davanstrien/model_cards_with_readmes · Datasets at Hugging Face

google/multiberts-seed_4-step_1000k

google

bert

8

33

transformers

0

null

true

false

apache-2.0

['en']

null

0

['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_1000k']

false

true

3,527

false

# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 1000k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model #4, captured at step 1000k (max: 2000k, i.e., 2M steps). ## Model Description This model was captured during a reproduction of [BERT-base uncased](https://github.com/google-research/bert), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP) objectives. The intended uses, limitations, training data and training procedure for the fully trained model are similar to [BERT-base uncased](https://github.com/google-research/bert). Two major differences with the original model: * We pre-trained the MultiBERTs models for 2 million steps using sequence length 512 (instead of 1 million steps using sequence length 128 then 512). * We used an alternative version of Wikipedia and Books Corpus, initially collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962). This is a best-effort reproduction, and so it is probable that differences with the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT). See our [technical report](https://arxiv.org/abs/2106.16163) for more details. ### How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1000k') model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_1000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_1000k') model = BertModel.from_pretrained("google/multiberts-seed_4-step_1000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Citation info ```bibtex @article{sellam2021multiberts, title={The MultiBERTs: BERT Reproductions for Robustness Analysis}, author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, journal={arXiv preprint arXiv:2106.16163}, year={2021} } ```

4285762bf791210792aadcc46a504ed0

ajtamayoh/NER_ehealth_Spanish_mBERT_fine_tuned

ajtamayoh

bert

14

5

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,386

false

# NER_ehealth_Spanish_mBERT_fine_tuned This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6563 - Precision: 0.8094 - Recall: 0.8330 - F1: 0.8210 - Accuracy: 0.9051 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 100 | 0.5335 | 0.8018 | 0.8307 | 0.8160 | 0.9047 | | No log | 2.0 | 200 | 0.5034 | 0.8110 | 0.8253 | 0.8181 | 0.9067 | | No log | 3.0 | 300 | 0.5632 | 0.7932 | 0.8230 | 0.8078 | 0.9038 | | No log | 4.0 | 400 | 0.5904 | 0.8004 | 0.8299 | 0.8149 | 0.9027 | | 0.017 | 5.0 | 500 | 0.5958 | 0.7993 | 0.8330 | 0.8158 | 0.9071 | | 0.017 | 6.0 | 600 | 0.6168 | 0.7980 | 0.8352 | 0.8162 | 0.9022 | | 0.017 | 7.0 | 700 | 0.6219 | 0.8079 | 0.8314 | 0.8195 | 0.9062 | | 0.017 | 8.0 | 800 | 0.6441 | 0.8046 | 0.8299 | 0.8171 | 0.9038 | | 0.017 | 9.0 | 900 | 0.6338 | 0.8086 | 0.8253 | 0.8168 | 0.9051 | | 0.0066 | 10.0 | 1000 | 0.6482 | 0.8021 | 0.8261 | 0.8139 | 0.9029 | | 0.0066 | 11.0 | 1100 | 0.6578 | 0.8039 | 0.8291 | 0.8163 | 0.9038 | | 0.0066 | 12.0 | 1200 | 0.6563 | 0.8094 | 0.8330 | 0.8210 | 0.9051 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

2e99b273e09ef1abea549261ae7f52fb

jonatasgrosman/exp_w2v2t_fr_wav2vec2_s227

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'fr']

false

true

456

false

# exp_w2v2t_fr_wav2vec2_s227 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

2831799ebf9edcdb62fa1f29f9f9ac5d

PabloZubeldia/distilbert-base-uncased-finetuned-tweets

PabloZubeldia

distilbert

24

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,553

false

# distilbert-base-uncased-finetuned-tweets This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2703 - Accuracy: 0.9068 - F1: 0.9081 ## 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: 64 - eval_batch_size: 64 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3212 | 1.0 | 143 | 0.2487 | 0.8989 | 0.8991 | | 0.2031 | 2.0 | 286 | 0.2268 | 0.9077 | 0.9074 | | 0.1474 | 3.0 | 429 | 0.2385 | 0.9094 | 0.9107 | | 0.1061 | 4.0 | 572 | 0.2516 | 0.9103 | 0.9111 | | 0.0804 | 5.0 | 715 | 0.2703 | 0.9068 | 0.9081 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

930663fede03e36c860572151020e87a

DOOGLAK/Tagged_One_250v5_NER_Model_3Epochs_AUGMENTED

DOOGLAK

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['tagged_one250v5_wikigold_split']

null

0

['generated_from_trainer']

true

1,565

false

# Tagged_One_250v5_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v5_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3623 - Precision: 0.5500 - Recall: 0.4923 - F1: 0.5196 - Accuracy: 0.8950 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 91 | 0.3950 | 0.2800 | 0.2138 | 0.2424 | 0.8558 | | No log | 2.0 | 182 | 0.3633 | 0.4938 | 0.4306 | 0.4601 | 0.8887 | | No log | 3.0 | 273 | 0.3623 | 0.5500 | 0.4923 | 0.5196 | 0.8950 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

99f1f86f32061ea33e81a2f56508090b

Khalsuu/english-filipino-wav2vec2-l-xls-r-test-06

Khalsuu

wav2vec2

13

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['filipino_voice']

null

0

['generated_from_trainer']

true

2,187

false

# english-filipino-wav2vec2-l-xls-r-test-06 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.5806 - Wer: 0.6568 ## 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: 0.002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - 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 | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0031 | 2.09 | 400 | 1.2366 | 0.8780 | | 0.9084 | 4.19 | 800 | 1.0653 | 0.8081 | | 0.6484 | 6.28 | 1200 | 1.1648 | 0.8258 | | 0.5335 | 8.38 | 1600 | 1.0903 | 0.7542 | | 0.4359 | 10.47 | 2000 | 0.9466 | 0.7058 | | 0.3629 | 12.57 | 2400 | 0.9266 | 0.7048 | | 0.3057 | 14.66 | 2800 | 1.0879 | 0.7018 | | 0.2477 | 16.75 | 3200 | 1.1113 | 0.7022 | | 0.208 | 18.85 | 3600 | 1.1345 | 0.6742 | | 0.1781 | 20.94 | 4000 | 1.3117 | 0.6974 | | 0.1465 | 23.04 | 4400 | 1.3248 | 0.6916 | | 0.1288 | 25.13 | 4800 | 1.4306 | 0.6523 | | 0.1108 | 27.23 | 5200 | 1.5155 | 0.6685 | | 0.099 | 29.32 | 5600 | 1.5806 | 0.6568 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

6c50bb99da5b8b391bbaec9d697c1232

mqy/mt5-small-finetuned-18jan-4

mqy

mt5

15

4

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

2,152

false

# mt5-small-finetuned-18jan-4 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6070 - Rouge1: 5.8518 - Rouge2: 0.3333 - Rougel: 5.8423 - Rougelsum: 5.7268 ## 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: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 7.6303 | 1.0 | 60 | 3.0842 | 6.1768 | 1.2345 | 6.2047 | 6.1838 | | 3.8899 | 2.0 | 120 | 2.7540 | 7.9407 | 1.0 | 7.8852 | 7.9087 | | 3.4335 | 3.0 | 180 | 2.7391 | 8.5431 | 0.5667 | 8.5448 | 8.4406 | | 3.2524 | 4.0 | 240 | 2.6775 | 8.7375 | 0.4167 | 8.6926 | 8.569 | | 3.0853 | 5.0 | 300 | 2.6776 | 7.7823 | 0.1667 | 7.7548 | 7.6573 | | 2.974 | 6.0 | 360 | 2.6641 | 8.375 | 0.1667 | 8.3333 | 8.2167 | | 2.9018 | 7.0 | 420 | 2.6233 | 7.2137 | 0.3333 | 7.147 | 7.0595 | | 2.859 | 8.0 | 480 | 2.6238 | 6.6125 | 0.4167 | 6.656 | 6.4595 | | 2.8123 | 9.0 | 540 | 2.5961 | 6.4262 | 0.3333 | 6.3682 | 6.2131 | | 2.7843 | 10.0 | 600 | 2.6070 | 5.8518 | 0.3333 | 5.8423 | 5.7268 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

fe6bf9f509391e9766194257746c0028

mqy/mt5-small-finetuned-12feb-1

mqy

mt5

17

0

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

1,904

false

# mt5-small-finetuned-12feb-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4285 - Rouge1: 18.23 - Rouge2: 5.42 - Rougel: 18.09 ## 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: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 3.0346 | 1.0 | 311 | 2.4880 | 17.19 | 5.28 | 17.06 | | 2.8943 | 2.0 | 622 | 2.4751 | 17.77 | 5.18 | 17.59 | | 2.8397 | 3.0 | 933 | 2.4719 | 17.65 | 5.38 | 17.55 | | 2.806 | 4.0 | 1244 | 2.4614 | 18.26 | 5.23 | 18.03 | | 2.7842 | 5.0 | 1555 | 2.4464 | 18.08 | 5.51 | 17.96 | | 2.7855 | 6.0 | 1866 | 2.4437 | 17.9 | 5.37 | 17.8 | | 2.7796 | 7.0 | 2177 | 2.4270 | 18.07 | 5.38 | 17.95 | | 2.7951 | 8.0 | 2488 | 2.4267 | 17.96 | 5.36 | 17.85 | | 2.7864 | 9.0 | 2799 | 2.4285 | 18.23 | 5.42 | 18.09 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

9b6560da7f4ae4395a9443934980224e

burakyldrm/wav2vec2-full-small_gpu_deneme4

burakyldrm

wav2vec2

15

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,087

false

# wav2vec2-full-small_gpu_deneme4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

fff0164b615714fe587a80fc6c799223

jonatasgrosman/exp_w2v2t_it_unispeech-sat_s306

jonatasgrosman

unispeech-sat

10

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['it']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'it']

false

true

463

false

# exp_w2v2t_it_unispeech-sat_s306 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

a1286de1106a94d18ff8bf8a96b12cbd

fulviodan/ddpm-butterflies-128

fulviodan

null

13

3

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,231

false

# ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/fulviodan/ddpm-butterflies-128/tensorboard?#scalars)

dc27bd8ec5d6c47110b97d2dd507f948

Arnold/wav2vec2-large-xlsr-hausa2-demo-colab

Arnold

wav2vec2

18

11

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,556

false

# wav2vec2-large-xlsr-hausa2-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2993 - Wer: 0.4826 ## 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: 9.6e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 13 - gradient_accumulation_steps: 3 - total_train_batch_size: 36 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.1549 | 12.5 | 400 | 2.7289 | 1.0 | | 2.0566 | 25.0 | 800 | 0.4582 | 0.6768 | | 0.4423 | 37.5 | 1200 | 0.3037 | 0.5138 | | 0.2991 | 50.0 | 1600 | 0.2993 | 0.4826 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

898ae311392f4a39d1148bcb3d08be09

Helsinki-NLP/opus-mt-kqn-sv

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-kqn-sv * source languages: kqn * target languages: sv * OPUS readme: [kqn-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kqn-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/kqn-sv/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/kqn-sv/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/kqn-sv/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.kqn.sv | 23.3 | 0.409 |

e86dc333cf9326f5c640871f3c0df897

IMSyPP/hate_speech_en

IMSyPP

bert

7

1,747

transformers

5

text-classification

true

false

mit

['en']

null

0

[]

false

true

652

false

# Hate Speech Classifier for Social Media Content in English Language A monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model. ## Tokenizer During training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent

a547308e307eecba97e5b065d552b3e8

Avrik/abstract-anim-spritesheets

Avrik

null

22

44

diffusers

16

text-to-image

false

creativeml-openrail-m

['en']

null

3

0

3

0

2

0

['stable-diffusion', 'text-to-image', 'image-to-image']

false

true

2,246

false

# Abstract Animation Sprite Sheets An experimental Dreambooth model trained on individual frames of looping 3D animations that were then laid out on a 4x4 grid. Generates sprite sheets that can create very interesting abstract animations. Use the token **AbstrAnm spritesheet**. Size must be set at 512x512 or your outputs may not work properly. **Example prompt:** AbstrAnm spritesheet, animation of a red glowing orb in the sky, highly detailed, fog, atmosphere, glow, sprites, animated, abstract **Negative prompt:** high contrast, text, overlay Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 8 Feel free to experiment with other types of prompts and/or model merges. ![Sample Generations](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/AnimationGrid.gif) You can also upscale it 4x to produce 512x512 animations. Used SD Upscale from AUTOMATIC1111's web UI to add more sharpness and detail. ![Upscaled](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/AnimationGridUpscale.gif) Discovered it's actually quite flexible and could even animate less abstract concepts. ![New Animations](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/natureanims.gif) **Prompt 1:** AbstrAnm spritesheet, animation of magical swirling clouds in the clear blue sky, floating in crystal clear water, circular, sunny, timelapse, lens flare, nature, 35mm lens shot, photorealistic, sprites, animated, art by Greg Rutkowski **Negative prompt:** text, overlay, abstract, boring, empty, barren, simple background Steps: 25, Sampler: DPM++ 2S a, CFG scale: 10 **Prompt 2:** AbstrAnm spritesheet, animation of a beautiful flower blowing in the wind, serene, pink, sunny, timelapse, lens flare, nature, 35mm lens shot, photorealistic, sprites, animated, art by Greg Rutkowski **Negative prompt:** text, overlay, abstract, boring, empty, barren, simple background Steps: 25, Sampler: DPM++ 2S a, CFG scale: 10 Some issues with this model: - May not loop seamlessly - Tends to be too noisy - Sprites aren't usually perfect squares - Small size and short animation (could experiment with training on larger resolutions in the future)

