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scenario-TCR-XLMV-XCOPA-1_data-xcopa_all

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Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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  1. README.md +124 -74
  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. -->
19
  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:
21
  - Loss: 0.6931
22
- - Accuracy: 0.5325
23
- - F1: 0.5022
24
 
25
  ## Model description
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@@ -51,78 +51,128 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
53
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
54
- | No log | 0.38 | 5 | 0.6930 | 0.4967 | 0.4881 |
55
- | No log | 0.77 | 10 | 0.6932 | 0.4933 | 0.4786 |
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- | No log | 1.15 | 15 | 0.6930 | 0.5017 | 0.4754 |
57
- | No log | 1.54 | 20 | 0.6932 | 0.49 | 0.4613 |
58
- | No log | 1.92 | 25 | 0.6932 | 0.4858 | 0.4592 |
59
- | No log | 2.31 | 30 | 0.6932 | 0.4617 | 0.4323 |
60
- | No log | 2.69 | 35 | 0.6932 | 0.4708 | 0.4540 |
61
- | No log | 3.08 | 40 | 0.6932 | 0.4583 | 0.4288 |
62
- | No log | 3.46 | 45 | 0.6932 | 0.4725 | 0.4529 |
63
- | No log | 3.85 | 50 | 0.6931 | 0.4817 | 0.4601 |
64
- | No log | 4.23 | 55 | 0.6931 | 0.4975 | 0.4842 |
65
- | No log | 4.62 | 60 | 0.6931 | 0.5042 | 0.4884 |
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- | No log | 5.0 | 65 | 0.6931 | 0.5067 | 0.4957 |
67
- | No log | 5.38 | 70 | 0.6931 | 0.4992 | 0.4859 |
68
- | No log | 5.77 | 75 | 0.6931 | 0.5117 | 0.5034 |
69
- | No log | 6.15 | 80 | 0.6930 | 0.5325 | 0.5031 |
70
- | No log | 6.54 | 85 | 0.6931 | 0.5008 | 0.4823 |
71
- | No log | 6.92 | 90 | 0.6939 | 0.5133 | 0.4859 |
72
- | No log | 7.31 | 95 | 0.6937 | 0.4725 | 0.4510 |
73
- | No log | 7.69 | 100 | 0.6931 | 0.4975 | 0.4611 |
74
- | No log | 8.08 | 105 | 0.6931 | 0.5133 | 0.4767 |
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- | No log | 8.46 | 110 | 0.6931 | 0.4883 | 0.4468 |
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- | No log | 8.85 | 115 | 0.6931 | 0.5092 | 0.4736 |
77
- | No log | 9.23 | 120 | 0.6931 | 0.5383 | 0.5265 |
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- | No log | 9.62 | 125 | 0.6931 | 0.535 | 0.5223 |
79
- | No log | 10.0 | 130 | 0.6931 | 0.4792 | 0.4551 |
80
- | No log | 10.38 | 135 | 0.6931 | 0.4808 | 0.4597 |
81
- | No log | 10.77 | 140 | 0.6931 | 0.4917 | 0.4732 |
82
- | No log | 11.15 | 145 | 0.6931 | 0.4967 | 0.4784 |
83
- | No log | 11.54 | 150 | 0.6931 | 0.4883 | 0.4689 |
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- | No log | 11.92 | 155 | 0.6931 | 0.4875 | 0.4657 |
85
- | No log | 12.31 | 160 | 0.6931 | 0.4942 | 0.4790 |
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- | No log | 12.69 | 165 | 0.6931 | 0.4917 | 0.4795 |
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- | No log | 13.08 | 170 | 0.6931 | 0.5183 | 0.5085 |
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- | No log | 13.46 | 175 | 0.6931 | 0.4983 | 0.4828 |
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- | No log | 13.85 | 180 | 0.6931 | 0.4883 | 0.4670 |
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- | No log | 14.23 | 185 | 0.6931 | 0.5242 | 0.5140 |
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- | No log | 14.62 | 190 | 0.6931 | 0.4967 | 0.4775 |
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- | No log | 15.0 | 195 | 0.6931 | 0.495 | 0.4776 |
