| --- |
| license: cc-by-sa-4.0 |
| base_model: nlpaueb/bert-base-uncased-contracts |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: clause_model |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # clause_model |
| |
| This model is a fine-tuned version of [nlpaueb/bert-base-uncased-contracts](https://huggingface.co/nlpaueb/bert-base-uncased-contracts) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5961 |
| - Accuracy: 0.8955 |
| |
| ## 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 |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 3.0237 | 1.0 | 883 | 0.9479 | 0.7783 | |
| | 0.6024 | 2.0 | 1766 | 0.5360 | 0.8713 | |
| | 0.2674 | 3.0 | 2649 | 0.5095 | 0.8866 | |
| | 0.1629 | 4.0 | 3532 | 0.5706 | 0.8904 | |
| | 0.1027 | 5.0 | 4415 | 0.5767 | 0.8866 | |
| | 0.0724 | 6.0 | 5298 | 0.5502 | 0.8955 | |
| | 0.0646 | 7.0 | 6181 | 0.5825 | 0.8917 | |
| | 0.0458 | 8.0 | 7064 | 0.6150 | 0.8981 | |
| | 0.0359 | 9.0 | 7947 | 0.5936 | 0.8955 | |
| | 0.0268 | 10.0 | 8830 | 0.5961 | 0.8955 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.42.4 |
| - Pytorch 2.3.1+cu121 |
| - Datasets 2.21.0 |
| - Tokenizers 0.19.1 |
|
|