4b27bce4148f90dcdad8f6cc1859912e

fahadtouseef/wav2vec2-base-timit-demo-colab_3

fahadtouseef

wav2vec2

12

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,670

false

# wav2vec2-base-timit-demo-colab_3 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: 3.1942 - Wer: 1.0 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 4.2975 | 3.52 | 500 | 3.1771 | 1.0 | | 3.1468 | 7.04 | 1000 | 3.1917 | 1.0 | | 3.147 | 10.56 | 1500 | 3.1784 | 1.0 | | 3.1467 | 14.08 | 2000 | 3.1850 | 1.0 | | 3.1446 | 17.61 | 2500 | 3.2022 | 1.0 | | 3.1445 | 21.13 | 3000 | 3.2196 | 1.0 | | 3.1445 | 24.65 | 3500 | 3.2003 | 1.0 | | 3.1443 | 28.17 | 4000 | 3.1942 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

d45cdc5ff710ad38df692dd048cbc979

rajat99/Fine_Tuning_XLSR_300M_testing_6_model

rajat99

wav2vec2

9

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,349

false

# Fine_Tuning_XLSR_300M_testing_6_model This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2263 - Wer: 1.0 ## 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 | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 5.466 | 23.53 | 400 | 3.2263 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

e7d04393e4cea72629c5891105c8850f

Helsinki-NLP/opus-mt-de-nso

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-de-nso * source languages: de * target languages: nso * OPUS readme: [de-nso](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-nso/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-nso/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-nso/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-nso/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.de.nso | 31.1 | 0.519 |

5d82ae29ce4508767cbf2358b7b2f5a7

edugp/kenlm

edugp

null

167

0

null

9

null

false

mit

['es', 'af', 'ar', 'arz', 'as', 'bn', 'fr', 'sw', 'eu', 'ca', 'zh', 'en', 'hi', 'ur', 'id', 'pt', 'vi', 'gu', 'kn', 'ml', 'mr', 'ta', 'te', 'yo']

['wikipedia', 'oscar']

null

0

2

1

['kenlm', 'perplexity', 'n-gram', 'kneser-ney', 'bigscience']

false

true

2,467

false

# KenLM models This repo contains several KenLM models trained on different tokenized datasets and languages. KenLM models are probabilistic n-gram languge models that models. One use case of these models consist on fast perplexity estimation for [filtering or sampling large datasets](https://huggingface.co/bertin-project/bertin-roberta-base-spanish). For example, one could use a KenLM model trained on French Wikipedia to run inference on a large dataset and filter out samples that are very unlike to appear on Wikipedia (high perplexity), or very simple non-informative sentences that could appear repeatedly (low perplexity). At the root of this repo you will find different directories named after the dataset models were trained on (e.g. `wikipedia`, `oscar`). Within each directory, you will find several models trained on different language subsets of the dataset (e.g. `en (English)`, `es (Spanish)`, `fr (French)`). For each language you will find three different files * `{language}.arpa.bin`: The trained KenLM model binary * `{language}.sp.model`: The trained SentencePiece model used for tokenization * `{language}.sp.vocab`: The vocabulary file for the SentencePiece model The models have been trained using some of the preprocessing steps from [cc_net](https://github.com/facebookresearch/cc_net), in particular replacing numbers with zeros and normalizing punctuation. So, it is important to keep the default values for the parameters: `lower_case`, `remove_accents`, `normalize_numbers` and `punctuation` when using the pre-trained models in order to replicate the same pre-processing steps at inference time. # Dependencies * KenLM: `pip install https://github.com/kpu/kenlm/archive/master.zip` * SentencePiece: `pip install sentencepiece` # Example: ``` from model import KenlmModel # Load model trained on English wikipedia model = KenlmModel.from_pretrained("wikipedia", "en") # Get perplexity model.get_perplexity("I am very perplexed") # 341.3 (low perplexity, since sentence style is formal and with no grammar mistakes) model.get_perplexity("im hella trippin") # 46793.5 (high perplexity, since the sentence is colloquial and contains grammar mistakes) ``` In the example above we see that, since Wikipedia is a collection of encyclopedic articles, a KenLM model trained on it will naturally give lower perplexity scores to sentences with formal language and no grammar mistakes than colloquial sentences with grammar mistakes.

a07f4937d88c6260c98058dceb7f5f34

NimaBoscarino/efficientformer-l7-300

NimaBoscarino

null

5

0

timm

0

image-classification

false

apache-2.0

['en']

['imagenet-1k']

null

0

['mobile', 'vison', 'image-classification']

false

true

3,704

false

# EfficientFormer-L7 ## Table of Contents - [EfficientFormer-L7](#-model_id--defaultmymodelname-true) - [Table of Contents](#table-of-contents) - [Model Details](#model-details) - [How to Get Started with the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Direct Use](#direct-use) - [Downstream Use](#downstream-use) - [Misuse and Out-of-scope Use](#misuse-and-out-of-scope-use) - [Limitations and Biases](#limitations-and-biases) - [Training](#training) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Evaluation Results](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Citation Information](#citation-information) <model_details> ## Model Details  EfficientFormer-L7, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance. This checkpoint of EfficientFormer-L7 was trained for 300 epochs. - Developed by: Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren - Language(s): English - License: This model is licensed under the apache-2.0 license - Resources for more information: - [Research Paper](https://arxiv.org/abs/2206.01191) - [GitHub Repo](https://github.com/snap-research/EfficientFormer/) </model_details> <how_to_start> ## How to Get Started with the Model Use the code below to get started with the model. ```python # A nice code snippet here that describes how to use the model... ``` </how_to_start> <uses> ## Uses #### Direct Use This model can be used for image classification and semantic segmentation. On mobile devices (the model was tested on iPhone 12), the CoreML checkpoints will perform these tasks with low latency. <Limitations_and_Biases> ## Limitations and Biases Though most designs in EfficientFormer are general-purposed, e.g., dimension- consistent design and 4D block with CONV-BN fusion, the actual speed of EfficientFormer may vary on other platforms. For instance, if GeLU is not well supported while HardSwish is efficiently implemented on specific hardware and compiler, the operator may need to be modified accordingly. The proposed latency-driven slimming is simple and fast. However, better results may be achieved if search cost is not a concern and an enumeration-based brute search is performed. Since the model was trained on Imagenet-1K, the [biases embedded in that dataset](https://huggingface.co/datasets/imagenet-1k#considerations-for-using-the-data) will be reflected in the EfficientFormer models. </Limitations_and_Biases> <Training> ## Training #### Training Data This model was trained on ImageNet-1K. See the [data card](https://huggingface.co/datasets/imagenet-1k) for additional information. #### Training Procedure * Parameters: 82.1 M * GMACs: 10.2 * Train. Epochs: 300 Trained on a cluster with NVIDIA A100 and V100 GPUs. </Training> <Eval_Results> ## Evaluation Results Top-1 Accuracy: 83.3% on ImageNet 10K Latency: 3.0 ms </Eval_Results> <Cite> ## Citation Information ```bibtex @article{li2022efficientformer, title={EfficientFormer: Vision Transformers at MobileNet Speed}, author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian}, journal={arXiv preprint arXiv:2206.01191}, year={2022} } ``` </Cite>

1d63eaf4c91a9f3db544afe686fb5bee

SirVeggie/mixes

SirVeggie

null

8

0

null

5

null

false

creativeml-openrail-m

null

0

[]

false

true

4,723

false

# Model mixes Custom models created by combining different models together. You can and should influence the style of these models by mentioning the keywords of the artists included at a sufficiently high weight:\ For example (m_wlop illustration style:1.3) ## Symbol legend ``` A + B = weighted sum A + (B - C) = add difference @ 0.5 = merge strength/multiplier ``` Models marked with ★ are recommended. ## 1-berry First step of berry mix. (not uploaded, but used in most mixes) ``` novel + (F222 - sd1.4) @ 1.0 ``` ## anymix ★ Mix of the models based on anything v3. ``` A: wlop-any + nixeu-any @ 0.5 B: ross-any + robutts-any @ 0.5 C: A + B @ 0.5 1-berry + C @ 0.5 ``` ## diffmix ★ Similar to anymix, but using add differential for the first level merges. Specifics have been forgotten. Guweiz and Greg might be included - if I recall correctly - in addition to the models included in anymix. ## anydiff ★★ Mix anymix and diffmix at @0.5 (not included in the files) ## megamix Weighted sum merge between all of my models at equal proportions, including both waifu diffusion and anything v3 versions of the same model. Artists included are Wlop (m_wlop), Nixeu (m_nixeu), RossDraws (m_ross), Cutesexyrobutts (m_robutts), Guweiz (m_guweiz) and Grzegorz Rutkowski (m_greg). ## smoothmix ★ A semi-realistic model with smooth details. A complex merge that I forgot the details of. Includes probably 10-20 different models from various sources. ## different-v3-c ★★★ ``` smooth-diff = smoothmix + (diffmix - novel) @ 1.0 hd-ross = hd-18 + (ross - anything) @ 1.0 anymix-hardlight = anymix + (hardlight - anything) @ 1.0 #### Merge Block Weighted #### model_0 : - smooth.safetensors model_1 : diffmix.safetensors base_alpha : 0.8 output_file: S:\Library\Files\Tools\Super SD 2.0\models\Stable-diffusion\1-different.ckpt weights : 0,0,0,0,0,0,0,0,0,0,0,0,0.85,0.05,0.02,0.01,0.01,0.02,0.05,0.1,0.2,0.4,0.6,0.8,1 skip ids : 0 : 0:None, 1:Skip, 2:Reset #### Merge Block Weighted #### model_0 : 1-different.ckpt model_1 : smooth-diff.ckpt base_alpha : 0.1 output_file: S:\Library\Files\Tools\Super SD 2.0\models\Stable-diffusion\2-different.ckpt weights : 0,0,0,0,0,0,0,0,0,0,0,0,0.2,0.15,0.25,0.5,0.7,0.8,0.6,0.2,0.05,0.01,0,0,0 skip ids : 0 : 0:None, 1:Skip, 2:Reset #### Merge Block Weighted #### model_0 : 2-different.ckpt model_1 : protogenX53Photorealism_10.safetensors base_alpha : 0.1 output_file: S:\Library\Files\Tools\Super SD 2.0\models\Stable-diffusion\3-different.ckpt weights : 0.2,0.2,0.2,0.2,0.25,0.25,0.3,0.4,0.4,0.3,0.2,0.1,0.2,0,0,0,0,0,0,0,0,0,0,0,0 skip ids : 0 : 0:None, 1:Skip, 2:Reset #### Merge Block Weighted #### model_0 : 3-different.ckpt model_1 : protogenV22Anime_22.safetensors base_alpha : 0.1 output_file: S:\Library\Files\Tools\Super SD 2.0\models\Stable-diffusion\4-different.ckpt weights : 0.75,0.5,0.3,0.15,0.08,0.04,0.02,0.01,0.01,0.01,0.01,0.01,0.1,0,0,0,0,0,0,0,0,0,0,0,0 skip ids : 0 : 0:None, 1:Skip, 2:Reset #### Merge Block Weighted #### model_0 : 4-different.ckpt model_1 : hd-ross.ckpt base_alpha : 0.1 output_file: S:\Library\Files\Tools\Super SD 2.0\models\Stable-diffusion\different-v1.ckpt weights : 0,0,0,0,0,0.1,0.21,0.28,0.3,0.26,0.18,0.1,0.05,0.1,0.18,0.22,0.23,0.2,0.12,0,0,0,0,0,0 skip ids : 0 : 0:None, 1:Skip, 2:Reset #### Merge Block Weighted #### model_0 : different-v1.ckpt model_1 : anymix-hardlight.ckpt base_alpha : 0.2 output_file: S:\Library\Files\Tools\Super SD 2.0\models\Stable-diffusion\different-v1-x.ckpt weights : 0.05,0.12,0.19,0.2,0.17,0.12,0.06,0.05,0.07,0.08,0.11,0.15,0.25,0.25,0.18,0.11,0.05,0.08,0.12,0.14,0.15,0.13,0.11,0.09,0.1 skip ids : 0 : 0:None, 1:Skip, 2:Reset #### Merge Block Weighted #### model_0 : different-v1-x.ckpt model_1 : AbyssOrangeMix2_nsfw.safetensors base_alpha : 0.1 output_file: S:\Library\Files\Tools\Super SD 2.0\models\Stable-diffusion\different-v3-c.ckpt weights : 0.5,0.4,0.3,0.2,0.2,0.2,0.2,0.2,0.25,0.3,0.35,0.4,0.45,0.4,0.35,0.3,0.25,0.2,0.15,0.1,0.05,0,0,0,0 skip ids : 0 : 0:None, 1:Skip, 2:Reset ``` ## Links to models https://huggingface.co/SirVeggie/wlop\ https://huggingface.co/SirVeggie/nixeu\ https://huggingface.co/SirVeggie/ross_draws\ https://huggingface.co/SirVeggie/cutesexyrobutts\ https://huggingface.co/SirVeggie/guweiz\ https://huggingface.co/SirVeggie/greg_rutkowski https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release\ https://huggingface.co/darkstorm2150/Protogen_x5.3_Official_Release\ https://huggingface.co/WarriorMama777/OrangeMixs#model-detail--merge-recipes

