metadata
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-ner-essays-classify_span
results: []
bert-ner-essays-classify_span
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6785
- Claim: {'precision': 0.47368421052631576, 'recall': 0.375, 'f1-score': 0.4186046511627907, 'support': 144.0}
- Majorclaim: {'precision': 0.6923076923076923, 'recall': 0.5, 'f1-score': 0.5806451612903226, 'support': 72.0}
- Premise: {'precision': 0.7900677200902935, 'recall': 0.8905852417302799, 'f1-score': 0.8373205741626795, 'support': 393.0}
- Accuracy: 0.7225
- Macro avg: {'precision': 0.6520198743081005, 'recall': 0.5885284139100934, 'f1-score': 0.6121901288719309, 'support': 609.0}
- Weighted avg: {'precision': 0.7036999904062868, 'recall': 0.722495894909688, 'f1-score': 0.707967991832969, '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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | Premise | Accuracy | Macro avg | Weighted avg |
|---|---|---|---|---|---|---|---|---|---|
| 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} |
| 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} |
Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1