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--- |
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license: mit |
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base_model: Jean-Baptiste/roberta-large-ner-english |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: roberta |
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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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# roberta |
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This model is a fine-tuned version of [Jean-Baptiste/roberta-large-ner-english](https://huggingface.co/Jean-Baptiste/roberta-large-ner-english) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3908 |
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- Precision: 0.5990 |
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- Recall: 0.5581 |
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- F1: 0.5778 |
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- Accuracy: 0.9470 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 151 | 0.2078 | 0.1899 | 0.2388 | 0.2115 | 0.9246 | |
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| No log | 2.0 | 302 | 0.1499 | 0.4322 | 0.5535 | 0.4854 | 0.9393 | |
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| No log | 3.0 | 453 | 0.1916 | 0.5204 | 0.4946 | 0.5072 | 0.9418 | |
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| 0.1542 | 4.0 | 604 | 0.1671 | 0.4615 | 0.5109 | 0.4849 | 0.9426 | |
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| 0.1542 | 5.0 | 755 | 0.1940 | 0.4841 | 0.4829 | 0.4835 | 0.9439 | |
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| 0.1542 | 6.0 | 906 | 0.2462 | 0.5066 | 0.5651 | 0.5343 | 0.9428 | |
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| 0.0616 | 7.0 | 1057 | 0.2106 | 0.5041 | 0.5271 | 0.5153 | 0.9437 | |
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| 0.0616 | 8.0 | 1208 | 0.2621 | 0.5620 | 0.5202 | 0.5403 | 0.9474 | |
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| 0.0616 | 9.0 | 1359 | 0.2903 | 0.5242 | 0.5550 | 0.5392 | 0.9440 | |
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| 0.0326 | 10.0 | 1510 | 0.3083 | 0.5883 | 0.5628 | 0.5753 | 0.9483 | |
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| 0.0326 | 11.0 | 1661 | 0.3125 | 0.5451 | 0.5853 | 0.5645 | 0.9444 | |
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| 0.0326 | 12.0 | 1812 | 0.3616 | 0.5503 | 0.5388 | 0.5445 | 0.9427 | |
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| 0.0326 | 13.0 | 1963 | 0.3398 | 0.5978 | 0.5023 | 0.5459 | 0.9447 | |
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| 0.0155 | 14.0 | 2114 | 0.2942 | 0.5701 | 0.5550 | 0.5625 | 0.9467 | |
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| 0.0155 | 15.0 | 2265 | 0.3723 | 0.5771 | 0.5597 | 0.5683 | 0.9462 | |
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| 0.0155 | 16.0 | 2416 | 0.3651 | 0.5751 | 0.5760 | 0.5755 | 0.9439 | |
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| 0.0062 | 17.0 | 2567 | 0.3674 | 0.5667 | 0.5891 | 0.5777 | 0.9455 | |
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| 0.0062 | 18.0 | 2718 | 0.3866 | 0.5897 | 0.5403 | 0.5639 | 0.9463 | |
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| 0.0062 | 19.0 | 2869 | 0.3908 | 0.5990 | 0.5581 | 0.5778 | 0.9470 | |
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| 0.0033 | 20.0 | 3020 | 0.4036 | 0.5914 | 0.5620 | 0.5763 | 0.9467 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.2 |
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- Tokenizers 0.13.3 |
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