3f9447ac19e9fe5f398271b4765e43d2

gsdf/Replicant

gsdf

null

11

0

diffusers

24

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers']

false

true

6,595

false

# Please enable hires. fix when using it. Replicant is built by merging several models with fine-tuning WD1.4 and photorealistic SD2.0 models that works with danbooru tags.I trained 4 models to merge and prepared several LoRa models for tuning.As with SD1.x, merging individually trained models is better quality than training many concepts at once.This model is a workflow test and is not good enough. WD1.4 seems to vary greatly in quality with/without Hires. fix.In Replicant, the difference in quality is more noticeable because of the detailed drawings.So I recommend enabling Hires.fix for use. # Example Denoising strength 0.6 is a bit large. I like 0.57 better. The optimal CFG Scale value should also be examined. Hands often multiply. When this happens, increase the value of "extra hands". ![sample1](https://huggingface.co/gsdf/Replicant/resolve/main/sample_01.png) ((masterpiece, best quality)), 1girl, flower, solo, dress, holding, sky, cloud, hat, outdoors, bangs, bouquet, rose, expressionless, blush, pink hair, flower field, red flower, pink eyes, white dress, looking at viewer, midium hair, holding flower, small breasts, red rose, holding bouquet, sun hat, white headwear, depth of field Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit,(extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 576x384, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent ![sample2](https://huggingface.co/gsdf/Replicant/resolve/main/sample_02.png) ((masterpiece, best quality)), 1girl, skirt, shoes, solo, jacket, holding, alley, sitting, can, sneakers, hood, bag, hoodie, squatting, bangs, shirt, black hair, black skirt, short hair, white jacket, looking away, white footwear, full body, red eyes, long sleeves, open jacket, open clothes, holding can, Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit,(extra arms:1.2), extra legs, extra hands, fewer digits , long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes,drinking Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 576x384, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent ![sample3](https://huggingface.co/gsdf/Replicant/resolve/main/sample_03.png) ((masterpiece, best quality)), 1girl, blood, solo, wings, halo, dress, socks, angel, long hair, shoes, standing, ribbon, long hair, blue eyes, angel wings, blood on clothes, white hair, full body, white wings, black footwear, white dress, feathered wings, white sock, white background, long sleeves, simple background, Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit,(extra arms:1.2), extra legs, extra hands, fewer digits , long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 384x576, Denoising strength: 0.57, Hires upscale: 2, Hires upscaler: Latent ![sample4](https://huggingface.co/gsdf/Replicant/resolve/main/sample_04.png) ((masterpiece, best quality)), 1girl, car, solo, shorts, jacket, bangs, sitting, shirt, shoes, hairclip, socks, sneakers, denim, sidelocks, motor vehicle, long hair, ground vehicle,brown hair, looking at viewer, white shirt, black jacket, long sleeves, sports car, vehicle focus, aqua eyes, white socks, blue shorts, open clothes, black footwear, denim shorts, open jacket Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit, (extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 384x576, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent ![sample5](https://huggingface.co/gsdf/Replicant/resolve/main/sample_05.png) ((masterpiece, best quality)), 1girl, solo, twintails, lollipop, smile, ahoge, hairclip, bow, holding, ribbon, frills, blush, shirt, :d, stuffed toy, pink hair, stuffed animal, red nails, hair ornament, open mouth, looking at viewer, stuffed bunny, nail polish, short sleeves, object hug, puffy sleeves, hair between eyes, upper body, light blue eyes, puffy short sleeves, holding stuffed toy, hair bow, white bow, doll hug, hair ribbon, streaked hair, white shirt Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit, (extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 512x512, Denoising strength: 0.57, Hires upscale: 2, Hires upscaler: Latent ![sample6](https://huggingface.co/gsdf/Replicant/resolve/main/sample_06.png) ((masterpiece, best quality)), 1girl, solo, tail, barefoot, skirt, sleeping, lying, grass, shirt, outdoors, socks, flower, long hair, on side, animal ears, blonde hair, cat tail, closed eyes, blue skirt, white shirt, cat ears, school uniform, dappled sunlight, short sleeves, bare legs, closed mouth, full body, pleated skirt Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit, (extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 576x384, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent ![sample7](https://huggingface.co/gsdf/Replicant/resolve/main/sample_07.png) ((masterpiece, best quality)), 1girl, car, building, gun, weapon, outdoors, solo, military, day, city, standing, serious, pants, rifle, holding, jacket, motor vehicle, ground vehicle, brown hair, assault rifle, long hair, vehicle focus, holding gun, holding weapon, black footwear, military vehicle, full body, depth of field, Negative prompt: (low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), inaccurate eyes, extra digit, (extra arms:1.2), extra hands, fewer digits ,long body, cropped, jpeg artifacts, signature, watermark, username, blurry, empty eyes Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 576x384, Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent

22fc35ea63a98a789b7ef833037c49d7

leokai/distilroberta-base-wikitextepoch_50

leokai

roberta

6

2

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,757

false

# distilroberta-base-wikitextepoch_50 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6360 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 1.9729 | 1.0 | 2145 | 1.7725 | | 1.9158 | 2.0 | 4290 | 1.7521 | | 1.8479 | 3.0 | 6435 | 1.7376 | | 1.8081 | 4.0 | 8580 | 1.7272 | | 1.7966 | 5.0 | 10725 | 1.7018 | | 1.7284 | 6.0 | 12870 | 1.7010 | | 1.7198 | 7.0 | 15015 | 1.6868 | | 1.6985 | 8.0 | 17160 | 1.6879 | | 1.6712 | 9.0 | 19305 | 1.6930 | | 1.6489 | 10.0 | 21450 | 1.6594 | | 1.6643 | 11.0 | 23595 | 1.6856 | | 1.6215 | 12.0 | 25740 | 1.6816 | | 1.6125 | 13.0 | 27885 | 1.6714 | | 1.5936 | 14.0 | 30030 | 1.6760 | | 1.5745 | 15.0 | 32175 | 1.6660 | | 1.572 | 16.0 | 34320 | 1.6690 | | 1.5614 | 17.0 | 36465 | 1.6807 | | 1.558 | 18.0 | 38610 | 1.6711 | | 1.5305 | 19.0 | 40755 | 1.6446 | | 1.5021 | 20.0 | 42900 | 1.6573 | | 1.4923 | 21.0 | 45045 | 1.6648 | | 1.5086 | 22.0 | 47190 | 1.6757 | | 1.4895 | 23.0 | 49335 | 1.6525 | | 1.4918 | 24.0 | 51480 | 1.6577 | | 1.4642 | 25.0 | 53625 | 1.6633 | | 1.4604 | 26.0 | 55770 | 1.6462 | | 1.4644 | 27.0 | 57915 | 1.6509 | | 1.4633 | 28.0 | 60060 | 1.6417 | | 1.4188 | 29.0 | 62205 | 1.6519 | | 1.4066 | 30.0 | 64350 | 1.6363 | | 1.409 | 31.0 | 66495 | 1.6419 | | 1.4029 | 32.0 | 68640 | 1.6510 | | 1.4013 | 33.0 | 70785 | 1.6522 | | 1.3939 | 34.0 | 72930 | 1.6498 | | 1.3648 | 35.0 | 75075 | 1.6423 | | 1.3682 | 36.0 | 77220 | 1.6504 | | 1.3603 | 37.0 | 79365 | 1.6511 | | 1.3621 | 38.0 | 81510 | 1.6533 | | 1.3783 | 39.0 | 83655 | 1.6426 | | 1.3707 | 40.0 | 85800 | 1.6542 | | 1.3628 | 41.0 | 87945 | 1.6671 | | 1.3359 | 42.0 | 90090 | 1.6394 | | 1.3433 | 43.0 | 92235 | 1.6409 | | 1.3525 | 44.0 | 94380 | 1.6366 | | 1.3312 | 45.0 | 96525 | 1.6408 | | 1.3389 | 46.0 | 98670 | 1.6225 | | 1.3323 | 47.0 | 100815 | 1.6309 | | 1.3294 | 48.0 | 102960 | 1.6151 | | 1.3356 | 49.0 | 105105 | 1.6374 | | 1.3285 | 50.0 | 107250 | 1.6360 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.5.0 - Datasets 2.4.0 - Tokenizers 0.12.1

7bcac475ce8cbc81eaf835ed180d9f71

cemsubakan/cnn14-esc50

cemsubakan

null

7

4

null

0

null

false

apache-2.0

['en']

['ESC50']

null

0

['Sound Classification', 'CNN14']

false

true

2,570

false

e944b495c9e72a23881adb0a7de73b19

ArBert/bert-base-uncased-finetuned-ner

ArBert

bert

12

4

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,533

false

# bert-base-uncased-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0905 - Precision: 0.9068 - Recall: 0.9200 - F1: 0.9133 - Accuracy: 0.9787 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1266 | 1.0 | 1123 | 0.0952 | 0.8939 | 0.8869 | 0.8904 | 0.9742 | | 0.0741 | 2.0 | 2246 | 0.0866 | 0.8936 | 0.9247 | 0.9089 | 0.9774 | | 0.0496 | 3.0 | 3369 | 0.0905 | 0.9068 | 0.9200 | 0.9133 | 0.9787 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

