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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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- f1 |
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model-index: |
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- name: tests |
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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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# tests |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2590 |
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- F1: 0.6703 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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 | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| No log | 1.0 | 65 | 1.6059 | 0.4378 | |
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| No log | 2.0 | 130 | 1.1745 | 0.5973 | |
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| No log | 3.0 | 195 | 1.1097 | 0.6622 | |
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| No log | 4.0 | 260 | 1.1188 | 0.6459 | |
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| No log | 5.0 | 325 | 1.2955 | 0.6324 | |
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| No log | 6.0 | 390 | 1.5177 | 0.6541 | |
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| No log | 7.0 | 455 | 1.8703 | 0.6189 | |
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| 0.6433 | 8.0 | 520 | 1.7936 | 0.6486 | |
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| 0.6433 | 9.0 | 585 | 1.9287 | 0.6459 | |
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| 0.6433 | 10.0 | 650 | 1.9884 | 0.6595 | |
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| 0.6433 | 11.0 | 715 | 2.0985 | 0.6568 | |
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| 0.6433 | 12.0 | 780 | 2.1248 | 0.6730 | |
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| 0.6433 | 13.0 | 845 | 2.1511 | 0.6676 | |
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| 0.6433 | 14.0 | 910 | 2.2218 | 0.6622 | |
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| 0.6433 | 15.0 | 975 | 2.2069 | 0.6703 | |
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| 0.0065 | 16.0 | 1040 | 2.2273 | 0.6676 | |
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| 0.0065 | 17.0 | 1105 | 2.2626 | 0.6649 | |
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| 0.0065 | 18.0 | 1170 | 2.2593 | 0.6703 | |
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| 0.0065 | 19.0 | 1235 | 2.2577 | 0.6703 | |
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| 0.0065 | 20.0 | 1300 | 2.2590 | 0.6703 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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