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scenario-TCR-XLMV-XCOPA-5_data-xcopa_all

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PyTorch
xlm-roberta
Generated from Trainer
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  1. README.md +69 -47
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -19,8 +19,8 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.5125
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- - F1: 0.4891
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  ## Model description
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@@ -51,51 +51,73 @@ The following hyperparameters were used during training:
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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 | 0.6925 | 0.5333 | 0.5246 |
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- | No log | 0.77 | 10 | 0.6931 | 0.4867 | 0.4680 |
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- | No log | 1.15 | 15 | 0.6932 | 0.475 | 0.4483 |
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- | No log | 1.54 | 20 | 0.6932 | 0.4658 | 0.4241 |
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- | No log | 1.92 | 25 | 0.6932 | 0.4975 | 0.4724 |
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- | No log | 2.31 | 30 | 0.6931 | 0.5175 | 0.4970 |
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- | No log | 2.69 | 35 | 0.6931 | 0.5142 | 0.4987 |
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- | No log | 3.08 | 40 | 0.6931 | 0.5167 | 0.5026 |
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- | No log | 3.46 | 45 | 0.6931 | 0.53 | 0.5146 |
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- | No log | 3.85 | 50 | 0.6931 | 0.5492 | 0.5292 |
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- | No log | 4.23 | 55 | 0.6931 | 0.5517 | 0.5394 |
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- | No log | 4.62 | 60 | 0.6931 | 0.5508 | 0.5357 |
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- | No log | 5.0 | 65 | 0.6931 | 0.5533 | 0.5387 |
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- | No log | 5.38 | 70 | 0.6930 | 0.5592 | 0.5428 |
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- | No log | 5.77 | 75 | 0.6930 | 0.5608 | 0.5453 |
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- | No log | 6.15 | 80 | 0.6931 | 0.54 | 0.5258 |
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- | No log | 6.54 | 85 | 0.6933 | 0.4958 | 0.4860 |
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- | No log | 6.92 | 90 | 0.6931 | 0.5308 | 0.4987 |
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- | No log | 7.31 | 95 | 0.6931 | 0.53 | 0.5130 |
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- | No log | 7.69 | 100 | 0.6931 | 0.5292 | 0.5074 |
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- | No log | 8.08 | 105 | 0.6931 | 0.5358 | 0.5101 |
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- | No log | 8.46 | 110 | 0.6931 | 0.5225 | 0.4943 |
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- | No log | 8.85 | 115 | 0.6925 | 0.5575 | 0.5354 |
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- | No log | 9.23 | 120 | 0.6931 | 0.5417 | 0.5250 |
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- | No log | 9.62 | 125 | 0.6931 | 0.5133 | 0.4804 |
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- | No log | 10.0 | 130 | 0.6931 | 0.5358 | 0.5004 |
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- | No log | 10.38 | 135 | 0.6931 | 0.5425 | 0.5163 |
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- | No log | 10.77 | 140 | 0.6931 | 0.5433 | 0.5142 |
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- | No log | 11.15 | 145 | 0.6931 | 0.5425 | 0.5103 |
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- | No log | 11.54 | 150 | 0.6931 | 0.5467 | 0.5099 |
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- | No log | 11.92 | 155 | 0.6931 | 0.5358 | 0.4986 |
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- | No log | 12.31 | 160 | 0.6931 | 0.5275 | 0.4841 |
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- | No log | 12.69 | 165 | 0.6931 | 0.5192 | 0.4825 |
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- | No log | 13.08 | 170 | 0.6931 | 0.5283 | 0.4910 |
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- | No log | 13.46 | 175 | 0.6930 | 0.5508 | 0.5131 |
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- | No log | 13.85 | 180 | 0.6930 | 0.5542 | 0.5303 |
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- | No log | 14.23 | 185 | 0.6932 | 0.4908 | 0.4664 |
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- | No log | 14.62 | 190 | 0.6931 | 0.5075 | 0.4802 |
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- | No log | 15.0 | 195 | 0.6932 | 0.5083 | 0.4806 |
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- | No log | 15.38 | 200 | 0.6932 | 0.4625 | 0.4377 |
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- | No log | 15.77 | 205 | 0.6932 | 0.48 | 0.4694 |
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- | No log | 16.15 | 210 | 0.6932 | 0.49 | 0.4742 |
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- | No log | 16.54 | 215 | 0.6931 | 0.5333 | 0.5130 |
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- | No log | 16.92 | 220 | 0.6931 | 0.5217 | 0.4884 |
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- | No log | 17.31 | 225 | 0.6931 | 0.5125 | 0.4891 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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.
20
  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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51
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
53
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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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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