5a466f5a017335b3f0a7df182392be0d

Helsinki-NLP/opus-mt-en-itc

Helsinki-NLP

marian

11

8

transformers

1

translation

true

false

apache-2.0

['en', 'it', 'ca', 'rm', 'es', 'ro', 'gl', 'sc', 'co', 'wa', 'pt', 'oc', 'an', 'id', 'fr', 'ht', 'itc']

null

1

0

['translation']

false

true

5,233

false

### eng-itc * source group: English * target group: Italic languages * OPUS readme: [eng-itc](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-itc/README.md) * model: transformer * source language(s): eng * target language(s): arg ast cat cos egl ext fra frm_Latn gcf_Latn glg hat ind ita lad lad_Latn lat_Latn lij lld_Latn lmo max_Latn mfe min mwl oci pap pms por roh ron scn spa tmw_Latn vec wln zlm_Latn zsm_Latn * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-itc/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-itc/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-itc/opus2m-2020-08-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2016-enro-engron.eng.ron | 27.1 | 0.565 | | newsdiscussdev2015-enfr-engfra.eng.fra | 29.9 | 0.574 | | newsdiscusstest2015-enfr-engfra.eng.fra | 35.3 | 0.609 | | newssyscomb2009-engfra.eng.fra | 27.7 | 0.567 | | newssyscomb2009-engita.eng.ita | 28.6 | 0.586 | | newssyscomb2009-engspa.eng.spa | 29.8 | 0.569 | | news-test2008-engfra.eng.fra | 25.0 | 0.536 | | news-test2008-engspa.eng.spa | 27.1 | 0.548 | | newstest2009-engfra.eng.fra | 26.7 | 0.557 | | newstest2009-engita.eng.ita | 28.9 | 0.583 | | newstest2009-engspa.eng.spa | 28.9 | 0.567 | | newstest2010-engfra.eng.fra | 29.6 | 0.574 | | newstest2010-engspa.eng.spa | 33.8 | 0.598 | | newstest2011-engfra.eng.fra | 30.9 | 0.590 | | newstest2011-engspa.eng.spa | 34.8 | 0.598 | | newstest2012-engfra.eng.fra | 29.1 | 0.574 | | newstest2012-engspa.eng.spa | 34.9 | 0.600 | | newstest2013-engfra.eng.fra | 30.1 | 0.567 | | newstest2013-engspa.eng.spa | 31.8 | 0.576 | | newstest2016-enro-engron.eng.ron | 25.9 | 0.548 | | Tatoeba-test.eng-arg.eng.arg | 1.6 | 0.120 | | Tatoeba-test.eng-ast.eng.ast | 17.2 | 0.389 | | Tatoeba-test.eng-cat.eng.cat | 47.6 | 0.668 | | Tatoeba-test.eng-cos.eng.cos | 4.3 | 0.287 | | Tatoeba-test.eng-egl.eng.egl | 0.9 | 0.101 | | Tatoeba-test.eng-ext.eng.ext | 8.7 | 0.287 | | Tatoeba-test.eng-fra.eng.fra | 44.9 | 0.635 | | Tatoeba-test.eng-frm.eng.frm | 1.0 | 0.225 | | Tatoeba-test.eng-gcf.eng.gcf | 0.7 | 0.115 | | Tatoeba-test.eng-glg.eng.glg | 44.9 | 0.648 | | Tatoeba-test.eng-hat.eng.hat | 30.9 | 0.533 | | Tatoeba-test.eng-ita.eng.ita | 45.4 | 0.673 | | Tatoeba-test.eng-lad.eng.lad | 5.6 | 0.279 | | Tatoeba-test.eng-lat.eng.lat | 12.1 | 0.380 | | Tatoeba-test.eng-lij.eng.lij | 1.4 | 0.183 | | Tatoeba-test.eng-lld.eng.lld | 0.5 | 0.199 | | Tatoeba-test.eng-lmo.eng.lmo | 0.7 | 0.187 | | Tatoeba-test.eng-mfe.eng.mfe | 83.6 | 0.909 | | Tatoeba-test.eng-msa.eng.msa | 31.3 | 0.549 | | Tatoeba-test.eng.multi | 38.0 | 0.588 | | Tatoeba-test.eng-mwl.eng.mwl | 2.7 | 0.322 | | Tatoeba-test.eng-oci.eng.oci | 8.2 | 0.293 | | Tatoeba-test.eng-pap.eng.pap | 46.7 | 0.663 | | Tatoeba-test.eng-pms.eng.pms | 2.1 | 0.194 | | Tatoeba-test.eng-por.eng.por | 41.2 | 0.635 | | Tatoeba-test.eng-roh.eng.roh | 2.6 | 0.237 | | Tatoeba-test.eng-ron.eng.ron | 40.6 | 0.632 | | Tatoeba-test.eng-scn.eng.scn | 1.6 | 0.181 | | Tatoeba-test.eng-spa.eng.spa | 49.5 | 0.685 | | Tatoeba-test.eng-vec.eng.vec | 1.6 | 0.223 | | Tatoeba-test.eng-wln.eng.wln | 7.1 | 0.250 | ### System Info: - hf_name: eng-itc - source_languages: eng - target_languages: itc - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-itc/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'it', 'ca', 'rm', 'es', 'ro', 'gl', 'sc', 'co', 'wa', 'pt', 'oc', 'an', 'id', 'fr', 'ht', 'itc'] - src_constituents: {'eng'} - tgt_constituents: {'ita', 'cat', 'roh', 'spa', 'pap', 'bjn', 'lmo', 'mwl', 'lij', 'lat_Latn', 'lad_Latn', 'pcd', 'lat_Grek', 'ext', 'ron', 'ast', 'glg', 'pms', 'zsm_Latn', 'srd', 'gcf_Latn', 'lld_Latn', 'min', 'tmw_Latn', 'cos', 'wln', 'zlm_Latn', 'por', 'egl', 'oci', 'vec', 'arg', 'ind', 'fra', 'hat', 'lad', 'max_Latn', 'frm_Latn', 'scn', 'mfe'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-itc/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-itc/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: itc - short_pair: en-itc - chrF2_score: 0.588 - bleu: 38.0 - brevity_penalty: 0.9670000000000001 - ref_len: 73951.0 - src_name: English - tgt_name: Italic languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: itc - prefer_old: False - long_pair: eng-itc - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

582bc4d60fc0f2ac280aff045e7638a9

Helsinki-NLP/opus-mt-tr-az

Helsinki-NLP

marian

11

28

transformers

1

translation

true

false

apache-2.0

['tr', 'az']

null

1

0

['translation']

false

true

1,997

false

### tur-aze * source group: Turkish * target group: Azerbaijani * OPUS readme: [tur-aze](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-aze/README.md) * model: transformer-align * source language(s): tur * target language(s): aze_Latn * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.tur.aze | 27.7 | 0.551 | ### System Info: - hf_name: tur-aze - source_languages: tur - target_languages: aze - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-aze/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tr', 'az'] - src_constituents: {'tur'} - tgt_constituents: {'aze_Latn'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-aze/opus-2020-06-16.test.txt - src_alpha3: tur - tgt_alpha3: aze - short_pair: tr-az - chrF2_score: 0.551 - bleu: 27.7 - brevity_penalty: 1.0 - ref_len: 5436.0 - src_name: Turkish - tgt_name: Azerbaijani - train_date: 2020-06-16 - src_alpha2: tr - tgt_alpha2: az - prefer_old: False - long_pair: tur-aze - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

83b4ded85f8f36f3eb2bb59456790697

DrishtiSharma/lwg_chebakia

DrishtiSharma

null

4

0

transformers

0

null

false

mit

null

0

['huggan', 'gan']

false

true

775

false

# MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

a6e2b485983a99e6ad784e4da1cc69ad

Sounak/bert-large-finetuned

Sounak

bert

8

3

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,413

false

# Sounak/bert-large-finetuned This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.7634 - Validation Loss: 1.6843 - Epoch: 0 ## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 157, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.7634 | 1.6843 | 0 | ### Framework versions - Transformers 4.19.2 - TensorFlow 2.9.1 - Datasets 2.2.2 - Tokenizers 0.12.1

6e6c6349af641ecc064f5931f78225b3

Lemswasabi/wav2vec2-base-luxembourgish-4h-with-lm

Lemswasabi

wav2vec2

14

0

transformers

0

automatic-speech-recognition

true

false

mit

['lb']

null

0

['automatic-speech-recognition', 'generated_from_trainer']

false

true

1,810

false

# ## Model description We pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 4h of labelled Luxembourgish Speech from the same domain. Additionally, we rescore the output transcription with a 5-gram language model trained on text corpora from RTL.lu and the Luxembourgish parliament. ## 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: 7.5e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1 ## Citation This model is a result of our paper `IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS` submitted to the [IEEE SLT 2022 workshop](https://slt2022.org/) ``` @misc{lb-wav2vec2, author = {Nguyen, Le Minh and Nayak, Shekhar and Coler, Matt.}, keywords = {Luxembourgish, multilingual speech recognition, language modelling, wav2vec 2.0 XLSR-53, under-resourced language}, title = {IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS}, year = {2022}, copyright = {2023 IEEE} } ```

0fe3f510417ccd3f78dfcdf1b2ed2c03

ali2066/finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04

ali2066

distilbert

13

10

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,796

false

# finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1620 - Precision: 0.3509 - Recall: 0.3793 - F1: 0.3646 - Accuracy: 0.9468 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.2997 | 0.1125 | 0.2057 | 0.1454 | 0.8669 | | No log | 2.0 | 76 | 0.2620 | 0.1928 | 0.2849 | 0.2300 | 0.8899 | | No log | 3.0 | 114 | 0.2497 | 0.1923 | 0.2906 | 0.2314 | 0.8918 | | No log | 4.0 | 152 | 0.2474 | 0.1819 | 0.3377 | 0.2365 | 0.8905 | | No log | 5.0 | 190 | 0.2418 | 0.2128 | 0.3264 | 0.2576 | 0.8997 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

e8fdbd38ea2daf07cb68a1056a4e7e93

BatuhanYilmaz/dummy-model

BatuhanYilmaz

camembert

4

2

transformers

0

fill-mask

false

true

false

mit

null

0

['generated_from_keras_callback']

true

822

false

# 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

ebbbab8e6fe4f894d488bd5864f09a10

tucan9389/distilbert-base-uncased-finetuned-squad

tucan9389

distilbert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,285

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1560 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2252 | 1.0 | 5533 | 1.1671 | | 0.9494 | 2.0 | 11066 | 1.1279 | | 0.7696 | 3.0 | 16599 | 1.1560 | ### Framework versions - Transformers 4.12.4 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

e32cd619dd700a951c49f20c6623b5c0

elliotthwang/mt5-small-finetuned-tradition-zh

elliotthwang

mt5

16

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['xlsum']

null

0

['generated_from_trainer']

true

1,802

false

# mt5-small-finetuned-tradition-zh This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 2.9218 - Rouge1: 5.7806 - Rouge2: 1.266 - Rougel: 5.761 - Rougelsum: 5.7833 ## 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: 5.6e-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: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| | 4.542 | 1.0 | 2336 | 3.1979 | 4.8334 | 1.025 | 4.8142 | 4.8326 | | 3.7542 | 2.0 | 4672 | 3.0662 | 5.2155 | 1.0978 | 5.2025 | 5.2158 | | 3.5706 | 3.0 | 7008 | 3.0070 | 5.5471 | 1.3397 | 5.5386 | 5.5391 | | 3.4668 | 4.0 | 9344 | 2.9537 | 5.5865 | 1.1558 | 5.5816 | 5.5964 | | 3.4082 | 5.0 | 11680 | 2.9391 | 5.8061 | 1.3462 | 5.7944 | 5.812 | | 3.375 | 6.0 | 14016 | 2.9218 | 5.7806 | 1.266 | 5.761 | 5.7833 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

c3ac2f23cad9b3bdea266e0766021ef3

tomekkorbak/hopeful_newton

tomekkorbak

null

2

0

null

0

null

false

mit

['en']

['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000']

null

0

['generated_from_trainer']

true

8,009

false

# hopeful_newton This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 3147 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'every_n_steps': 32, 'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 32, 'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '9e6c78543a6ff1e4089002c38864d5a9cf71ec90', 'value_head_config': {'is_detached': False}}, 'path_or_name': 'tomekkorbak/nervous_wozniak'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 512, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'hopeful_newton', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 3346, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/1cgjg57y

40fa81d3952f09a1a9d01a888751dd05

google/mt5-small

google

mt5

10

193,292

transformers

37

text2text-generation

true

apache-2.0

['multilingual', 'af', 'am', 'ar', 'az', 'be', 'bg', 'bn', 'ca', 'ceb', 'co', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fil', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'haw', 'hi', 'hmn', 'ht', 'hu', 'hy', 'ig', 'is', 'it', 'iw', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lb', 'lo', 'lt', 'lv', 'mg', 'mi', 'mk', 'ml', 'mn', 'mr', 'ms', 'mt', 'my', 'ne', 'nl', False, 'ny', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'sm', 'sn', 'so', 'sq', 'sr', 'st', 'su', 'sv', 'sw', 'ta', 'te', 'tg', 'th', 'tr', 'uk', 'und', 'ur', 'uz', 'vi', 'xh', 'yi', 'yo', 'zh', 'zu']

['mc4']

null

2

0

1

0

[]

false

true

2,246

false

[Google's mT5](https://github.com/google-research/multilingual-t5) mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. **Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5) Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel* ## Abstract The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. All of the code and model checkpoints used in this work are publicly available.

