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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: bert-base-cased |
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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: fine_tune_bert-base-cased |
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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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# fine_tune_bert-base-cased |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co./bert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0842 |
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- Precision: 0.9376 |
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- Recall: 0.9541 |
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- F1: 0.9458 |
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- Accuracy: 0.9866 |
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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: 64 |
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- eval_batch_size: 64 |
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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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| 0.2667 | 1.0 | 220 | 0.0739 | 0.8619 | 0.9118 | 0.8862 | 0.9776 | |
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| 0.0602 | 2.0 | 440 | 0.0641 | 0.9109 | 0.9357 | 0.9231 | 0.9830 | |
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| 0.0361 | 3.0 | 660 | 0.0594 | 0.9187 | 0.9401 | 0.9293 | 0.9845 | |
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| 0.0234 | 4.0 | 880 | 0.0564 | 0.9233 | 0.9461 | 0.9346 | 0.9854 | |
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| 0.0164 | 5.0 | 1100 | 0.0585 | 0.9211 | 0.9465 | 0.9336 | 0.9856 | |
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| 0.0123 | 6.0 | 1320 | 0.0656 | 0.9212 | 0.9483 | 0.9346 | 0.9850 | |
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| 0.0084 | 7.0 | 1540 | 0.0639 | 0.9290 | 0.9514 | 0.9401 | 0.9864 | |
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| 0.0072 | 8.0 | 1760 | 0.0735 | 0.9325 | 0.9482 | 0.9403 | 0.9862 | |
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| 0.0051 | 9.0 | 1980 | 0.0745 | 0.9319 | 0.9488 | 0.9403 | 0.9856 | |
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| 0.0042 | 10.0 | 2200 | 0.0783 | 0.9308 | 0.9490 | 0.9398 | 0.9858 | |
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| 0.0034 | 11.0 | 2420 | 0.0782 | 0.9337 | 0.9509 | 0.9422 | 0.9862 | |
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| 0.0026 | 12.0 | 2640 | 0.0822 | 0.9328 | 0.9505 | 0.9416 | 0.9858 | |
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| 0.0019 | 13.0 | 2860 | 0.0785 | 0.9335 | 0.9525 | 0.9429 | 0.9862 | |
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| 0.0018 | 14.0 | 3080 | 0.0819 | 0.9382 | 0.9525 | 0.9453 | 0.9865 | |
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| 0.0015 | 15.0 | 3300 | 0.0846 | 0.9349 | 0.9524 | 0.9436 | 0.9863 | |
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| 0.0013 | 16.0 | 3520 | 0.0880 | 0.9353 | 0.9519 | 0.9435 | 0.9860 | |
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| 0.0012 | 17.0 | 3740 | 0.0846 | 0.9362 | 0.9527 | 0.9444 | 0.9864 | |
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| 0.001 | 18.0 | 3960 | 0.0868 | 0.9374 | 0.9532 | 0.9453 | 0.9864 | |
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| 0.0009 | 19.0 | 4180 | 0.0842 | 0.9381 | 0.9536 | 0.9458 | 0.9868 | |
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| 0.0009 | 20.0 | 4400 | 0.0842 | 0.9376 | 0.9541 | 0.9458 | 0.9866 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
