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--- |
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library_name: transformers |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: pretrain_model |
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results: [] |
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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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# pretrain_model |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6196 |
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- Precision: 0.6607 |
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- Recall: 0.6589 |
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- F1: 0.6598 |
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- Accuracy: 0.6575 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.6965 | 0.1377 | 500 | 0.6910 | 0.526 | 1.0 | 0.6894 | 0.526 | |
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| 0.6963 | 0.2755 | 1000 | 0.6921 | 0.526 | 1.0 | 0.6894 | 0.526 | |
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| 0.6957 | 0.4132 | 1500 | 0.6666 | 0.6154 | 0.7300 | 0.6678 | 0.618 | |
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| 0.6914 | 0.5510 | 2000 | 0.6834 | 0.7069 | 0.4677 | 0.5629 | 0.618 | |
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| 0.6768 | 0.6887 | 2500 | 0.6838 | 0.6412 | 0.6388 | 0.64 | 0.622 | |
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| 0.6786 | 0.8264 | 3000 | 0.6539 | 0.7273 | 0.4259 | 0.5372 | 0.614 | |
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| 0.663 | 0.9642 | 3500 | 0.6743 | 0.6560 | 0.5437 | 0.5946 | 0.61 | |
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| 0.6564 | 1.1019 | 4000 | 0.6381 | 0.6763 | 0.6198 | 0.6468 | 0.644 | |
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| 0.6468 | 1.2397 | 4500 | 0.6010 | 0.6613 | 0.7871 | 0.7188 | 0.676 | |
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| 0.6275 | 1.3774 | 5000 | 0.6103 | 0.7246 | 0.5703 | 0.6383 | 0.66 | |
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| 0.6275 | 1.5152 | 5500 | 0.6018 | 0.7311 | 0.5894 | 0.6526 | 0.67 | |
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| 0.6141 | 1.6529 | 6000 | 0.5947 | 0.7269 | 0.6578 | 0.6906 | 0.69 | |
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| 0.617 | 1.7906 | 6500 | 0.5872 | 0.7165 | 0.6920 | 0.7041 | 0.694 | |
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| 0.6059 | 1.9284 | 7000 | 0.5816 | 0.7227 | 0.7034 | 0.7129 | 0.702 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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