8ece6e015d555d9189ab3b98c4314480

gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola_128

gokuls

mobilebert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,717

false

# mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.7034 - Matthews Correlation: 0.1046 ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.6386 | 1.0 | 1669 | 0.7034 | 0.1046 | | 0.5613 | 2.0 | 3338 | 0.7201 | 0.0912 | | 0.535 | 3.0 | 5007 | 0.7257 | 0.1111 | | 0.5023 | 4.0 | 6676 | 0.7109 | 0.1655 | | 0.4569 | 5.0 | 8345 | 0.7769 | 0.1762 | | 0.4162 | 6.0 | 10014 | 0.7752 | 0.1431 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

e24b075767c2c8235c3621ff86306811

jonatasgrosman/exp_w2v2t_nl_vp-sv_s607

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['nl']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'nl']

false

true

469

false

# exp_w2v2t_nl_vp-sv_s607 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

2f0e4d618eda41f349bdd47589e9efac

Williamlokok/ddpm-butterflies-128

Williamlokok

null

27

1

diffusers

0

null

false

apache-2.0

['en']

['cars']

null

0

[]

false

true

1,201

false

# ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `cars` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Williamlokok/ddpm-butterflies-128/tensorboard?#scalars)

38e12942043eeea386af6ee37f583fef

ykleeee/wav2vec2-5epochs-3e4

ykleeee

wav2vec2

13

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,056

false

# wav2vec2-owndata This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2515 - Wer: 0.3212 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.262 | 0.36 | 100 | 3.4482 | 0.9832 | | 3.0032 | 0.72 | 200 | 2.9441 | 0.9832 | | 2.9141 | 1.08 | 300 | 2.9393 | 0.9832 | | 2.8585 | 1.44 | 400 | 2.8848 | 0.9627 | | 2.2837 | 1.8 | 500 | 2.1732 | 1.0111 | | 0.9834 | 2.16 | 600 | 0.8765 | 0.7345 | | 0.7288 | 2.52 | 700 | 0.5741 | 0.5641 | | 0.5521 | 2.88 | 800 | 0.3937 | 0.4467 | | 0.3751 | 3.24 | 900 | 0.3484 | 0.4112 | | 0.3733 | 3.6 | 1000 | 0.2964 | 0.3912 | | 0.2443 | 3.96 | 1100 | 0.2673 | 0.3446 | | 0.2667 | 4.32 | 1200 | 0.2657 | 0.3357 | | 0.2237 | 4.68 | 1300 | 0.2515 | 0.3212 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.1 - Datasets 2.9.0 - Tokenizers 0.10.3

342cc7b45d1018ff040fc9baec2e8164

Supreeth/distilbert-base-uncased-MLM

Supreeth

distilbert

16

9

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,045

false

# distilbert-base-uncased-MLM This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2156 - Accuracy: 0.5252 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0a0+936e930 - Datasets 2.8.0 - Tokenizers 0.13.2

a39b54c63153549fee14fcc2397f3237

dxiao/bert-finetuned-ner-80percent

dxiao

bert

12

5

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,525

false

# bert-finetuned-ner-80percent This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5462 - Precision: 0.8116 - Recall: 0.8408 - F1: 0.8260 - Accuracy: 0.9238 ## 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: 8 - eval_batch_size: 8 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 60 | 0.5514 | 0.7966 | 0.8348 | 0.8152 | 0.9170 | | No log | 2.0 | 120 | 0.5718 | 0.8020 | 0.8333 | 0.8174 | 0.9184 | | No log | 3.0 | 180 | 0.5462 | 0.8116 | 0.8408 | 0.8260 | 0.9238 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

d005a1662dbc974eb9518fa07f78ef72

jonatasgrosman/exp_w2v2r_en_xls-r_gender_male-10_female-0_s287

jonatasgrosman

wav2vec2

10

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'en']

false

true

477

false

# exp_w2v2r_en_xls-r_gender_male-10_female-0_s287 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

acfc69349df5810b18802642226131c4

google/t5-efficient-small-nl8

google

t5

12

7

transformers

0

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,251

false

# T5-Efficient-SMALL-NL8 (Deep-Narrow version) T5-Efficient-SMALL-NL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-small-nl8** - is of model type **Small** with the following variations: - **nl** is **8** It has **75.21** million parameters and thus requires *ca.* **300.84 MB** of memory in full precision (*fp32*) or **150.42 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

a0cc0a3dca479e6c28936121e4b83f07

Helsinki-NLP/opus-mt-es-bzs

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-es-bzs * source languages: es * target languages: bzs * OPUS readme: [es-bzs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-bzs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-bzs/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-bzs/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-bzs/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.es.bzs | 26.4 | 0.451 |

5628ddbbcd2fcb3e5ebab076d15658e6

gunyoung/distilbert-base-uncased-finetuned-emotion

gunyoung

distilbert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,325

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2187 - Accuracy: 0.924 - F1: 0.9241 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8161 | 1.0 | 250 | 0.3112 | 0.9135 | 0.9102 | | 0.2468 | 2.0 | 500 | 0.2187 | 0.924 | 0.9241 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2

623b1697506f0ed2067216f5f9dac8be

AokiDaiki/distilbert-base-uncased-finetuned-emotion

AokiDaiki

distilbert

12

5

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,344

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2174 - Accuracy: 0.927 - F1: 0.9271 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8148 | 1.0 | 250 | 0.3148 | 0.9 | 0.8967 | | 0.2487 | 2.0 | 500 | 0.2174 | 0.927 | 0.9271 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

8a3877888be8cdb642ef6f975d54f686

avtanh/wav2vec2-large-xls-r-300m-vietnamese-cv11.0-colab

avtanh

wav2vec2

42

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice_11_0']

null

0

['generated_from_trainer']

true

1,685

false

# wav2vec2-large-xls-r-300m-vietnamese-cv11.0-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_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6392 - Wer: 0.4792 ## 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: 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 | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 10.0365 | 4.55 | 400 | 3.4508 | 0.9984 | | 2.5036 | 9.09 | 800 | 1.0268 | 0.6972 | | 0.5974 | 13.64 | 1200 | 0.7071 | 0.5492 | | 0.3221 | 18.18 | 1600 | 0.6401 | 0.5071 | | 0.2046 | 22.73 | 2000 | 0.6154 | 0.4871 | | 0.1445 | 27.27 | 2400 | 0.6392 | 0.4792 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 2.8.0 - Tokenizers 0.10.3

4c6e705bdfacd6710b4103baf0518df1

jonatasgrosman/exp_w2v2t_et_hubert_s390

jonatasgrosman

hubert

10

4

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['et']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'et']

false

true

452

false

# exp_w2v2t_et_hubert_s390 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

3378f65997425ff3be371c4076149b12

steja/whisper-large-shona

steja

whisper

11

0

transformers

1

automatic-speech-recognition

true

false

apache-2.0

null

['google/fleurs']

null

0

['whisper-event', 'generated_from_trainer']

true

1,446

false

# Whisper_large_Shona This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the google/fleurs sn_zw dataset. It achieves the following results on the evaluation set: - Loss: 0.9189 - Wer: 37.5 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0005 | 41.64 | 500 | 0.8784 | 37.525 | | 0.0003 | 83.32 | 1000 | 0.9189 | 37.5 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

de8b90d993d4748910bd15a5a9dcc8b4

kapilkd13/xls-r-300m-hi-prod

kapilkd13

wav2vec2

19

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['hi']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event']

true

2,444

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.7805 - Wer: 0.4340 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 8000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.36 | 400 | 1.9130 | 0.9244 | | 5.0013 | 2.71 | 800 | 0.7789 | 0.5944 | | 0.6544 | 4.07 | 1200 | 0.7298 | 0.5852 | | 0.4021 | 5.42 | 1600 | 0.6978 | 0.5667 | | 0.3003 | 6.78 | 2000 | 0.6764 | 0.5382 | | 0.3003 | 8.14 | 2400 | 0.7249 | 0.5463 | | 0.2345 | 9.49 | 2800 | 0.7280 | 0.5124 | | 0.1993 | 10.85 | 3200 | 0.7289 | 0.4690 | | 0.1617 | 12.2 | 3600 | 0.7431 | 0.4733 | | 0.1432 | 13.56 | 4000 | 0.7448 | 0.4733 | | 0.1432 | 14.92 | 4400 | 0.7746 | 0.4485 | | 0.1172 | 16.27 | 4800 | 0.7589 | 0.4742 | | 0.1035 | 17.63 | 5200 | 0.7539 | 0.4353 | | 0.0956 | 18.98 | 5600 | 0.7648 | 0.4495 | | 0.0845 | 20.34 | 6000 | 0.7877 | 0.4719 | | 0.0845 | 21.69 | 6400 | 0.7884 | 0.4434 | | 0.0761 | 23.05 | 6800 | 0.7796 | 0.4386 | | 0.0634 | 24.41 | 7200 | 0.7729 | 0.4306 | | 0.0571 | 25.76 | 7600 | 0.7826 | 0.4298 | | 0.0508 | 27.12 | 8000 | 0.7805 | 0.4340 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.1+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

3ddb9aa2cd0f4863d69f5b9bee71e492

carblacac/twitter-sentiment-analysis

carblacac

distilbert

14

1

transformers

0

text-classification

true

false

apache-2.0

null

['new_dataset']

null

0

['generated_from_trainer']

true

1,396

false

# sentiment-analysis-twitter This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the new_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.4579 - Accuracy: 0.7965 ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5315 | 1.0 | 157 | 0.4517 | 0.788 | | 0.388 | 2.0 | 314 | 0.4416 | 0.8 | | 0.3307 | 3.0 | 471 | 0.4579 | 0.7965 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.1.0 - Tokenizers 0.12.1

91d08f72b8f473ede08f84d59757f89c

nandysoham16/Warsaw_Pact-clustered

nandysoham16

distilbert

8

10

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,863

false

# nandysoham16/Warsaw_Pact-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0828 - Train End Logits Accuracy: 0.9792 - Train Start Logits Accuracy: 0.9826 - Validation Loss: 2.2175 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.0 - Epoch: 0 ## 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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.0828 | 0.9792 | 0.9826 | 2.2175 | 0.0 | 0.0 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

44dfc852ca733c71e0747295f84deedd

Wizounovziki/t5-small-devices-sum-ver2

Wizounovziki

t5

11

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,350

false

# t5-small-devices-sum-ver2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3679 - Rouge1: 90.6465 - Rouge2: 65.2833 - Rougel: 90.6707 - Rougelsum: 90.7313 - Gen Len: 4.4702 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 91 | 1.0957 | 58.9566 | 33.4113 | 58.8004 | 58.8863 | 4.8308 | | No log | 2.0 | 182 | 0.7017 | 78.9566 | 49.9716 | 78.9338 | 78.9643 | 4.3329 | | No log | 3.0 | 273 | 0.5386 | 84.8786 | 56.9622 | 84.8204 | 84.9117 | 4.4577 | | No log | 4.0 | 364 | 0.4693 | 87.9792 | 61.0779 | 87.8795 | 88.0098 | 4.4383 | | No log | 5.0 | 455 | 0.4273 | 89.4667 | 63.1994 | 89.4169 | 89.5197 | 4.4743 | | 1.0586 | 6.0 | 546 | 0.4002 | 89.6456 | 63.5041 | 89.6062 | 89.7042 | 4.4452 | | 1.0586 | 7.0 | 637 | 0.3848 | 89.9993 | 64.2505 | 89.9775 | 90.0651 | 4.423 | | 1.0586 | 8.0 | 728 | 0.3752 | 90.4249 | 64.819 | 90.4434 | 90.5111 | 4.4799 | | 1.0586 | 9.0 | 819 | 0.3703 | 90.4689 | 65.0086 | 90.4954 | 90.5632 | 4.4632 | | 1.0586 | 10.0 | 910 | 0.3679 | 90.6465 | 65.2833 | 90.6707 | 90.7313 | 4.4702 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

4726430561003e05159b71210b6c72c3

lucasgbezerra/classification_text_model

lucasgbezerra

distilbert

16

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,270

false

# classification_text_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2001 - Accuracy: 0.9334 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2056 | 1.0 | 1000 | 0.1771 | 0.9313 | | 0.1283 | 2.0 | 2000 | 0.2001 | 0.9334 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

1abab2cef288655de3b5f8fd36bd88c9

imjunaidafzal/saqib-14-dec

imjunaidafzal

null

15

4

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

1

0

['text-to-image', 'stable-diffusion']

false

true

620

false

### saqib_14_dec Dreambooth model trained by imjunaidafzal with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:

00d39f45ffce37f18b97d88af8051ccf

yanaiela/roberta-base-epoch_53

yanaiela

roberta

9

2

transformers

0

fill-mask

true

false

mit

['en']

['wikipedia', 'bookcorpus']

null

0

['roberta-base', 'roberta-base-epoch_53']

false

true

2,102

false

# RoBERTa, Intermediate Checkpoint - Epoch 53 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_53. ## Model Description This model was captured during a reproduction of [RoBERTa-base](https://huggingface.co/roberta-base), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM). The intended uses, limitations, training data and training procedure for the fully trained model are similar to [RoBERTa-base](https://huggingface.co/roberta-base). Two major differences with the original model: * We trained our model for 100K steps, instead of 500K * We only use Wikipedia and the Book Corpus, as corpora which are publicly available. ### How to use Using code from [RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on PyTorch: ``` from transformers import pipeline model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10) model("Hello, I'm the <mask> RoBERTa-base language model") ``` ## Citation info ```bibtex @article{2207.14251, Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg}, Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions}, Year = {2022}, Eprint = {arXiv:2207.14251}, } ```

