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--- |
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license: cc-by-nc-3.0 |
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base_model: yanekyuk/bert-keyword-extractor |
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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: bert-keyword-extractor_oknashar |
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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-keyword-extractor_oknashar |
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This model is a fine-tuned version of [yanekyuk/bert-keyword-extractor](https://huggingface.co/yanekyuk/bert-keyword-extractor) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2965 |
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- Precision: 0.5862 |
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- Recall: 0.6937 |
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- F1: 0.6354 |
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- Accuracy: 0.9555 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) 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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| 0.204 | 1.0 | 1357 | 0.1717 | 0.3345 | 0.3419 | 0.3382 | 0.9375 | |
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| 0.161 | 2.0 | 2714 | 0.1547 | 0.3949 | 0.4845 | 0.4352 | 0.9426 | |
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| 0.1333 | 3.0 | 4071 | 0.1506 | 0.4369 | 0.5342 | 0.4807 | 0.9481 | |
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| 0.1147 | 4.0 | 5428 | 0.1461 | 0.5121 | 0.5424 | 0.5268 | 0.9522 | |
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| 0.0925 | 5.0 | 6785 | 0.1514 | 0.4864 | 0.5868 | 0.5319 | 0.9506 | |
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| 0.0788 | 6.0 | 8142 | 0.1477 | 0.5173 | 0.6043 | 0.5574 | 0.9529 | |
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| 0.0676 | 7.0 | 9499 | 0.1765 | 0.5126 | 0.6277 | 0.5644 | 0.9543 | |
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| 0.0542 | 8.0 | 10856 | 0.1638 | 0.5066 | 0.6704 | 0.5771 | 0.9500 | |
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| 0.0453 | 9.0 | 12213 | 0.1746 | 0.5158 | 0.6598 | 0.5790 | 0.9517 | |
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| 0.0366 | 10.0 | 13570 | 0.1905 | 0.5876 | 0.6429 | 0.6140 | 0.9569 | |
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| 0.0327 | 11.0 | 14927 | 0.2215 | 0.5709 | 0.6593 | 0.6119 | 0.9557 | |
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| 0.0296 | 12.0 | 16284 | 0.2109 | 0.5648 | 0.6598 | 0.6086 | 0.9557 | |
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| 0.0269 | 13.0 | 17641 | 0.2226 | 0.5632 | 0.6745 | 0.6138 | 0.9543 | |
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| 0.0203 | 14.0 | 18998 | 0.2464 | 0.5682 | 0.6861 | 0.6217 | 0.9543 | |
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| 0.0163 | 15.0 | 20355 | 0.2738 | 0.5747 | 0.6791 | 0.6226 | 0.9541 | |
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| 0.016 | 16.0 | 21712 | 0.2805 | 0.5868 | 0.6832 | 0.6314 | 0.9557 | |
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| 0.0115 | 17.0 | 23069 | 0.2923 | 0.5798 | 0.6879 | 0.6292 | 0.9551 | |
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| 0.0107 | 18.0 | 24426 | 0.2923 | 0.5943 | 0.6926 | 0.6397 | 0.9558 | |
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| 0.0105 | 19.0 | 25783 | 0.2992 | 0.5890 | 0.6902 | 0.6356 | 0.9553 | |
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| 0.0087 | 20.0 | 27140 | 0.2965 | 0.5862 | 0.6937 | 0.6354 | 0.9555 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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