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--- |
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license: mit |
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base_model: facebook/xlm-v-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-XLMV-XCOPA-5_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-XLMV-XCOPA-5_data-xcopa_all |
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This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6931 |
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- Accuracy: 0.505 |
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- F1: 0.4396 |
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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: 214 |
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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 | 0.6937 | 0.5158 | 0.4808 | |
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| No log | 0.77 | 10 | 0.6929 | 0.4917 | 0.4777 | |
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| No log | 1.15 | 15 | 0.6933 | 0.5025 | 0.4919 | |
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| No log | 1.54 | 20 | 0.6945 | 0.5125 | 0.4734 | |
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| No log | 1.92 | 25 | 0.6931 | 0.4767 | 0.4442 | |
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| No log | 2.31 | 30 | 0.6931 | 0.5192 | 0.4898 | |
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| No log | 2.69 | 35 | 0.6931 | 0.5042 | 0.4644 | |
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| No log | 3.08 | 40 | 0.6931 | 0.5183 | 0.5085 | |
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| No log | 3.46 | 45 | 0.6931 | 0.5083 | 0.4896 | |
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| No log | 3.85 | 50 | 0.6931 | 0.5158 | 0.5004 | |
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| No log | 4.23 | 55 | 0.6931 | 0.5292 | 0.5031 | |
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| No log | 4.62 | 60 | 0.6931 | 0.5233 | 0.5077 | |
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| No log | 5.0 | 65 | 0.6931 | 0.5292 | 0.5083 | |
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| No log | 5.38 | 70 | 0.6931 | 0.5 | 0.4643 | |
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| No log | 5.77 | 75 | 0.6931 | 0.4983 | 0.4837 | |
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| No log | 6.15 | 80 | 0.6932 | 0.4708 | 0.4385 | |
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| No log | 6.54 | 85 | 0.6932 | 0.475 | 0.4375 | |
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| No log | 6.92 | 90 | 0.6932 | 0.4717 | 0.4379 | |
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| No log | 7.31 | 95 | 0.6932 | 0.4925 | 0.4518 | |
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| No log | 7.69 | 100 | 0.6932 | 0.4758 | 0.4448 | |
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| No log | 8.08 | 105 | 0.6932 | 0.4908 | 0.4617 | |
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| No log | 8.46 | 110 | 0.6931 | 0.5017 | 0.4613 | |
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| No log | 8.85 | 115 | 0.6931 | 0.4875 | 0.4591 | |
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| No log | 9.23 | 120 | 0.6931 | 0.4783 | 0.4371 | |
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| No log | 9.62 | 125 | 0.6932 | 0.4533 | 0.4164 | |
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| No log | 10.0 | 130 | 0.6932 | 0.475 | 0.4335 | |
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| No log | 10.38 | 135 | 0.6931 | 0.5242 | 0.4870 | |
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| No log | 10.77 | 140 | 0.6931 | 0.5258 | 0.4969 | |
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| No log | 11.15 | 145 | 0.6931 | 0.4808 | 0.4452 | |
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| No log | 11.54 | 150 | 0.6932 | 0.4408 | 0.3993 | |
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| No log | 11.92 | 155 | 0.6932 | 0.4375 | 0.3968 | |
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| No log | 12.31 | 160 | 0.6932 | 0.4325 | 0.3925 | |
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| No log | 12.69 | 165 | 0.6932 | 0.435 | 0.4084 | |
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| No log | 13.08 | 170 | 0.6932 | 0.4825 | 0.4557 | |
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| No log | 13.46 | 175 | 0.6931 | 0.4892 | 0.4541 | |
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| No log | 13.85 | 180 | 0.6931 | 0.5 | 0.4828 | |
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| No log | 14.23 | 185 | 0.6931 | 0.5325 | 0.5109 | |
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| No log | 14.62 | 190 | 0.6931 | 0.5367 | 0.5071 | |
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| No log | 15.0 | 195 | 0.6932 | 0.47 | 0.4291 | |
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| No log | 15.38 | 200 | 0.6932 | 0.4483 | 0.4193 | |
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| No log | 15.77 | 205 | 0.6932 | 0.4325 | 0.4052 | |
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| No log | 16.15 | 210 | 0.6932 | 0.47 | 0.4411 | |
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| No log | 16.54 | 215 | 0.6932 | 0.47 | 0.4441 | |
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| No log | 16.92 | 220 | 0.6932 | 0.4567 | 0.4210 | |
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| No log | 17.31 | 225 | 0.6932 | 0.4408 | 0.4067 | |
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| No log | 17.69 | 230 | 0.6932 | 0.44 | 0.4032 | |
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| No log | 18.08 | 235 | 0.6932 | 0.455 | 0.4233 | |
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| No log | 18.46 | 240 | 0.6932 | 0.4558 | 0.4297 | |
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| No log | 18.85 | 245 | 0.6932 | 0.4542 | 0.4239 | |
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| No log | 19.23 | 250 | 0.6932 | 0.4783 | 0.4431 | |
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| No log | 19.62 | 255 | 0.6931 | 0.4908 | 0.4664 | |
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| No log | 20.0 | 260 | 0.6932 | 0.4825 | 0.4567 | |
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| No log | 20.38 | 265 | 0.6932 | 0.4775 | 0.4581 | |
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| No log | 20.77 | 270 | 0.6946 | 0.495 | 0.4580 | |
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| No log | 21.15 | 275 | 0.6931 | 0.5208 | 0.4806 | |
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| No log | 21.54 | 280 | 0.6931 | 0.5 | 0.4652 | |
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| No log | 21.92 | 285 | 0.6931 | 0.5067 | 0.4426 | |
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| No log | 22.31 | 290 | 0.6931 | 0.4908 | 0.3944 | |
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| No log | 22.69 | 295 | 0.6931 | 0.5317 | 0.4458 | |
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| No log | 23.08 | 300 | 0.6931 | 0.4933 | 0.4109 | |
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| No log | 23.46 | 305 | 0.6931 | 0.5025 | 0.4176 | |
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| No log | 23.85 | 310 | 0.6931 | 0.5033 | 0.4601 | |
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| No log | 24.23 | 315 | 0.6931 | 0.5017 | 0.4305 | |
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| No log | 24.62 | 320 | 0.6931 | 0.4883 | 0.4408 | |
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| No log | 25.0 | 325 | 0.6931 | 0.5267 | 0.4799 | |
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| No log | 25.38 | 330 | 0.6931 | 0.5083 | 0.4370 | |
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| No log | 25.77 | 335 | 0.6931 | 0.505 | 0.4396 | |
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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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