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--- |
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license: mit |
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base_model: xlm-roberta-base |
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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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- f1 |
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model-index: |
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- name: scenario-TCR-XLMR-XCOPA_data-xcopa_all |
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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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# scenario-TCR-XLMR-XCOPA_data-xcopa_all |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6932 |
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- Accuracy: 0.4558 |
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- F1: 0.4185 |
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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: 500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 0.38 | 5 | 0.6932 | 0.49 | 0.4715 | |
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| No log | 0.77 | 10 | 0.6932 | 0.4925 | 0.4634 | |
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| No log | 1.15 | 15 | 0.6931 | 0.5033 | 0.4669 | |
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| No log | 1.54 | 20 | 0.6932 | 0.4683 | 0.4491 | |
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| No log | 1.92 | 25 | 0.6932 | 0.4883 | 0.4689 | |
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| No log | 2.31 | 30 | 0.6932 | 0.4808 | 0.4716 | |
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| No log | 2.69 | 35 | 0.6931 | 0.5058 | 0.4785 | |
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| No log | 3.08 | 40 | 0.6932 | 0.4867 | 0.4615 | |
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| No log | 3.46 | 45 | 0.6932 | 0.4533 | 0.4090 | |
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| No log | 3.85 | 50 | 0.6932 | 0.4592 | 0.4190 | |
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| No log | 4.23 | 55 | 0.6931 | 0.5167 | 0.4858 | |
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| No log | 4.62 | 60 | 0.6931 | 0.5133 | 0.4832 | |
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| No log | 5.0 | 65 | 0.6931 | 0.525 | 0.5128 | |
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| No log | 5.38 | 70 | 0.6931 | 0.5158 | 0.5072 | |
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| No log | 5.77 | 75 | 0.6931 | 0.5275 | 0.5158 | |
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| No log | 6.15 | 80 | 0.6931 | 0.5333 | 0.5302 | |
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| No log | 6.54 | 85 | 0.6931 | 0.5075 | 0.4883 | |
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| No log | 6.92 | 90 | 0.6931 | 0.5042 | 0.4893 | |
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| No log | 7.31 | 95 | 0.6930 | 0.5042 | 0.4979 | |
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| No log | 7.69 | 100 | 0.6932 | 0.4883 | 0.4623 | |
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| No log | 8.08 | 105 | 0.6932 | 0.4617 | 0.4159 | |
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| No log | 8.46 | 110 | 0.6932 | 0.4425 | 0.4085 | |
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| No log | 8.85 | 115 | 0.6931 | 0.4992 | 0.4705 | |
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| No log | 9.23 | 120 | 0.6931 | 0.4975 | 0.4668 | |
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| No log | 9.62 | 125 | 0.6931 | 0.4867 | 0.4510 | |
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| No log | 10.0 | 130 | 0.6931 | 0.4817 | 0.4456 | |
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| No log | 10.38 | 135 | 0.6931 | 0.4808 | 0.4442 | |
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| No log | 10.77 | 140 | 0.6932 | 0.49 | 0.4565 | |
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| No log | 11.15 | 145 | 0.6932 | 0.4908 | 0.4579 | |
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| No log | 11.54 | 150 | 0.6931 | 0.5067 | 0.4761 | |
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| No log | 11.92 | 155 | 0.6931 | 0.5083 | 0.4887 | |
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| No log | 12.31 | 160 | 0.6931 | 0.5217 | 0.4974 | |
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| No log | 12.69 | 165 | 0.6932 | 0.4967 | 0.4499 | |
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| No log | 13.08 | 170 | 0.6931 | 0.5158 | 0.4872 | |
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| No log | 13.46 | 175 | 0.6931 | 0.5225 | 0.5065 | |
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| No log | 13.85 | 180 | 0.6931 | 0.5475 | 0.5323 | |
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| No log | 14.23 | 185 | 0.6931 | 0.5383 | 0.5224 | |
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| No log | 14.62 | 190 | 0.6932 | 0.4925 | 0.4558 | |
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| No log | 15.0 | 195 | 0.6932 | 0.4783 | 0.4340 | |
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| No log | 15.38 | 200 | 0.6931 | 0.5267 | 0.5120 | |
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| No log | 15.77 | 205 | 0.6931 | 0.5158 | 0.4890 | |
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| No log | 16.15 | 210 | 0.6931 | 0.53 | 0.5179 | |
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| No log | 16.54 | 215 | 0.6932 | 0.48 | 0.4497 | |
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| No log | 16.92 | 220 | 0.6932 | 0.4558 | 0.4133 | |
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| No log | 17.31 | 225 | 0.6932 | 0.4742 | 0.4290 | |
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| No log | 17.69 | 230 | 0.6931 | 0.4967 | 0.4838 | |
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| No log | 18.08 | 235 | 0.6931 | 0.5325 | 0.5126 | |
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| No log | 18.46 | 240 | 0.6931 | 0.5242 | 0.5090 | |
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| No log | 18.85 | 245 | 0.6931 | 0.5242 | 0.5013 | |
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| No log | 19.23 | 250 | 0.6932 | 0.4683 | 0.4273 | |
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| No log | 19.62 | 255 | 0.6931 | 0.5158 | 0.4890 | |
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| No log | 20.0 | 260 | 0.6932 | 0.4533 | 0.4225 | |
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| No log | 20.38 | 265 | 0.6932 | 0.4658 | 0.4282 | |
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| No log | 20.77 | 270 | 0.6932 | 0.4667 | 0.4326 | |
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| No log | 21.15 | 275 | 0.6932 | 0.4725 | 0.4403 | |
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| No log | 21.54 | 280 | 0.6932 | 0.4667 | 0.4306 | |
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| No log | 21.92 | 285 | 0.6932 | 0.4658 | 0.4272 | |
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| No log | 22.31 | 290 | 0.6932 | 0.4625 | 0.4256 | |
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| No log | 22.69 | 295 | 0.6932 | 0.4617 | 0.4232 | |
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| No log | 23.08 | 300 | 0.6932 | 0.465 | 0.4247 | |
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| No log | 23.46 | 305 | 0.6932 | 0.455 | 0.4223 | |
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| No log | 23.85 | 310 | 0.6931 | 0.4558 | 0.4226 | |
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| No log | 24.23 | 315 | 0.6931 | 0.4875 | 0.4638 | |
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| No log | 24.62 | 320 | 0.6931 | 0.4642 | 0.4315 | |
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| No log | 25.0 | 325 | 0.6931 | 0.4508 | 0.4153 | |
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| No log | 25.38 | 330 | 0.6932 | 0.4558 | 0.4185 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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