| | --- |
| | license: apache-2.0 |
| | base_model: bert-base-cased |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: bert-ner-essays-find_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-find_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: 0.1978 |
| | - B-span: {'precision': 0.8451327433628318, 'recall': 0.8856259659969088, 'f1-score': 0.8649056603773585, 'support': 647.0} |
| | - I-span: {'precision': 0.9613473219215903, 'recall': 0.9557182067703568, 'f1-score': 0.9585244999082401, 'support': 10930.0} |
| | - O: {'precision': 0.89764120320277, 'recall': 0.9040976460331299, 'f1-score': 0.9008578564447822, 'support': 4588.0} |
| | - Accuracy: 0.9383 |
| | - Macro avg: {'precision': 0.9013737561623975, 'recall': 0.9151472729334652, 'f1-score': 0.9080960055767937, 'support': 16165.0} |
| | - Weighted avg: {'precision': 0.9386145965884964, 'recall': 0.9382616764614908, 'f1-score': 0.9384103056993428, 'support': 16165.0} |
| | |
| | ## 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 | B-span | I-span | O | Accuracy | Macro avg | Weighted avg | |
| | |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:| |
| | | No log | 1.0 | 196 | 0.1948 | {'precision': 0.8323076923076923, 'recall': 0.8361669242658424, 'f1-score': 0.8342328450269854, 'support': 647.0} | {'precision': 0.9544583371360774, 'recall': 0.9568161024702653, 'f1-score': 0.9556357655229132, 'support': 10930.0} | {'precision': 0.8977621763931549, 'recall': 0.8918918918918919, 'f1-score': 0.89481740651651, 'support': 4588.0} | 0.9336 | {'precision': 0.8948427352789748, 'recall': 0.894958306209333, 'f1-score': 0.8948953390221361, 'support': 16165.0} | {'precision': 0.9334776100904544, 'recall': 0.9335601608413239, 'f1-score': 0.9335149909678719, 'support': 16165.0} | |
| | | No log | 2.0 | 392 | 0.1840 | {'precision': 0.8016528925619835, 'recall': 0.8995363214837713, 'f1-score': 0.8477785870356882, 'support': 647.0} | {'precision': 0.9520368530394725, 'recall': 0.9643183897529735, 'f1-score': 0.9581382664424344, 'support': 10930.0} | {'precision': 0.9198717948717948, 'recall': 0.8757628596338274, 'f1-score': 0.8972755694506476, 'support': 4588.0} | 0.9366 | {'precision': 0.8911871801577503, 'recall': 0.9132058569568574, 'f1-score': 0.9010641409762568, 'support': 16165.0} | {'precision': 0.936888587694453, 'recall': 0.9365914011753789, 'f1-score': 0.936446910650632, 'support': 16165.0} | |
| | | 0.2568 | 3.0 | 588 | 0.1978 | {'precision': 0.8451327433628318, 'recall': 0.8856259659969088, 'f1-score': 0.8649056603773585, 'support': 647.0} | {'precision': 0.9613473219215903, 'recall': 0.9557182067703568, 'f1-score': 0.9585244999082401, 'support': 10930.0} | {'precision': 0.89764120320277, 'recall': 0.9040976460331299, 'f1-score': 0.9008578564447822, 'support': 4588.0} | 0.9383 | {'precision': 0.9013737561623975, 'recall': 0.9151472729334652, 'f1-score': 0.9080960055767937, 'support': 16165.0} | {'precision': 0.9386145965884964, 'recall': 0.9382616764614908, 'f1-score': 0.9384103056993428, 'support': 16165.0} | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.37.1 |
| | - Pytorch 2.1.2+cu121 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.1 |
| | |
