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scenario-TCR-XLMV-XCOPA-2_data-xcopa_all

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PyTorch
xlm-roberta
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  1. README.md +47 -64
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -18,9 +18,9 @@ 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: 1.3263
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- - Accuracy: 0.5417
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- - F1: 0.5291
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  ## Model description
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@@ -51,67 +51,50 @@ 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 | 0.6932 | 0.5075 | 0.4793 |
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- | No log | 0.77 | 10 | 0.6932 | 0.5025 | 0.4749 |
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- | No log | 1.15 | 15 | 0.6931 | 0.51 | 0.4878 |
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- | No log | 1.54 | 20 | 0.6931 | 0.5192 | 0.4934 |
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- | No log | 1.92 | 25 | 0.6931 | 0.51 | 0.4869 |
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- | No log | 2.31 | 30 | 0.6931 | 0.5417 | 0.5089 |
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- | No log | 2.69 | 35 | 0.6931 | 0.5533 | 0.5323 |
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- | No log | 3.08 | 40 | 0.6931 | 0.5542 | 0.5352 |
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- | No log | 3.46 | 45 | 0.6931 | 0.55 | 0.5329 |
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- | No log | 3.85 | 50 | 0.6931 | 0.5475 | 0.5233 |
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- | No log | 4.23 | 55 | 0.6931 | 0.5217 | 0.4884 |
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- | No log | 4.62 | 60 | 0.6932 | 0.5283 | 0.5052 |
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- | No log | 5.0 | 65 | 0.6931 | 0.5308 | 0.4969 |
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- | No log | 5.38 | 70 | 0.6930 | 0.5217 | 0.4929 |
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- | No log | 5.77 | 75 | 0.6930 | 0.53 | 0.5138 |
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- | No log | 6.15 | 80 | 0.6931 | 0.5417 | 0.5259 |
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- | No log | 6.54 | 85 | 0.6931 | 0.5525 | 0.5318 |
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- | No log | 6.92 | 90 | 0.6931 | 0.5417 | 0.5054 |
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- | No log | 7.31 | 95 | 0.6931 | 0.56 | 0.5319 |
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- | No log | 7.69 | 100 | 0.6930 | 0.555 | 0.5291 |
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- | No log | 8.08 | 105 | 0.6931 | 0.5192 | 0.4978 |
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- | No log | 8.46 | 110 | 0.6931 | 0.5192 | 0.4943 |
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- | No log | 8.85 | 115 | 0.6930 | 0.5383 | 0.5249 |
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- | No log | 9.23 | 120 | 0.6930 | 0.5375 | 0.5244 |
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- | No log | 9.62 | 125 | 0.6930 | 0.54 | 0.5192 |
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- | No log | 10.0 | 130 | 0.6930 | 0.5617 | 0.5450 |
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- | No log | 10.38 | 135 | 0.6930 | 0.54 | 0.5217 |
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- | No log | 10.77 | 140 | 0.6929 | 0.5475 | 0.5241 |
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- | No log | 11.15 | 145 | 0.6926 | 0.57 | 0.5559 |
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- | No log | 11.54 | 150 | 0.6920 | 0.5658 | 0.5611 |
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- | No log | 11.92 | 155 | 0.6896 | 0.5758 | 0.5661 |
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- | No log | 12.31 | 160 | 0.6879 | 0.5275 | 0.5074 |
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- | No log | 12.69 | 165 | 0.7069 | 0.4792 | 0.4435 |
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- | No log | 13.08 | 170 | 0.6925 | 0.5358 | 0.5276 |
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- | No log | 13.46 | 175 | 0.6941 | 0.5275 | 0.5082 |
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- | No log | 13.85 | 180 | 0.7034 | 0.52 | 0.5052 |
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- | No log | 14.23 | 185 | 0.7386 | 0.54 | 0.5208 |
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- | No log | 14.62 | 190 | 0.7108 | 0.5242 | 0.4987 |
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- | No log | 15.0 | 195 | 0.7418 | 0.5275 | 0.4978 |
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- | No log | 15.38 | 200 | 0.8982 | 0.5292 | 0.5074 |
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- | No log | 15.77 | 205 | 0.8073 | 0.5525 | 0.5398 |
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- | No log | 16.15 | 210 | 1.0723 | 0.5633 | 0.5459 |
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- | No log | 16.54 | 215 | 0.8696 | 0.5508 | 0.5293 |
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- | No log | 16.92 | 220 | 0.8563 | 0.5533 | 0.5315 |
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- | No log | 17.31 | 225 | 1.1253 | 0.5317 | 0.5147 |
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- | No log | 17.69 | 230 | 1.0949 | 0.5483 | 0.5279 |
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- | No log | 18.08 | 235 | 0.8768 | 0.5592 | 0.5482 |
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- | No log | 18.46 | 240 | 1.1880 | 0.5558 | 0.5401 |
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- | No log | 18.85 | 245 | 1.1781 | 0.5617 | 0.5434 |
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- | No log | 19.23 | 250 | 0.9759 | 0.5508 | 0.5381 |
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- | No log | 19.62 | 255 | 1.2402 | 0.5442 | 0.5321 |
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- | No log | 20.0 | 260 | 1.2534 | 0.5308 | 0.5048 |
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- | No log | 20.38 | 265 | 1.1987 | 0.5567 | 0.5382 |
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- | No log | 20.77 | 270 | 1.1627 | 0.5425 | 0.5344 |
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- | No log | 21.15 | 275 | 1.2200 | 0.5417 | 0.5133 |
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- | No log | 21.54 | 280 | 1.4727 | 0.5342 | 0.5152 |
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- | No log | 21.92 | 285 | 1.1766 | 0.55 | 0.5271 |
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- | No log | 22.31 | 290 | 1.3099 | 0.5458 | 0.5265 |
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- | No log | 22.69 | 295 | 1.3408 | 0.5558 | 0.5361 |
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- | No log | 23.08 | 300 | 1.2960 | 0.5458 | 0.5164 |
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- | No log | 23.46 | 305 | 1.3263 | 0.5417 | 0.5291 |
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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.
