metadata
license: apache-2.0
base_model: bert-base-cased
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 bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6855
- Claim: {'precision': 0.4016393442622951, 'recall': 0.3402777777777778, 'f1-score': 0.3684210526315789, 'support': 144.0}
- Majorclaim: {'precision': 0.5882352941176471, 'recall': 0.5555555555555556, 'f1-score': 0.5714285714285715, 'support': 72.0}
- Premise: {'precision': 0.7947494033412887, 'recall': 0.8473282442748091, 'f1-score': 0.8201970443349754, 'support': 393.0}
- Accuracy: 0.6929
- Macro avg: {'precision': 0.5948746805737436, 'recall': 0.5810538592027141, 'f1-score': 0.5866822227983753, 'support': 609.0}
- Weighted avg: {'precision': 0.6773818099562685, 'recall': 0.6929392446633826, 'f1-score': 0.6839621135393265, '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 | 0.6910 | {'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} |
| 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} |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3