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--- |
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license: mit |
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base_model: roberta-base |
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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 [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6785 |
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- Claim: {'precision': 0.47368421052631576, 'recall': 0.375, 'f1-score': 0.4186046511627907, 'support': 144.0} |
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- Majorclaim: {'precision': 0.6923076923076923, 'recall': 0.5, 'f1-score': 0.5806451612903226, 'support': 72.0} |
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- Premise: {'precision': 0.7900677200902935, 'recall': 0.8905852417302799, 'f1-score': 0.8373205741626795, 'support': 393.0} |
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- Accuracy: 0.7225 |
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- Macro avg: {'precision': 0.6520198743081005, 'recall': 0.5885284139100934, 'f1-score': 0.6121901288719309, 'support': 609.0} |
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- Weighted avg: {'precision': 0.7036999904062868, 'recall': 0.722495894909688, 'f1-score': 0.707967991832969, '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 | 0.7119 | {'precision': 0.5072463768115942, 'recall': 0.24305555555555555, 'f1-score': 0.32863849765258213, 'support': 144.0} | {'precision': 0.5753424657534246, 'recall': 0.5833333333333334, 'f1-score': 0.5793103448275863, 'support': 72.0} | {'precision': 0.7687366167023555, 'recall': 0.9134860050890585, 'f1-score': 0.8348837209302326, 'support': 393.0} | 0.7159 | {'precision': 0.6171084864224582, 'recall': 0.5799582979926492, 'f1-score': 0.580944187803467, 'support': 609.0} | {'precision': 0.6840420790790506, 'recall': 0.715927750410509, 'f1-score': 0.6849648453450565, 'support': 609.0} | |
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| 0.7298 | 2.0 | 534 | 0.6785 | {'precision': 0.47368421052631576, 'recall': 0.375, 'f1-score': 0.4186046511627907, 'support': 144.0} | {'precision': 0.6923076923076923, 'recall': 0.5, 'f1-score': 0.5806451612903226, 'support': 72.0} | {'precision': 0.7900677200902935, 'recall': 0.8905852417302799, 'f1-score': 0.8373205741626795, 'support': 393.0} | 0.7225 | {'precision': 0.6520198743081005, 'recall': 0.5885284139100934, 'f1-score': 0.6121901288719309, 'support': 609.0} | {'precision': 0.7036999904062868, 'recall': 0.722495894909688, 'f1-score': 0.707967991832969, 'support': 609.0} | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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