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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-2 |
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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-2 |
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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: 2.1449 |
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- Accuracy: 0.75 |
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- Precision: 0.7115 |
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- Recall: 0.7093 |
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- F1: 0.7103 |
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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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| 0.9838 | 1.0 | 50 | 0.8621 | 0.645 | 0.6536 | 0.6130 | 0.6124 | |
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| 0.7134 | 2.0 | 100 | 0.8124 | 0.7 | 0.6628 | 0.6421 | 0.6483 | |
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| 0.4911 | 3.0 | 150 | 0.8571 | 0.7 | 0.6726 | 0.6314 | 0.6361 | |
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| 0.3104 | 4.0 | 200 | 0.8228 | 0.76 | 0.7298 | 0.7367 | 0.7294 | |
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| 0.1942 | 5.0 | 250 | 1.1132 | 0.76 | 0.7282 | 0.7031 | 0.7119 | |
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| 0.1409 | 6.0 | 300 | 1.2218 | 0.685 | 0.6516 | 0.6560 | 0.6524 | |
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| 0.0976 | 7.0 | 350 | 1.3648 | 0.715 | 0.6984 | 0.7044 | 0.6946 | |
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| 0.0791 | 8.0 | 400 | 1.5985 | 0.745 | 0.7183 | 0.7113 | 0.7124 | |
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| 0.0647 | 9.0 | 450 | 1.8884 | 0.725 | 0.6818 | 0.6761 | 0.6785 | |
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| 0.0275 | 10.0 | 500 | 1.8639 | 0.725 | 0.6979 | 0.7008 | 0.6958 | |
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| 0.0329 | 11.0 | 550 | 1.8831 | 0.72 | 0.6816 | 0.6869 | 0.6838 | |
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| 0.0169 | 12.0 | 600 | 2.1426 | 0.73 | 0.6864 | 0.6776 | 0.6794 | |
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| 0.0072 | 13.0 | 650 | 2.2483 | 0.725 | 0.7187 | 0.7054 | 0.6968 | |
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| 0.0203 | 14.0 | 700 | 2.2901 | 0.735 | 0.6986 | 0.6885 | 0.6921 | |
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| 0.0093 | 15.0 | 750 | 2.3134 | 0.725 | 0.6830 | 0.6666 | 0.6723 | |
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| 0.0089 | 16.0 | 800 | 2.1598 | 0.73 | 0.6919 | 0.6860 | 0.6885 | |
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| 0.0061 | 17.0 | 850 | 2.0879 | 0.75 | 0.7129 | 0.7132 | 0.7125 | |
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| 0.0024 | 18.0 | 900 | 2.1285 | 0.745 | 0.7062 | 0.7071 | 0.7049 | |
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| 0.0043 | 19.0 | 950 | 2.1386 | 0.74 | 0.7001 | 0.7003 | 0.6985 | |
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| 0.0028 | 20.0 | 1000 | 2.1449 | 0.75 | 0.7115 | 0.7093 | 0.7103 | |
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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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