93
- | No log | 15.38 | 200 | 0.6931 | 0.5192 | 0.5114 |
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- | No log | 15.77 | 205 | 0.6931 | 0.53 | 0.53 |
95
- | No log | 16.15 | 210 | 0.6931 | 0.5358 | 0.5315 |
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- | No log | 16.54 | 215 | 0.6931 | 0.5242 | 0.5099 |
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- | No log | 16.92 | 220 | 0.6931 | 0.5258 | 0.5166 |
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- | No log | 17.31 | 225 | 0.6931 | 0.4742 | 0.4584 |
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- | No log | 17.69 | 230 | 0.6931 | 0.4783 | 0.4575 |
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- | No log | 18.08 | 235 | 0.6931 | 0.4775 | 0.4514 |
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- | No log | 18.46 | 240 | 0.6931 | 0.4742 | 0.4450 |
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- | No log | 18.85 | 245 | 0.6931 | 0.4742 | 0.4431 |
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- | No log | 19.23 | 250 | 0.6931 | 0.4692 | 0.4437 |
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- | No log | 19.62 | 255 | 0.6931 | 0.4725 | 0.4519 |
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- | No log | 20.0 | 260 | 0.6931 | 0.4733 | 0.4504 |
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- | No log | 20.38 | 265 | 0.6931 | 0.4717 | 0.4477 |
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- | No log | 20.77 | 270 | 0.6931 | 0.4725 | 0.4500 |
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- | No log | 21.15 | 275 | 0.6931 | 0.4692 | 0.4466 |
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- | No log | 21.54 | 280 | 0.6931 | 0.4658 | 0.4392 |
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- | No log | 21.92 | 285 | 0.6931 | 0.4725 | 0.4452 |
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- | No log | 22.31 | 290 | 0.6931 | 0.4775 | 0.4524 |
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- | No log | 22.69 | 295 | 0.6931 | 0.4792 | 0.4532 |
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- | No log | 23.08 | 300 | 0.6931 | 0.4717 | 0.4429 |
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- | No log | 23.46 | 305 | 0.6931 | 0.4775 | 0.4476 |
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- | No log | 23.85 | 310 | 0.6931 | 0.4742 | 0.4431 |
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- | No log | 24.23 | 315 | 0.6931 | 0.4725 | 0.4452 |
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- | No log | 24.62 | 320 | 0.6931 | 0.475 | 0.4454 |
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- | No log | 25.0 | 325 | 0.6931 | 0.4717 | 0.4429 |
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- | No log | 25.38 | 330 | 0.6931 | 0.4742 | 0.4470 |
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- | No log | 25.77 | 335 | 0.6931 | 0.4758 | 0.4468 |
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- | No log | 26.15 | 340 | 0.6931 | 0.4717 | 0.4544 |
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- | No log | 26.54 | 345 | 0.6931 | 0.5358 | 0.5144 |
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- | No log | 26.92 | 350 | 0.6931 | 0.5342 | 0.5057 |
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- | No log | 27.31 | 355 | 0.6931 | 0.5292 | 0.5013 |
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- | No log | 27.69 | 360 | 0.6931 | 0.5325 | 0.5022 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
126
 
