| | --- |
| | license: apache-2.0 |
| | base_model: bert-base-cased |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: bert-ner-essays-label_span |
| | 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. --> |
| |
|
| | # bert-ner-essays-label_span |
| | |
| | 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: 0.8463 |
| | - Claim: {'precision': 0.4140127388535032, 'recall': 0.4513888888888889, 'f1-score': 0.4318936877076412, 'support': 144.0} |
| | - Majorclaim: {'precision': 0.6923076923076923, 'recall': 0.5, 'f1-score': 0.5806451612903226, 'support': 72.0} |
| | - Premise: {'precision': 0.8025, 'recall': 0.816793893129771, 'f1-score': 0.8095838587641867, 'support': 393.0} |
| | - Accuracy: 0.6929 |
| | - Macro avg: {'precision': 0.6362734770537318, 'recall': 0.5893942606728867, 'f1-score': 0.6073742359207168, 'support': 609.0} |
| | - Weighted avg: {'precision': 0.6976132811840038, 'recall': 0.6929392446633826, 'f1-score': 0.6932111644287832, 'support': 609.0} |
| | |
| | ## 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: 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 | Claim | Majorclaim | Premise | Accuracy | Macro avg | Weighted avg | |
| | |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:| |
| | | 0.7343 | 1.0 | 533 | 0.6230 | {'precision': 0.47058823529411764, 'recall': 0.2777777777777778, 'f1-score': 0.3493449781659389, 'support': 144.0} | {'precision': 0.5647058823529412, 'recall': 0.6666666666666666, 'f1-score': 0.6114649681528662, 'support': 72.0} | {'precision': 0.7790432801822323, 'recall': 0.8702290076335878, 'f1-score': 0.8221153846153846, 'support': 393.0} | 0.7061 | {'precision': 0.6047791326097637, 'recall': 0.6048911506926774, 'f1-score': 0.5943084436447299, 'support': 609.0} | {'precision': 0.6807677151451265, 'recall': 0.7060755336617406, 'f1-score': 0.6854228254790602, 'support': 609.0} | |
| | | 0.5313 | 2.0 | 1066 | 0.6606 | {'precision': 0.4491525423728814, 'recall': 0.3680555555555556, 'f1-score': 0.4045801526717558, 'support': 144.0} | {'precision': 0.6612903225806451, 'recall': 0.5694444444444444, 'f1-score': 0.6119402985074627, 'support': 72.0} | {'precision': 0.7878787878787878, 'recall': 0.8600508905852418, 'f1-score': 0.8223844282238443, 'support': 393.0} | 0.7094 | {'precision': 0.6327738842774381, 'recall': 0.5991836301950806, 'f1-score': 0.6129682931343542, 'support': 609.0} | {'precision': 0.6928197585613547, 'recall': 0.7093596059113301, 'f1-score': 0.6987131753189507, 'support': 609.0} | |
| | | 0.3551 | 3.0 | 1599 | 0.8463 | {'precision': 0.4140127388535032, 'recall': 0.4513888888888889, 'f1-score': 0.4318936877076412, 'support': 144.0} | {'precision': 0.6923076923076923, 'recall': 0.5, 'f1-score': 0.5806451612903226, 'support': 72.0} | {'precision': 0.8025, 'recall': 0.816793893129771, 'f1-score': 0.8095838587641867, 'support': 393.0} | 0.6929 | {'precision': 0.6362734770537318, 'recall': 0.5893942606728867, 'f1-score': 0.6073742359207168, 'support': 609.0} | {'precision': 0.6976132811840038, 'recall': 0.6929392446633826, 'f1-score': 0.6932111644287832, 'support': 609.0} | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.37.1 |
| | - Pytorch 2.1.2+cu121 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.1 |
| | |
