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--- |
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license: mit |
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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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base_model: roberta-base |
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model-index: |
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- name: run-1 |
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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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# run-1 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3480 |
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- Accuracy: 0.73 |
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- Precision: 0.6930 |
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- Recall: 0.6829 |
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- F1: 0.6871 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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 | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.0042 | 1.0 | 50 | 0.8281 | 0.665 | 0.6105 | 0.6240 | 0.6016 | |
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| 0.8062 | 2.0 | 100 | 0.9313 | 0.665 | 0.6513 | 0.6069 | 0.5505 | |
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| 0.627 | 3.0 | 150 | 0.8275 | 0.72 | 0.6713 | 0.6598 | 0.6638 | |
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| 0.4692 | 4.0 | 200 | 0.8289 | 0.68 | 0.6368 | 0.6447 | 0.6398 | |
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| 0.2766 | 5.0 | 250 | 1.1263 | 0.72 | 0.6893 | 0.6431 | 0.6417 | |
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| 0.1868 | 6.0 | 300 | 1.2901 | 0.725 | 0.6823 | 0.6727 | 0.6764 | |
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| 0.1054 | 7.0 | 350 | 1.6742 | 0.68 | 0.6696 | 0.6427 | 0.6384 | |
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| 0.0837 | 8.0 | 400 | 1.6199 | 0.72 | 0.6826 | 0.6735 | 0.6772 | |
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| 0.0451 | 9.0 | 450 | 1.8324 | 0.735 | 0.7029 | 0.6726 | 0.6727 | |
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| 0.0532 | 10.0 | 500 | 2.1136 | 0.705 | 0.6949 | 0.6725 | 0.6671 | |
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| 0.0178 | 11.0 | 550 | 2.1136 | 0.73 | 0.6931 | 0.6810 | 0.6832 | |
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| 0.0111 | 12.0 | 600 | 2.2740 | 0.69 | 0.6505 | 0.6430 | 0.6461 | |
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| 0.0205 | 13.0 | 650 | 2.3026 | 0.725 | 0.6965 | 0.6685 | 0.6716 | |
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| 0.0181 | 14.0 | 700 | 2.2901 | 0.735 | 0.7045 | 0.6806 | 0.6876 | |
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| 0.0074 | 15.0 | 750 | 2.2277 | 0.74 | 0.7075 | 0.6923 | 0.6978 | |
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| 0.0063 | 16.0 | 800 | 2.2720 | 0.75 | 0.7229 | 0.7051 | 0.7105 | |
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| 0.0156 | 17.0 | 850 | 2.1237 | 0.73 | 0.6908 | 0.6841 | 0.6854 | |
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| 0.0027 | 18.0 | 900 | 2.2376 | 0.73 | 0.6936 | 0.6837 | 0.6874 | |
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| 0.003 | 19.0 | 950 | 2.3359 | 0.735 | 0.6992 | 0.6897 | 0.6937 | |
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| 0.0012 | 20.0 | 1000 | 2.3480 | 0.73 | 0.6930 | 0.6829 | 0.6871 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Tokenizers 0.13.2 |
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