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128
  ### Framework versions
 
19
  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:
21
  - Loss: 0.6931
22
+ - Accuracy: 0.5592
23
+ - F1: 0.5289
24
 
25
  ## Model description
26
 
 
51
 
52
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
53
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
54
+ | No log | 0.38 | 5 | 0.6932 | 0.4917 | 0.4383 |
55
+ | No log | 0.77 | 10 | 0.6931 | 0.5192 | 0.5064 |
56
+ | No log | 1.15 | 15 | 0.6931 | 0.5017 | 0.4613 |
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+ | No log | 1.54 | 20 | 0.6932 | 0.4942 | 0.4576 |
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+ | No log | 1.92 | 25 | 0.6931 | 0.505 | 0.4629 |
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+ | No log | 2.31 | 30 | 0.6931 | 0.5 | 0.4643 |
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+ | No log | 2.69 | 35 | 0.6931 | 0.4892 | 0.4580 |
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+ | No log | 3.08 | 40 | 0.6931 | 0.4833 | 0.4552 |
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+ | No log | 3.46 | 45 | 0.6932 | 0.4967 | 0.4588 |
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+ | No log | 3.85 | 50 | 0.6931 | 0.5042 | 0.4711 |
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+ | No log | 4.23 | 55 | 0.6931 | 0.5108 | 0.4846 |
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+ | No log | 4.62 | 60 | 0.6932 | 0.4875 | 0.4591 |
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+ | No log | 5.0 | 65 | 0.6931 | 0.4958 | 0.4641 |
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+ | No log | 5.38 | 70 | 0.6931 | 0.4933 | 0.4777 |
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+ | No log | 5.77 | 75 | 0.6931 | 0.5075 | 0.4901 |
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+ | No log | 6.15 | 80 | 0.6931 | 0.4833 | 0.4464 |
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+ | No log | 6.54 | 85 | 0.6931 | 0.5175 | 0.4917 |
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+ | No log | 6.92 | 90 | 0.6931 | 0.4442 | 0.4225 |
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+ | No log | 7.31 | 95 | 0.6931 | 0.4583 | 0.4377 |
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+ | No log | 7.69 | 100 | 0.6931 | 0.5192 | 0.4978 |
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+ | No log | 8.08 | 105 | 0.6931 | 0.5425 | 0.5230 |
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+ | No log | 8.46 | 110 | 0.6931 | 0.535 | 0.5122 |
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+ | No log | 8.85 | 115 | 0.6931 | 0.545 | 0.5194 |
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+ | No log | 9.23 | 120 | 0.6931 | 0.5492 | 0.5259 |
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+ | No log | 9.62 | 125 | 0.6931 | 0.535 | 0.5114 |
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+ | No log | 10.0 | 130 | 0.6931 | 0.5475 | 0.5233 |
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+ | No log | 10.38 | 135 | 0.6931 | 0.5525 | 0.5269 |
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+ | No log | 10.77 | 140 | 0.6931 | 0.5458 | 0.5223 |
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+ | No log | 11.15 | 145 | 0.6931 | 0.5392 | 0.5145 |
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+ | No log | 11.54 | 150 | 0.6931 | 0.5483 | 0.5246 |
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+ | No log | 11.92 | 155 | 0.6931 | 0.5342 | 0.5084 |
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+ | No log | 12.31 | 160 | 0.6931 | 0.54 | 0.5158 |
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+ | No log | 12.69 | 165 | 0.6931 | 0.5375 | 0.5084 |
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+ | No log | 13.08 | 170 | 0.6931 | 0.5433 | 0.5133 |
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+ | No log | 13.46 | 175 | 0.6931 | 0.5333 | 0.5096 |
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+ | No log | 13.85 | 180 | 0.6931 | 0.5458 | 0.5215 |
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+ | No log | 14.23 | 185 | 0.6931 | 0.5508 | 0.5259 |
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+ | No log | 14.62 | 190 | 0.6931 | 0.5433 | 0.5168 |
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+ | No log | 15.0 | 195 | 0.6931 | 0.55 | 0.5280 |
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+ | No log | 15.38 | 200 | 0.6931 | 0.5442 | 0.5231 |
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+ | No log | 15.77 | 205 | 0.6931 | 0.55 | 0.5280 |
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+ | No log | 16.15 | 210 | 0.6931 | 0.5458 | 0.5257 |
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+ | No log | 16.54 | 215 | 0.6931 | 0.5392 | 0.5195 |
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+ | No log | 16.92 | 220 | 0.6931 | 0.5367 | 0.5165 |
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+ | No log | 17.31 | 225 | 0.6931 | 0.5433 | 0.5235 |
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+ | No log | 17.69 | 230 | 0.6931 | 0.55 | 0.5271 |
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+ | No log | 18.08 | 235 | 0.6931 | 0.5425 | 0.5222 |
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+ | No log | 18.46 | 240 | 0.6931 | 0.5417 | 0.5158 |
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+ | No log | 18.85 | 245 | 0.6931 | 0.4983 | 0.4719 |
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+ | No log | 19.23 | 250 | 0.6931 | 0.5483 | 0.5237 |
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+ | No log | 19.62 | 255 | 0.6931 | 0.5425 | 0.5230 |
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+ | No log | 20.0 | 260 | 0.6931 | 0.5467 | 0.5220 |
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+ | No log | 20.38 | 265 | 0.6931 | 0.5467 | 0.5220 |
