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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: training-4 |
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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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# training-4 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0335 |
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- Accuracy: 0.9931 |
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- Precision: 0.9982 |
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- Recall: 0.9875 |
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- F1: 0.9928 |
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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: 1e-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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0.25 | 85 | 0.0511 | 0.9819 | 0.9821 | 0.9804 | 0.9813 | |
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| No log | 0.5 | 170 | 0.0752 | 0.9836 | 0.9982 | 0.9679 | 0.9828 | |
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| No log | 0.75 | 255 | 0.0550 | 0.9888 | 0.9841 | 0.9929 | 0.9885 | |
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| 0.1064 | 1.0 | 340 | 0.0383 | 0.9923 | 0.9964 | 0.9875 | 0.9919 | |
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| 0.1064 | 1.25 | 425 | 0.0485 | 0.9923 | 0.9982 | 0.9857 | 0.9919 | |
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| 0.1064 | 1.5 | 510 | 0.0468 | 0.9914 | 0.9964 | 0.9857 | 0.9910 | |
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| 0.1064 | 1.76 | 595 | 0.0477 | 0.9914 | 1.0 | 0.9822 | 0.9910 | |
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| 0.0322 | 2.01 | 680 | 0.0506 | 0.9931 | 1.0 | 0.9857 | 0.9928 | |
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| 0.0322 | 2.26 | 765 | 0.0455 | 0.9914 | 0.9928 | 0.9893 | 0.9911 | |
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| 0.0322 | 2.51 | 850 | 0.0466 | 0.9914 | 0.9946 | 0.9875 | 0.9911 | |
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| 0.0322 | 2.76 | 935 | 0.0491 | 0.9931 | 1.0 | 0.9857 | 0.9928 | |
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| 0.0217 | 3.01 | 1020 | 0.0517 | 0.9923 | 0.9964 | 0.9875 | 0.9919 | |
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| 0.0217 | 3.26 | 1105 | 0.0455 | 0.9931 | 1.0 | 0.9857 | 0.9928 | |
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| 0.0217 | 3.51 | 1190 | 0.0338 | 0.9931 | 0.9982 | 0.9875 | 0.9928 | |
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| 0.0217 | 3.76 | 1275 | 0.0385 | 0.9940 | 1.0 | 0.9875 | 0.9937 | |
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| 0.024 | 4.01 | 1360 | 0.0376 | 0.9931 | 1.0 | 0.9857 | 0.9928 | |
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| 0.024 | 4.26 | 1445 | 0.0332 | 0.9931 | 0.9982 | 0.9875 | 0.9928 | |
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| 0.024 | 4.51 | 1530 | 0.0343 | 0.9923 | 0.9946 | 0.9893 | 0.9920 | |
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| 0.024 | 4.76 | 1615 | 0.0335 | 0.9931 | 0.9982 | 0.9875 | 0.9928 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.2.0.dev20230913+cu121 |
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- Tokenizers 0.13.3 |
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