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# 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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-ner-essays-label_span", "results": []}]}
transformers pytorch safetensors bert token-classification generated_from_trainer base_model:bert-base-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us