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End of training

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: FacebookAI/roberta-large
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: roberta-large-zeroshot-v2.0-2024-03-22-14-58
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # roberta-large-zeroshot-v2.0-2024-03-22-14-58
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+
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+ This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1357
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+ - F1 Macro: 0.5228
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+ - F1 Micro: 0.5294
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+ - Accuracy Balanced: 0.5577
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+ - Accuracy: 0.5294
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+ - Precision Macro: 0.6624
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+ - Recall Macro: 0.5577
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+ - Precision Micro: 0.5294
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+ - Recall Micro: 0.5294
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 9e-06
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+ - train_batch_size: 4
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.06
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+ - num_epochs: 2
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
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+ | 0.2319 | 1.0 | 27331 | 0.6148 | 0.7746 | 0.7904 | 0.7810 | 0.7904 | 0.7704 | 0.7810 | 0.7904 | 0.7904 |
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+ | 0.1646 | 2.0 | 54662 | 0.6324 | 0.7822 | 0.7999 | 0.7847 | 0.7999 | 0.7800 | 0.7847 | 0.7999 | 0.7999 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.2
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