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--- |
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license: apache-2.0 |
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base_model: bert-base-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert-ner-essays-classify_span |
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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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# bert-ner-essays-classify_span |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6855 |
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- Claim: {'precision': 0.4016393442622951, 'recall': 0.3402777777777778, 'f1-score': 0.3684210526315789, 'support': 144.0} |
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- Majorclaim: {'precision': 0.5882352941176471, 'recall': 0.5555555555555556, 'f1-score': 0.5714285714285715, 'support': 72.0} |
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- Premise: {'precision': 0.7947494033412887, 'recall': 0.8473282442748091, 'f1-score': 0.8201970443349754, 'support': 393.0} |
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- Accuracy: 0.6929 |
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- Macro avg: {'precision': 0.5948746805737436, 'recall': 0.5810538592027141, 'f1-score': 0.5866822227983753, 'support': 609.0} |
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- Weighted avg: {'precision': 0.6773818099562685, 'recall': 0.6929392446633826, 'f1-score': 0.6839621135393265, 'support': 609.0} |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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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 | Claim | Majorclaim | Premise | Accuracy | Macro avg | Weighted avg | |
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|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:| |
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| No log | 1.0 | 267 | 0.6910 | {'precision': 0.40860215053763443, 'recall': 0.2638888888888889, 'f1-score': 0.3206751054852321, 'support': 144.0} | {'precision': 0.625, 'recall': 0.3472222222222222, 'f1-score': 0.44642857142857145, 'support': 72.0} | {'precision': 0.7521008403361344, 'recall': 0.910941475826972, 'f1-score': 0.8239355581127733, 'support': 393.0} | 0.6913 | {'precision': 0.5952343302912563, 'recall': 0.5073508623126944, 'f1-score': 0.5303464116755255, 'support': 609.0} | {'precision': 0.6558527749253206, 'recall': 0.6912972085385879, 'f1-score': 0.66030664478005, 'support': 609.0} | |
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| 0.7234 | 2.0 | 534 | 0.6855 | {'precision': 0.4016393442622951, 'recall': 0.3402777777777778, 'f1-score': 0.3684210526315789, 'support': 144.0} | {'precision': 0.5882352941176471, 'recall': 0.5555555555555556, 'f1-score': 0.5714285714285715, 'support': 72.0} | {'precision': 0.7947494033412887, 'recall': 0.8473282442748091, 'f1-score': 0.8201970443349754, 'support': 393.0} | 0.6929 | {'precision': 0.5948746805737436, 'recall': 0.5810538592027141, 'f1-score': 0.5866822227983753, 'support': 609.0} | {'precision': 0.6773818099562685, 'recall': 0.6929392446633826, 'f1-score': 0.6839621135393265, 'support': 609.0} | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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