89c5cd85d048531b4e63ea290d519f55

bondarchukb/minicooper

bondarchukb

null

18

2

diffusers

1

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image', 'stable-diffusion']

false

true

616

false

### minicooper Dreambooth model trained by bondarchukb with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:

3344b8a39e8ec6f835d68f9b6f51fee3

Helsinki-NLP/opus-mt-pa-en

Helsinki-NLP

marian

10

389

transformers

1

translation

true

false

apache-2.0

null

0

['translation']

false

true

803

false

### opus-mt-pa-en * source languages: pa * target languages: en * OPUS readme: [pa-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pa-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pa-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pa-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pa-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.pa.en | 20.6 | 0.320 | | Tatoeba.pa.en | 29.3 | 0.464 |

fbda15bb940e304eec1abf581d170bb0

ShussarSDFA/MitoAzX

ShussarSDFA

null

10

0

null

1

null

false

creativeml-openrail-m

null

0

[]

false

true

669

false

Just finetuned [DrBob2142's](https://huggingface.co/DrBob2142) [MidnightMix model](https://huggingface.co/DrBob2142/Mix-Models/blob/main/Midnight%20Mix.safetensors) Usable model Recipe: (Add Difference 1)MitoAzXEP62 + F222 + S.D. 1.4 = MitoMix (Weighted Sum 0.3) MitoMix + Blossom-extract = MitoExtract (Weighted Sum 0.4) MitoExtract + MitoAzXEP62 = MitoAzXMixedModel New mixes have about ~10 my finetuned models and ~6 "third-party" models like : Blossom extract, [Nuigurumi's](https://huggingface.co/nuigurumi) basil_mix, [WarriorMama777's](https://huggingface.co/WarriorMama777) AbyssOrangeMix2, ChinaBerry,[DrBob2142's](https://huggingface.co/DrBob2142) mixes

1b6b8ac501e78b230e8e493de7c0c3d0

gsarti/it5-small

gsarti

t5

12

120

transformers

1

text2text-generation

true

apache-2.0

['it']

['gsarti/clean_mc4_it']

null

0

['seq2seq', 'lm-head']

false

true

5,697

false

# Italian T5 Small 🇮🇹 The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-transfer-transformer). This model is released as part of the project ["IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation"](https://arxiv.org/abs/2203.03759), by [Gabriele Sarti](https://gsarti.com/) and [Malvina Nissim](https://malvinanissim.github.io/) with the support of [Huggingface](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) and with TPU usage sponsored by Google's [TPU Research Cloud](https://sites.research.google/trc/). All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process. *The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the [`it5`](https://huggingface.co/it5) organization provide some examples of this model fine-tuned on various downstream task.* ## Model variants This repository contains the checkpoints for the `base` version of the model. The model was trained for one epoch (1.05M steps) on the [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB) using 🤗 Datasets and the `google/t5-v1_1-small` improved configuration. The training procedure is made available [on Github](https://github.com/gsarti/t5-flax-gcp). The following table summarizes the parameters for all available models | |`it5-small` (this one) |`it5-base` |`it5-large` |`it5-base-oscar` | |-----------------------|-----------------------|----------------------|-----------------------|----------------------------------| |`dataset` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`oscar/unshuffled_deduplicated_it`| |`architecture` |`google/t5-v1_1-small` |`google/t5-v1_1-base` |`google/t5-v1_1-large` |`t5-base` | |`learning rate` | 5e-3 | 5e-3 | 5e-3 | 1e-2 | |`steps` | 1'050'000 | 1'050'000 | 2'100'000 | 258'000 | |`training time` | 36 hours | 101 hours | 370 hours | 98 hours | |`ff projection` |`gated-gelu` |`gated-gelu` |`gated-gelu` |`relu` | |`tie embeds` |`false` |`false` |`false` |`true` | |`optimizer` | adafactor | adafactor | adafactor | adafactor | |`max seq. length` | 512 | 512 | 512 | 512 | |`per-device batch size`| 16 | 16 | 8 | 16 | |`tot. batch size` | 128 | 128 | 64 | 128 | |`weigth decay` | 1e-3 | 1e-3 | 1e-2 | 1e-3 | |`validation split size`| 15K examples | 15K examples | 15K examples | 15K examples | The high training time of `it5-base-oscar` was due to [a bug](https://github.com/huggingface/transformers/pull/13012) in the training script. For a list of individual model parameters, refer to the `config.json` file in the respective repositories. ## Using the models ```python from transformers import AutoTokenzier, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-small") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-small") ``` *Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example [here](https://huggingface.co/it5/it5-base-question-answering).* Flax and Tensorflow versions of the model are also available: ```python from transformers import FlaxT5ForConditionalGeneration, TFT5ForConditionalGeneration model_flax = FlaxT5ForConditionalGeneration.from_pretrained("gsarti/it5-small") model_tf = TFT5ForConditionalGeneration.from_pretrained("gsarti/it5-small") ``` ## Limitations Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors. ## Model curators For problems or updates on this model, please contact [[email protected]](mailto:[email protected]). ## Citation Information ```bibtex @article{sarti-nissim-2022-it5, title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ```

406ec9332d32914e0d56a0e1504f0d7f

kevinbram/testarbaraz

kevinbram

distilbert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,143

false

# testarbaraz This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2153 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.2806 | 1.0 | 5533 | 1.2153 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

d728f020ba8d10bc231fa811a7ef909d

arrafmousa/SimQA-roberta-base

arrafmousa

roberta

9

5

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,294

false

# SimQA-roberta-base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1454 - Epoch: 2 ## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 597, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 0.7101 | 0 | | 0.1836 | 1 | | 0.1454 | 2 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

a040439d4a4ae8dc9eccc97efeec76e9

peterhsu/tf-bert-finetuned-squad

peterhsu

bert

8

5

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,334

false

# tf-bert-finetuned-squad This model is a fine-tuned version of [peterhsu/tf-bert-finetuned-squad](https://huggingface.co/peterhsu/tf-bert-finetuned-squad) 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results ### Framework versions - Transformers 4.17.0 - TensorFlow 2.8.0 - Datasets 2.0.0 - Tokenizers 0.11.6

beef9d0beed8e8623d935af346357a10

Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa

Intel

distilbert

9

30

transformers

2

fill-mask

true

false

apache-2.0

['en']

null

0

[]

false

true

427

false

# 90% Sparse DistilBERT-Base (uncased) Prune OFA This model is a result from our paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021. For further details on the model and its result, see our paper and our implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).

651bbf218cfc6ce32509385dbaf9cf54

Ussen/whisper-medium-finetuned-on-fleurs-ln_cd1

Ussen

whisper

15

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,572

false

# whisper-medium-finetuned-on-fleurs-ln_cd1 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the "google/fleurs" "ln_cd" subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4483 - Wer: 14.7079 ## 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0528 | 4.78 | 1000 | 0.3612 | 17.4812 | | 0.0013 | 9.57 | 2000 | 0.4214 | 15.7308 | | 0.0003 | 14.35 | 3000 | 0.4423 | 14.8670 | | 0.0002 | 19.14 | 4000 | 0.4483 | 14.7079 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.8.0 - Tokenizers 0.13.2

dfedf7ce2154e35463f780b422136b9b

facebook/wav2vec2-xls-r-2b-en-to-15

facebook

speech-encoder-decoder

9

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['multilingual', 'en', 'de', 'tr', 'fa', 'sv', 'mn', 'zh', 'cy', 'ca', 'sl', 'et', 'id', 'ar', 'ta', 'lv', 'ja']

['common_voice', 'multilingual_librispeech', 'covost2']

null

1

0

1

0

['speech', 'xls_r', 'automatic-speech-recognition', 'xls_r_translation']

false

true

4,400

false

# Wav2Vec2-XLS-R-2B-EN-15 Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.** ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png) This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model. The encoder was warm-started from the [**`facebook/wav2vec2-xls-r-2b`**](https://huggingface.co/facebook/wav2vec2-xls-r-2b) checkpoint and the decoder from the [**`facebook/mbart-large-50`**](https://huggingface.co/facebook/mbart-large-50) checkpoint. Consequently, the encoder-decoder model was fine-tuned on 15 `en` -> `{lang}` translation pairs of the [Covost2 dataset](https://huggingface.co/datasets/covost2). The model can translate from spoken `en` (Engish) to the following written languages `{lang}`: `en` -> {`de`, `tr`, `fa`, `sv-SE`, `mn`, `zh-CN`, `cy`, `ca`, `sl`, `et`, `id`, `ar`, `ta`, `lv`, `ja`} For more information, please refer to Section *5.1.1* of the [official XLS-R paper](https://arxiv.org/abs/2111.09296). ## Usage ### Demo The model can be tested on [**this space**](https://huggingface.co/spaces/facebook/XLS-R-2B-EN-15). You can select the target language, record some audio in English, and then sit back and see how well the checkpoint can translate the input. ### Example As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. You can use the model directly via the ASR pipeline. By default, the checkpoint will translate spoken English to written German. To change the written target language, you need to pass the correct `forced_bos_token_id` to `generate(...)` to condition the decoder on the correct target language. To select the correct `forced_bos_token_id` given your choosen language id, please make use of the following mapping: ```python MAPPING = { "de": 250003, "tr": 250023, "fa": 250029, "sv": 250042, "mn": 250037, "zh": 250025, "cy": 250007, "ca": 250005, "sl": 250052, "et": 250006, "id": 250032, "ar": 250001, "ta": 250044, "lv": 250017, "ja": 250012, } ``` As an example, if you would like to translate to Swedish, you can do the following: ```python from datasets import load_dataset from transformers import pipeline # select correct `forced_bos_token_id` forced_bos_token_id = MAPPING["sv"] # replace following lines to load an audio file of your choice librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") audio_file = librispeech_en[0]["file"] asr = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-xls-r-2b-en-to-15", feature_extractor="facebook/wav2vec2-xls-r-2b-en-to-15") translation = asr(audio_file, forced_bos_token_id=forced_bos_token_id) ``` or step-by-step as follows: ```python import torch from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel from datasets import load_dataset model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-2b-en-to-15") processor = Speech2Text2Processor.from_pretrained("facebook/wav2vec2-xls-r-2b-en-to-15") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # select correct `forced_bos_token_id` forced_bos_token_id = MAPPING["sv"] inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["array"]["sampling_rate"], return_tensors="pt") generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"], forced_bos_token_id=forced_bos_token) transcription = processor.batch_decode(generated_ids) ``` ## Results `en` -> `{lang}` See the row of **XLS-R (2B)** for the performance on [Covost2](https://huggingface.co/datasets/covost2) for this model. ![results image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/English-%3EX.png) ## More XLS-R models for `{lang}` -> `en` Speech Translation - [Wav2Vec2-XLS-R-300M-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-300m-en-to-15) - [Wav2Vec2-XLS-R-1B-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-1b-en-to-15) - [Wav2Vec2-XLS-R-2B-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-2b-en-to-15) - [Wav2Vec2-XLS-R-2B-22-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16)

4cfae72bf49f3dbbfe96d07a3cf52dcc

alibaba-pai/pai-ckbert-base-zh

alibaba-pai

bert

5

3

transformers

1

fill-mask

true

false

apache-2.0

['zh']

null

0

['bert']

false

true

1,851

false

## Chinese Kowledge-enhanced BERT (CKBERT) Knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis. Unlike English, there is a lack of high-performing open-source Chinese KEPLMs in the natural language processing (NLP) community to support various language understanding applications. For Chinese natural language processing, we provide three **Chinese Kowledge-enhanced BERT (CKBERT)** models named **pai-ckbert-bert-zh**, **pai-ckbert-large-zh** and **pai-ckbert-huge-zh**, from our **EMNLP 2022** paper named **Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training**. This repository is developed based on the EasyNLP framework: [https://github.com/alibaba/EasyNLP](https://github.com/alibaba/EasyNLP ) ## Citation If you find the resource is useful, please cite the following papers in your work. - For the EasyNLP framework: ``` @article{easynlp, title = {EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing}, author = {Wang, Chengyu and Qiu, Minghui and Zhang, Taolin and Liu, Tingting and Li, Lei and Wang, Jianing and Wang, Ming and Huang, Jun and Lin, Wei}, publisher = {arXiv}, url = {https://arxiv.org/abs/2205.00258}, year = {2022} } ``` - For CKBERT: ``` @article{ckbert, title = {Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training}, author = {Zhang, Taolin and Dong, Junwei and Wang, Jianing and Wang, Chengyu and Wang, An and Liu, Yinghui and Huang, Jun and Li, Yong and He, Xiaofeng}, publisher = {EMNLP}, url = {https://arxiv.org/abs/2210.05287}, year = {2022} } ```

