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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-5 |
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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-5 |
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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: 2.2694 |
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- Accuracy: 0.745 |
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- Precision: 0.7091 |
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- Recall: 0.7017 |
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- F1: 0.7043 |
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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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| 0.9558 | 1.0 | 50 | 0.8587 | 0.665 | 0.6541 | 0.6084 | 0.5787 | |
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| 0.7752 | 2.0 | 100 | 0.8892 | 0.655 | 0.6416 | 0.5835 | 0.5790 | |
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| 0.5771 | 3.0 | 150 | 0.7066 | 0.715 | 0.6884 | 0.7026 | 0.6915 | |
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| 0.3738 | 4.0 | 200 | 1.0130 | 0.705 | 0.6578 | 0.6409 | 0.6455 | |
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| 0.253 | 5.0 | 250 | 1.1405 | 0.74 | 0.7132 | 0.7018 | 0.7059 | |
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| 0.1604 | 6.0 | 300 | 1.1993 | 0.69 | 0.6334 | 0.6244 | 0.6261 | |
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| 0.1265 | 7.0 | 350 | 1.5984 | 0.705 | 0.6875 | 0.6775 | 0.6764 | |
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| 0.0741 | 8.0 | 400 | 1.4755 | 0.745 | 0.7116 | 0.7132 | 0.7114 | |
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| 0.0505 | 9.0 | 450 | 2.2514 | 0.71 | 0.6791 | 0.6427 | 0.6524 | |
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| 0.0372 | 10.0 | 500 | 2.2234 | 0.71 | 0.6675 | 0.6503 | 0.6488 | |
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| 0.0161 | 11.0 | 550 | 2.1070 | 0.72 | 0.6783 | 0.6712 | 0.6718 | |
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| 0.016 | 12.0 | 600 | 2.0232 | 0.72 | 0.6737 | 0.6659 | 0.6688 | |
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| 0.0197 | 13.0 | 650 | 2.0224 | 0.74 | 0.7065 | 0.6954 | 0.6895 | |
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| 0.01 | 14.0 | 700 | 2.1777 | 0.74 | 0.7023 | 0.6904 | 0.6936 | |
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| 0.0173 | 15.0 | 750 | 2.3227 | 0.72 | 0.6761 | 0.6590 | 0.6638 | |
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| 0.0066 | 16.0 | 800 | 2.2131 | 0.735 | 0.6983 | 0.6912 | 0.6923 | |
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| 0.0043 | 17.0 | 850 | 2.1196 | 0.76 | 0.7278 | 0.7207 | 0.7191 | |
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| 0.0039 | 18.0 | 900 | 2.4087 | 0.72 | 0.6791 | 0.6590 | 0.6650 | |
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| 0.0041 | 19.0 | 950 | 2.1487 | 0.73 | 0.6889 | 0.6860 | 0.6873 | |
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| 0.0024 | 20.0 | 1000 | 2.2694 | 0.745 | 0.7091 | 0.7017 | 0.7043 | |
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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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