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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: 0.5
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+ - F1: 0.4671
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  ## Model description
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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 | 0.4858 | 0.4767 |
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+ | No log | 0.77 | 10 | 0.6931 | 0.515 | 0.5134 |
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+ | No log | 1.15 | 15 | 0.6931 | 0.5158 | 0.5038 |
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+ | No log | 1.54 | 20 | 0.6931 | 0.5108 | 0.5021 |
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+ | No log | 1.92 | 25 | 0.6931 | 0.5217 | 0.5035 |
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+ | No log | 2.31 | 30 | 0.6931 | 0.525 | 0.5069 |
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+ | No log | 2.69 | 35 | 0.6931 | 0.5283 | 0.5070 |
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+ | No log | 3.08 | 40 | 0.6931 | 0.5292 | 0.5125 |
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+ | No log | 3.46 | 45 | 0.6931 | 0.5333 | 0.5122 |
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+ | No log | 3.85 | 50 | 0.6930 | 0.5125 | 0.4970 |
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+ | No log | 4.23 | 55 | 0.6930 | 0.5342 | 0.5251 |
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+ | No log | 4.62 | 60 | 0.6931 | 0.5417 | 0.5217 |
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+ | No log | 5.0 | 65 | 0.6931 | 0.5592 | 0.5482 |
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+ | No log | 5.38 | 70 | 0.6931 | 0.5667 | 0.5517 |
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+ | No log | 5.77 | 75 | 0.6931 | 0.5458 | 0.5362 |
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+ | No log | 6.15 | 80 | 0.6931 | 0.535 | 0.5311 |
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+ | No log | 6.54 | 85 | 0.6930 | 0.5433 | 0.5276 |
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+ | No log | 6.92 | 90 | 0.6931 | 0.5025 | 0.4731 |
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+ | No log | 7.31 | 95 | 0.6931 | 0.505 | 0.4715 |
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+ | No log | 7.69 | 100 | 0.6931 | 0.5017 | 0.4514 |
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+ | No log | 8.08 | 105 | 0.6931 | 0.5042 | 0.4831 |
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+ | No log | 8.46 | 110 | 0.6931 | 0.5058 | 0.4785 |
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+ | No log | 8.85 | 115 | 0.6931 | 0.5158 | 0.4872 |
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+ | No log | 9.23 | 120 | 0.6931 | 0.5158 | 0.4890 |
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+ | No log | 9.62 | 125 | 0.6931 | 0.5075 | 0.4829 |
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+ | No log | 10.0 | 130 | 0.6931 | 0.505 | 0.4780 |
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+ | No log | 10.38 | 135 | 0.6931 | 0.5 | 0.4709 |
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+ | No log | 10.77 | 140 | 0.6931 | 0.485 | 0.4579 |
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+ | No log | 11.15 | 145 | 0.6931 | 0.4858 | 0.4592 |
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+ | No log | 11.54 | 150 | 0.6931 | 0.485 | 0.4569 |
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+ | No log | 11.92 | 155 | 0.6931 | 0.4917 | 0.4611 |
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+ | No log | 12.31 | 160 | 0.6931 | 0.4908 | 0.4664 |
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+ | No log | 12.69 | 165 | 0.6931 | 0.4858 | 0.4602 |
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+ | No log | 13.08 | 170 | 0.6931 | 0.4983 | 0.4756 |
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+ | No log | 13.46 | 175 | 0.6931 | 0.4992 | 0.4788 |
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+ | No log | 13.85 | 180 | 0.6931 | 0.4942 | 0.4717 |
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+ | No log | 14.23 | 185 | 0.6931 | 0.4958 | 0.4735 |
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+ | No log | 14.62 | 190 | 0.6931 | 0.5017 | 0.48 |
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+ | No log | 15.0 | 195 | 0.6931 | 0.4942 | 0.4633 |
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+ | No log | 15.38 | 200 | 0.6931 | 0.4942 | 0.4527 |
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+ | No log | 15.77 | 205 | 0.6931 | 0.4925 | 0.4509 |
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+ | No log | 16.15 | 210 | 0.6931 | 0.495 | 0.4570 |
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+ | No log | 16.54 | 215 | 0.6931 | 0.4933 | 0.4581 |
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+ | No log | 16.92 | 220 | 0.6931 | 0.5 | 0.4671 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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