66adad4d909ddecca3c1dba75ad43ccf

fathyshalab/massive_play-roberta-large-v1-2-0.64

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,462

false

# fathyshalab/massive_play-roberta-large-v1-2-0.64 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_play-roberta-large-v1-2-0.64") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

dd87ebfdb40fca60a98a5d63bb2a344f

rifkat/uztext-3Gb-BPE-Roberta

rifkat

roberta

7

transformers

3

fill-mask

true

false

apache-2.0

['uz']

null

0

['transformers', 'mit', 'robert', 'uzrobert', 'uzbek', 'cyrillic', 'latin']

false

true

2,959

false

UzRoBerta model. Pre-prepared model in Uzbek (Cyrillic and latin script) to model the masked language and predict the next sentences. How to use. You can use this model directly with a pipeline for masked language modeling: <pre><code class="language-python"> from transformers import pipeline unmasker = pipeline('fill-mask', model='rifkat/uztext-3Gb-BPE-Roberta') unmasker("Алишер Навоий – улуғ ўзбек ва бошқа туркий халқларнинг [mask], мутафаккири ва давлат арбоби бўлган.") [{'score': 0.5902208685874939, 'sequence': 'Алишер Навоий – улуғ ўзбек ва бошқа туркий халқларнинг шоири, мутафаккири ва давлат арбоби бўлган.', 'token': 28809, 'token_str': ' шоири'}, {'score': 0.08303504437208176, 'sequence': 'Алишер Навоий – улуғ ўзбек ва бошқа туркий халқларнинг устози, мутафаккири ва давлат арбоби бўлган.', 'token': 17484, 'token_str': ' устози'}, {'score': 0.035882771015167236, 'sequence': 'Алишер Навоий – улуғ ўзбек ва бошқа туркий халқларнинг арбоби, мутафаккири ва давлат арбоби бўлган.', 'token': 34552, 'token_str': ' арбоби'}, {'score': 0.03447483479976654, 'sequence': 'Алишер Навоий – улуғ ўзбек ва бошқа туркий халқларнинг асосчиси, мутафаккири ва давлат арбоби бўлган.', 'token': 14034, 'token_str': ' асосчиси'}, {'score': 0.03044942207634449, 'sequence': 'Алишер Навоий – улуғ ўзбек ва бошқа туркий халқларнинг дўсти, мутафаккири ва давлат арбоби бўлган.', 'token': 28100, 'token_str': ' дўсти'}] unmasker("Kuchli yomg‘irlar tufayli bir qator [mask] kuchli sel oqishi kuzatildi.") [{'score': 0.410250186920166, 'sequence': 'Kuchli yomg‘irlar tufayli bir qator hududlarda kuchli sel oqishi kuzatildi.', 'token': 11009, 'token_str': ' hududlarda'}, {'score': 0.2023029774427414, 'sequence': 'Kuchli yomg‘irlar tufayli bir qator tumanlarda kuchli sel oqishi kuzatildi.', 'token': 35370, 'token_str': ' tumanlarda'}, {'score': 0.129830002784729, 'sequence': 'Kuchli yomg‘irlar tufayli bir qator viloyatlarda kuchli sel oqishi kuzatildi.', 'token': 33584, 'token_str': ' viloyatlarda'}, {'score': 0.04539087787270546, 'sequence': 'Kuchli yomg‘irlar tufayli bir qator mamlakatlarda kuchli sel oqishi kuzatildi.', 'token': 19315, 'token_str': ' mamlakatlarda'}, {'score': 0.0369882769882679, 'sequence': 'Kuchli yomg‘irlar tufayli bir qator joylarda kuchli sel oqishi kuzatildi.', 'token': 5853, 'token_str': ' joylarda'}] </code></pre> Training data. UzBERT model was pretrained on ≈2M news articles (≈3Gb). <pre><code class="language-python"> @misc {rifkat_davronov_2022, author = { {Adilova Fatima,Rifkat Davronov, Samariddin Kushmuratov, Ruzmat Safarov} }, title = { uztext-3Gb-BPE-Roberta (Revision 0c87494) }, year = 2022, url = { https://huggingface.co/rifkat/uztext-3Gb-BPE-Roberta }, doi = { 10.57967/hf/0140 }, publisher = { Hugging Face } } </code></pre>

1167a1d814f61251ec6c496e55256ff9

ravinduj/finetuning-sentiment-model-3000-samples

ravinduj

distilbert

13

11

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,055

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3489 - Accuracy: 0.8533 - F1: 0.8543 ## 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: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

8524958b0401a7dd8eed637e5a16db7f

transformersbook/xlm-roberta-base-finetuned-panx-fr

transformersbook

xlm-roberta

11

13

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,676

false

# xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/04_multilingual-ner.ipynb). It achieves the following results on the evaluation set: - Loss: 0.2772 - F1: 0.8455 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.562 | 1.0 | 191 | 0.3183 | 0.7828 | | 0.2697 | 2.0 | 382 | 0.2706 | 0.8324 | | 0.1735 | 3.0 | 573 | 0.2772 | 0.8455 | ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

e1b15b6bf1acde548deea3c11407a385

cometrain/neurotitle-rugpt3-small

cometrain

gpt2

9

5

transformers

1

text-generation

true

false

mit

['ru', 'en']

['All-NeurIPS-Papers-Scraper']

null

1

0

['Cometrain AutoCode', 'Cometrain AlphaML']

false

true

819

false

# neurotitle-rugpt3-small Model based on [ruGPT-3](https://huggingface.co/sberbank-ai) for generating scientific paper titles. Trained on [All NeurIPS (NIPS) Papers](https://www.kaggle.com/rowhitswami/nips-papers-1987-2019-updated) dataset. Use exclusively as a crazier alternative to SCIgen. ## Made with Cometrain AlphaML & AutoCode This model was automatically fine-tuned using the Cometrain AlphaML framework and tested with CI/CD pipeline made by Cometrain AutoCode ## Cometrain AlphaML command ```shell $ cometrain create --name neurotitle --model auto --task task_0x2231.txt --output transformers ``` ## Use with Transformers ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model="CometrainResearch/neurotitle-rugpt3-small") generator("BERT:", max_length=50) ```

86590bebf25927e54dd2c66b27592543

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_stsb_192

gokuls

distilbert

17

2

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

2,156

false

# distilbert_sa_GLUE_Experiment_logit_kd_stsb_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1279 - Pearson: nan - Spearmanr: nan - Combined Score: nan ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 3.3853 | 1.0 | 23 | 1.9990 | -0.0411 | -0.0438 | -0.0425 | | 2.183 | 2.0 | 46 | 1.5416 | -0.0346 | -0.0339 | -0.0343 | | 1.6692 | 3.0 | 69 | 1.2526 | -0.1157 | -0.1181 | -0.1169 | | 1.3094 | 4.0 | 92 | 1.1279 | nan | nan | nan | | 1.1238 | 5.0 | 115 | 1.1817 | 0.0181 | 0.0180 | 0.0181 | | 1.0934 | 6.0 | 138 | 1.1718 | 0.0580 | 0.0536 | 0.0558 | | 1.0784 | 7.0 | 161 | 1.1594 | 0.0592 | 0.0625 | 0.0609 | | 1.0191 | 8.0 | 184 | 1.2390 | 0.0613 | 0.0770 | 0.0692 | | 0.9587 | 9.0 | 207 | 1.2917 | 0.0993 | 0.1113 | 0.1053 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

20ac04b753a3851aeb0148bdd5dc9065

FluxML/wideresnet101

FluxML

null

3

0

null

0

null

false

mit

null

2

0

2

0

[]

false

true

527

false

WideResNet101 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef). To use this model in Julia, [add the Metalhead.jl package to your environment](https://pkgdocs.julialang.org/v1/managing-packages/#Adding-packages). Then execute: ```julia using Metalhead model = WideResNet(101; pretrain = true) ```

e51fa7166cda055fd51e9353799f03a4

samiulhaq/iwslt-bt-en-ur

samiulhaq

null

5

0

fairseq

0

translation

false

apache-2.0

['en', 'ur']

['iwslt14']

null

0

[]

false

true

1,374

false

### English to Urdu Translation English to Urdu translation model is a Transformer model trained on IWSLT back-translated data using Faireq. This model is produced during the experimentation related to building Context-Aware NMT models for low-resourced languages such as Urdu, Hindi, Sindhi, Pashtu and Punjabi. This particular model does not contains any contextual information and it is baseline sentence-level transformer model. The evaluation is done on WMT2017 standard test set. * source group: English * target group: Urdu * model: transformer * Contextual * Test Set: WMT2017 * pre-processing: Moses + Indic Tokenizer * Dataset + Libray Details: [DLNMT](https://github.com/sami-haq99/nrpu-dlnmt) ## Benchmarks | testset | BLEU | |-----------------------|-------| | Wmt2017 | 57.95 | ## How to use model? * This model can be accessed via git clone: ``` git clone https://huggingface.co/samiulhaq/iwslt-bt-en-ur ``` * You can use Fairseq library to access the model for translations: ``` from fairseq.models.transformer import TransformerModel ``` ### Load the model ``` model = TransformerModel.from_pretrained('path/to/model') ``` #### Set the model to evaluation mode ``` model.eval() ``` #### Perform inference ``` input_text = 'Hello, how are you?' output_text = model.translate(input_text) print(output_text) ```

3efbf90e714cc51fe4615aa9bac0148a

icelab/spaceroberta

icelab

roberta

12

106

transformers

0

fill-mask

true

false

mit

['en']

null

0

[]

false

true

973

false

### SpaceRoBERTa This is one of the 3 further pre-trained models from the SpaceTransformers family presented in [SpaceTransformers: Language Modeling for Space Systems](https://ieeexplore.ieee.org/document/9548078). The original Git repo is [strath-ace/smart-nlp](https://github.com/strath-ace/smart-nlp). The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to space systems. Corpus size is 14.3 GB. SpaceRoBERTa was further pre-trained on this domain-specific corpus from [RoBERTa-Base](https://huggingface.co/roberta-base). In our paper, it is then fine-tuned for a Concept Recognition task. ### BibTeX entry and citation info ``` @ARTICLE{ 9548078, author={Berquand, Audrey and Darm, Paul and Riccardi, Annalisa}, journal={IEEE Access}, title={SpaceTransformers: Language Modeling for Space Systems}, year={2021}, volume={9}, number={}, pages={133111-133122}, doi={10.1109/ACCESS.2021.3115659} } ```

bba25517099f5ed432afc43c5642c6ec

adache/tf-distilbert-base-uncased-finetuned-emotion

adache

distilbert

4

6

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

973

false

# tf-distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Tokenizers 0.11.6

8293d0071853a24d2f8f60131347ff94

Eleven/distilbert-base-uncased-finetuned-emotion

Eleven

distilbert

14

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,326

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2263 - Accuracy: 0.9225 - F1: 0.9221 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8571 | 1.0 | 250 | 0.3333 | 0.902 | 0.8982 | | 0.2507 | 2.0 | 500 | 0.2263 | 0.9225 | 0.9221 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Tokenizers 0.12.1

e6e7d7b1552c97a469f390a3a546a216

speechbrain/sepformer-wham

speechbrain

null

14

216

speechbrain

7

audio-to-audio

false

apache-2.0

['en']

['WHAM!']

