | 54.55 | 3000 | 0.4062 | 0.3526 | 0.0888 | | 0.1882 | 63.64 | 3500 | 0.3991 | 0.3445 | 0.0850 | | 0.1766 | 72.73 | 4000 | 0.4214 | 0.3396 | 0.0847 | | 0.116 | 81.82 | 4500 | 0.4182 | 0.3265 | 0.0812 | | 0.0718 | 90.91 | 5000 | 0.4259 | 0.3191 | 0.0781 | | 0.019 | 100.0 | 5500 | 0.4164 | 0.3098 | 0.0764 | ## Evaluation Commands Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing. 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset mozilla-foundation/common_voice_8_0 --config tr --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "tr"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}

Baybars/wav2vec2-xls-r-300m-cv8-turkish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - TR dataset. It achieves the following results on the evaluation set: * Loss: 0.4164 * Wer: 0.3098 * Cer: 0.0764 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Language Model -------------- N-gram language model is trained by mpoyraz on a Turkish Wikipedia articles using KenLM and ngram-lm-wiki repo was used to generate arpa LM and convert it into binary format. Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 64 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results Evaluation Commands ------------------- Please install unicode\_tr package before running evaluation. It is used for Turkish text processing. 1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test' 2. To evaluate on 'speech-recognition-community-v2/dev\_data' ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

text2text-generation

transformers

# Query Generation This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery). The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the relevant passage. The model can be used for query generation to learn semantic search models without requiring annotated training data: [Synthetic Query Generation](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/query_generation). ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained('model-name') model = T5ForConditionalGeneration.from_pretrained('model-name') para = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(para, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=3) print("Paragraph:") print(para) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ```

{}

BeIR/query-gen-msmarco-t5-base-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Query Generation This model is the t5-base model from docTTTTTquery. The T5-base model was trained on the MS MARCO Passage Dataset, which consists of about 500k real search queries from Bing together with the relevant passage. The model can be used for query generation to learn semantic search models without requiring annotated training data: Synthetic Query Generation. ## Usage

text2text-generation

transformers

# Query Generation This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery). The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the relevant passage. The model can be used for query generation to learn semantic search models without requiring annotated training data: [Synthetic Query Generation](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/query_generation). ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained('model-name') model = T5ForConditionalGeneration.from_pretrained('model-name') para = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(para, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=3) print("Paragraph:") print(para) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ```

{}

BeIR/query-gen-msmarco-t5-large-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Query Generation This model is the t5-base model from docTTTTTquery. The T5-base model was trained on the MS MARCO Passage Dataset, which consists of about 500k real search queries from Bing together with the relevant passage. The model can be used for query generation to learn semantic search models without requiring annotated training data: Synthetic Query Generation. ## Usage

feature-extraction

transformers

# SPARTA Re-Implementation of [SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval](https://arxiv.org/abs/2009.13013). It is the re-implementation we used for [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://arxiv.org/abs/2104.08663). Also have a look at our BEIR repository: https://github.com/UKPLab/beir Have a look at https://github.com/nreimers/beir-sparta for the training and inference code of this SPARTA model

{}

BeIR/sparta-msmarco-distilbert-base-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #feature-extraction #arxiv-2009.13013 #arxiv-2104.08663 #endpoints_compatible #region-us

# SPARTA Re-Implementation of SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval. It is the re-implementation we used for BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models. Also have a look at our BEIR repository: URL Have a look at URL for the training and inference code of this SPARTA model

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5774 - Matthews Correlation: 0.5332 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.2347 | 1.0 | 535 | 0.5774 | 0.5332 | ### Framework versions - Transformers 4.11.0 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.533214904586951, "name": "Matthews Correlation"}]}]}]}

BearThreat/distilbert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-cola ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.5774 * Matthews Correlation: 0.5332 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.11.0 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3

text-generation

transformers

# Cartman Southpark DialoGPT2 small 18 epochs

{"tags": ["conversational"]}

Bee-Garbs/DialoGPT-real-cartman-small

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Cartman Southpark DialoGPT2 small 18 epochs