null

0

['audio-to-audio', 'audio-source-separation', 'Source Separation', 'Speech Separation', 'Audio Source Separation', 'WHAM!', 'SepFormer', 'Transformer', 'speechbrain']

false

true

3,794

false

7d676ca81b8469aa5b1ad8f820719aef

Jungwonchang/wav2vec2-large-xls-r-300m-vietnamese-colab

Jungwonchang

wav2vec2

13

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,108

false

# wav2vec2-large-xls-r-300m-vietnamese-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 1.18.3 - Tokenizers 0.10.3

86a348d2732b10b7fb3d885b6ac55b11

inverse-scaling/opt-66b_eval

inverse-scaling

opt

53

3

transformers

0

text-generation

true

other

['en']

null

14

4

5

0

['text-generation', 'opt']

true

9,908

false

# OPT : Open Pre-trained Transformer Language Models OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf). Content from **this** model card has been written by the Hugging Face team. ## Intro To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068) > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity. > We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M > to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match > the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data > collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and > to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the > collective research community as a whole, which is only possible when models are available for study. ## Model description OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective. For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read the [official paper](https://arxiv.org/abs/2205.01068). ## Intended uses & limitations The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation. In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt). ### How to use For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU. It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate) method as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b", use_fast=False) >>> prompt = "Hello, I am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> generated_ids = model.generate(input_ids) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['Hello, I am conscious and I am here.\nI am also conscious and I am here'] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b", use_fast=False) >>> prompt = "Hello, I am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['Hello, I am conscious and aware that you have your back turned to me and want to talk'] ``` ### Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. Here's an example of how the model can have biased predictions: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b", use_fast=False) >>> prompt = "The woman worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) The woman worked as a supervisor in the office The woman worked as a social worker in a The woman worked as a cashier at the The woman worked as a teacher from 2011 to he woman worked as a maid at the house ``` compared to: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b", use_fast=False) >>> prompt = "The man worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) The man worked as a school bus driver for The man worked as a bartender in a bar The man worked as a cashier at the The man worked as a teacher, and was The man worked as a professional at a range ``` This bias will also affect all fine-tuned versions of this model. ## Training data The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: - BookCorpus, which consists of more than 10K unpublished books, - CC-Stories, which contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas, - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. - Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in Roller et al. (2021) - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News dataset that was used in RoBERTa (Liu et al., 2019b) The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally to each dataset’s size in the pretraining corpus. The dataset might contains offensive content as parts of the dataset are a subset of public Common Crawl data, along with a subset of public Reddit data, which could contain sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety. ### Collection process The dataset was collected form internet, and went through classic data processing algorithms and re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or *This ebook by Project Gutenberg.* ## Training procedure ### Preprocessing The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training. ### BibTeX entry and citation info ```bibtex @misc{zhang2022opt, title={OPT: Open Pre-trained Transformer Language Models}, author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer}, year={2022}, eprint={2205.01068}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

53834aa35d3436f0f4f3cee27b530468

Ktolodozo/Beau

Ktolodozo

null

2

0

null

0

null

false

openrail

null

0

[]

false

true

1,432

false

pip install --upgrade diffusers transformers scipy huggingface-cli login import torch from torch import autocast from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True) pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" with autocast("cuda"): image = pipe(prompt, guidance_scale=7.5).images[0] image.save("astronaut_rides_horse.png") import torch pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=True) pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" with autocast("cuda"): image = pipe(prompt, guidance_scale=7.5).images[0] image.save("astronaut_rides_horse.png") from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler model_id = "CompVis/stable-diffusion-v1-4" # Use the K-LMS scheduler here instead scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000) pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" with autocast("cuda"): image = pipe(prompt, guidance_scale=7.5).images[0] image.save("astronaut_rides_horse.png")

ed5d8331f7cd4c2a256a90833615620c

anmol-chawla/animecharacters1

anmol-chawla

null

15

50

diffusers

1

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image', 'stable-diffusion']

false

true

623

false

### animecharacters1 Dreambooth model trained by anmol-chawla with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:

cf5ba08195c757b86df582e38272ac27

clhuang/albert-sentiment

clhuang

bert

7

39

transformers

0

text-classification

true

false

afl-3.0

['tw']

null

0

['albert', 'classification']

false

true

1,102

false

# 繁體中文情緒分類: 負面(0)、正面(1) 依據ckiplab/albert預訓練模型微調，訓練資料集只有8萬筆，做為課程的範例模型。 # 使用範例: from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clhuang/albert-sentiment") model = AutoModelForSequenceClassification.from_pretrained("clhuang/albert-sentiment") ## Pediction target_names=['Negative','Positive'] max_length = 200 # 最多字數若超出模型訓練時的字數，以模型最大字數為依據 def get_sentiment_proba(text): # prepare our text into tokenized sequence inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt") # perform inference to our model outputs = model(**inputs) # get output probabilities by doing softmax probs = outputs[0].softmax(1) response = {'Negative': round(float(probs[0, 0]), 2), 'Positive': round(float(probs[0, 1]), 2)} # executing argmax function to get the candidate label #return probs.argmax() return response get_sentiment_proba('我喜歡這本書') get_sentiment_proba('不喜歡這款產品')

e78cdfea809d46d6a371dced57054789

jEVVB/dillyg

jEVVB

null

23

4

diffusers

0

null

false

mit

null

2

0

[]

false

true

1,255

false

### DillyG on Stable Diffusion via Dreambooth #### model by jEVVB This your the Stable Diffusion model fine-tuned the DillyG concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks man** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/jEVVB/dillyg/resolve/main/concept_images/0.jpeg) ![image 1](https://huggingface.co/jEVVB/dillyg/resolve/main/concept_images/2.jpeg) ![image 2](https://huggingface.co/jEVVB/dillyg/resolve/main/concept_images/3.jpeg) ![image 3](https://huggingface.co/jEVVB/dillyg/resolve/main/concept_images/4.jpeg) ![image 4](https://huggingface.co/jEVVB/dillyg/resolve/main/concept_images/1.jpeg)

fa1c4d00b7434cc154fbea30cfd0fea6

Eto-Demerzel/core

Eto-Demerzel

null

18

7

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image', 'stable-diffusion']

false

true

418

false

### Core Dreambooth model trained by Eto-Demerzel with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

1c72a407ca2b248a17c7db3f5ab65b11

fathyshalab/bert-uncased-massive-intent-classification-banking-1

fathyshalab

bert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,287

false

# bert-uncased-massive-intent-classification-banking-1 This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7010 - Accuracy: 0.1289 ## 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: 6 - eval_batch_size: 6 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6675 | 1.0 | 3 | 2.7010 | 0.1289 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

52dbc6fcd589f67acd3ec0f260992f1f

lmqg/mt5-small-ruquad-ae

lmqg

mt5

13

33

transformers

0

text2text-generation

true

false

cc-by-4.0

['ru']

['lmqg/qg_ruquad']

null

0

['answer extraction']

true

4,781

false

# Model Card of `lmqg/mt5-small-ruquad-ae` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for answer extraction on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** ru - **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="ru", model="lmqg/mt5-small-ruquad-ae") # model prediction answers = model.generate_a("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-ruquad-ae") output = pipe("<hl> в английском языке в нарицательном смысле применяется термин rapid transit (скоростной городской транспорт), однако употребляется он только тогда, когда по смыслу невозможно ограничиться названием одной конкретной системы метрополитена. <hl> в остальных случаях используются индивидуальные названия: в лондоне — london underground, в нью-йорке — new york subway, в ливерпуле — merseyrail, в вашингтоне — washington metrorail, в сан-франциско — bart и т. п. в некоторых городах применяется название метро (англ. metro) для систем, по своему характеру близких к метро, или для всего городского транспорта (собственно метро и наземный пассажирский транспорт (в том числе автобусы и трамваи)) в совокупности.") ``` ## Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 33 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | AnswerF1Score | 56.62 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | BERTScore | 80.96 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_1 | 28.5 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_2 | 24.12 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_3 | 20.13 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_4 | 16.37 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | METEOR | 34.93 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | MoverScore | 68.52 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | ROUGE_L | 44.12 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 5 - batch: 32 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-ruquad-ae/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

d9e7c45da6cf3806479f6d0566a4d6c4

juancopi81/mt5-small-finetuned-amazon-en-es

juancopi81

mt5

8

1

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,645

false

# juancopi81/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1238 - Validation Loss: 3.4046 - Epoch: 7 ## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2166 | 4.4331 | 0 | | 6.0386 | 3.8849 | 1 | | 5.2369 | 3.6628 | 2 | | 4.7882 | 3.5569 | 3 | | 4.5111 | 3.4850 | 4 | | 4.3250 | 3.4330 | 5 | | 4.1930 | 3.4163 | 6 | | 4.1238 | 3.4046 | 7 | ### Framework versions - Transformers 4.19.3 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1

0067fdd4b5adb6ebd04b4e8916d2fdf9

mrizalf7/indobert-qa-finetuned-small-squad-indonesian-rizal

mrizalf7

bert

24

4

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,355

false

# indobert-finetuned-small-squad-indonesian-rizal This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the small-squad indonesian dataset. It achieves the following results on the evaluation set: - Loss: 2.3344 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2921 | 1.0 | 2700 | 2.1491 | | 1.0084 | 2.0 | 5400 | 2.1961 | | 0.814 | 3.0 | 8100 | 2.3344 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

7bd5cd4add89492baafa410541024bfc

sd-dreambooth-library/mertgunhan

sd-dreambooth-library

null

35

9

diffusers

0

null

false

mit

null

2

0

[]

false

true

3,074

false

### mertgunhan on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook #### model by teragron This your the Stable Diffusion model fine-tuned the mertgunhan concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **mertgunhan** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: mertgunhan ![mertgunhan 0](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(1).png) ![mertgunhan 1](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(2).png) ![mertgunhan 2](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(3).png) ![mertgunhan 3](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(4).png) ![mertgunhan 4](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(5).png) ![mertgunhan 5](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(6).png) ![mertgunhan 6](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(7).png) ![mertgunhan 7](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(8).png) ![mertgunhan 8](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(9).png) ![mertgunhan 9](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(10).png) ![mertgunhan 10](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(11).png) ![mertgunhan 11](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(12).png) ![mertgunhan 12](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(13).png) ![mertgunhan 13](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(14).png) ![mertgunhan 14](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(15).png) ![mertgunhan 15](https://huggingface.co/sd-dreambooth-library/mertgunhan/resolve/main/concept_images/mertgunhan_(16).png)

33056975faea85d3c016cf1ab7590ed5

freedomtw/stable_diffusion_tflite

freedomtw

null

13

0

null

0

null

false

openrail

null

0

['tflite', 'stable_diffusion']

false

true

1,045

false

# Model Card for Model ID  Stable Diffusion TFLite models # Model Details converted from [Keras CV Stable Diffusion](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion) ## Model Description  - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s) (NLP):** English - **License:** The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based. ## Model Sources  - **conversion script:** https://github.com/freedomtan/keras_cv_stable_diffusion_to_tflite - **converted from:** https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion

6dd5ae0f80d809d34b2cc2b7a872318d

tmobaggins/marian-finetuned-kde4-en-to-es

tmobaggins

marian

15

3

transformers

0

translation

true

false

apache-2.0

null

['kde4']

null

0

['translation', 'generated_from_trainer']

true

987

false

# marian-finetuned-kde4-en-to-es This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) on the kde4 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: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

96128110c7f5b55917d71434cb48556d

Helsinki-NLP/opus-mt-bzs-fr

Helsinki-NLP

marian

10

9

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-bzs-fr * source languages: bzs * target languages: fr * OPUS readme: [bzs-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fr/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fr/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-fr/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.bzs.fr | 30.0 | 0.479 |

e6749702aae9923e2c363f019f47a8b4

jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-8_sixties-2_s130

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'es']

false

true

497

false

# exp_w2v2r_es_vp-100k_age_teens-8_sixties-2_s130 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

a0570e11ab6a617213ca0518e9f0960d

MrPotato/ner-bert-multilingual-uncased-geocite

MrPotato

bert

12

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

997

false

# ner-bert-multilingual-uncased-geocite This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) 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: 16 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

6b19250876c982ff49535f5f05f118a5

espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend-truncated-55c091

espnet

null

31

0

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['en']

['librispeech']

null

0

['espnet', 'audio', 'automatic-speech-recognition']

false

true

1,983

false

## Example ESPnet2 ASR model ### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave` ♻️ Imported from https://zenodo.org/record/4543003/ This model was trained by kamo-naoyuki using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

1e4ee85e628a444c8768897dc7cded4b

Helsinki-NLP/opus-mt-it-lt

Helsinki-NLP

marian

11

14

transformers

0

translation

true

false

apache-2.0

['it', 'lt']

null

1

0

['translation']

false

true

2,004

false

### ita-lit * source group: Italian * target group: Lithuanian * OPUS readme: [ita-lit](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-lit/README.md) * model: transformer-align * source language(s): ita * target language(s): lit * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ita.lit | 38.1 | 0.652 | ### System Info: - hf_name: ita-lit - source_languages: ita - target_languages: lit - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-lit/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'lt'] - src_constituents: {'ita'} - tgt_constituents: {'lit'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.test.txt - src_alpha3: ita - tgt_alpha3: lit - short_pair: it-lt - chrF2_score: 0.652 - bleu: 38.1 - brevity_penalty: 0.9590000000000001 - ref_len: 1321.0 - src_name: Italian - tgt_name: Lithuanian - train_date: 2020-06-17 - src_alpha2: it - tgt_alpha2: lt - prefer_old: False - long_pair: ita-lit - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

206f48917be024ba438fb7fc8b1310d7

vvincentt/roberta-base-squad2

vvincentt

bert

12

4

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

952

false

# roberta-base-squad2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

86abf34a29980f2220aa5ecfd70b273a