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+ | No log | 20.77 | 270 | 0.6931 | 0.5508 | 0.5251 |
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+ | No log | 21.15 | 275 | 0.6931 | 0.555 | 0.5283 |
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+ | No log | 21.54 | 280 | 0.6931 | 0.5533 | 0.5257 |
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+ | No log | 21.92 | 285 | 0.6931 | 0.555 | 0.5283 |
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+ | No log | 22.31 | 290 | 0.6931 | 0.5533 | 0.5298 |
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+ | No log | 22.69 | 295 | 0.6931 | 0.5517 | 0.5281 |
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+ | No log | 23.08 | 300 | 0.6931 | 0.5567 | 0.5325 |
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+ | No log | 23.46 | 305 | 0.6931 | 0.55 | 0.5288 |
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+ | No log | 23.85 | 310 | 0.6931 | 0.5475 | 0.5233 |
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+ | No log | 24.23 | 315 | 0.6931 | 0.5467 | 0.5220 |
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+ | No log | 24.62 | 320 | 0.6931 | 0.55 | 0.5246 |
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+ | No log | 25.0 | 325 | 0.6931 | 0.5483 | 0.5212 |
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+ | No log | 25.38 | 330 | 0.6931 | 0.5467 | 0.5203 |
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+ | No log | 25.77 | 335 | 0.6931 | 0.5483 | 0.5204 |
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+ | No log | 26.15 | 340 | 0.6931 | 0.5492 | 0.5225 |
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+ | No log | 26.54 | 345 | 0.6931 | 0.5492 | 0.5250 |
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+ | No log | 26.92 | 350 | 0.6931 | 0.5542 | 0.5295 |
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+ | No log | 27.31 | 355 | 0.6931 | 0.5567 | 0.5350 |
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+ | No log | 27.69 | 360 | 0.6931 | 0.5533 | 0.5290 |
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+ | No log | 28.08 | 365 | 0.6931 | 0.5558 | 0.5296 |
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+ | No log | 28.46 | 370 | 0.6931 | 0.5542 | 0.5270 |
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+ | No log | 28.85 | 375 | 0.6931 | 0.5383 | 0.5166 |
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+ | No log | 29.23 | 380 | 0.6931 | 0.5483 | 0.5220 |
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+ | No log | 29.62 | 385 | 0.6931 | 0.5475 | 0.5190 |
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+ | No log | 30.0 | 390 | 0.6931 | 0.5483 | 0.5212 |
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+ | No log | 30.38 | 395 | 0.6931 | 0.5208 | 0.4871 |
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+ | No log | 30.77 | 400 | 0.6931 | 0.4867 | 0.4690 |
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+ | No log | 31.15 | 405 | 0.6931 | 0.485 | 0.4663 |
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+ | No log | 31.54 | 410 | 0.6931 | 0.455 | 0.4313 |
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+ | No log | 31.92 | 415 | 0.6931 | 0.4608 | 0.4369 |
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+ | No log | 32.31 | 420 | 0.6931 | 0.4617 | 0.4421 |
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+ | No log | 32.69 | 425 | 0.6931 | 0.5258 | 0.4942 |
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+ | No log | 33.08 | 430 | 0.6931 | 0.5608 | 0.5340 |
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+ | No log | 33.46 | 435 | 0.6931 | 0.5583 | 0.5310 |
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+ | No log | 33.85 | 440 | 0.6931 | 0.56 | 0.5352 |
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+ | No log | 34.23 | 445 | 0.6931 | 0.5567 | 0.5325 |
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+ | No log | 34.62 | 450 | 0.6931 | 0.5525 | 0.5277 |
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+ | No log | 35.0 | 455 | 0.6931 | 0.5542 | 0.5303 |
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+ | No log | 35.38 | 460 | 0.6931 | 0.5633 | 0.5379 |
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+ | No log | 35.77 | 465 | 0.6931 | 0.5542 | 0.5295 |
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+ | No log | 36.15 | 470 | 0.6931 | 0.5567 | 0.5309 |
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+ | No log | 36.54 | 475 | 0.6931 | 0.555 | 0.5291 |
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+ | No log | 36.92 | 480 | 0.6931 | 0.5575 | 0.5330 |
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+ | No log | 37.31 | 485 | 0.6931 | 0.5517 | 0.5256 |
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+ | No log | 37.69 | 490 | 0.6931 | 0.545 | 0.5168 |
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+ | No log | 38.08 | 495 | 0.6931 | 0.54 | 0.5132 |
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+ | 0.6936 | 38.46 | 500 | 0.6931 | 0.55 | 0.5238 |
154
+ | 0.6936 | 38.85 | 505 | 0.6931 | 0.5425 | 0.512 |
155
+ | 0.6936 | 39.23 | 510 | 0.6931 | 0.54 | 0.5106 |
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+ | 0.6936 | 39.62 | 515 | 0.6931 | 0.5242 | 0.4906 |
157
+ | 0.6936 | 40.0 | 520 | 0.6931 | 0.5292 | 0.4978 |
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+ | 0.6936 | 40.38 | 525 | 0.6931 | 0.53 | 0.5009 |
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+ | 0.6936 | 40.77 | 530 | 0.6931 | 0.5308 | 0.5031 |
160
+ | 0.6936 | 41.15 | 535 | 0.6931 | 0.5425 | 0.5205 |
161
+ | 0.6936 | 41.54 | 540 | 0.6931 | 0.535 | 0.5088 |
162
+ | 0.6936 | 41.92 | 545 | 0.6931 | 0.5342 | 0.5084 |
163
+ | 0.6936 | 42.31 | 550 | 0.6931 | 0.5425 | 0.5205 |
164
+ | 0.6936 | 42.69 | 555 | 0.6931 | 0.5475 | 0.5241 |
165
+ | 0.6936 | 43.08 | 560 | 0.6931 | 0.5517 | 0.5264 |
166
+ | 0.6936 | 43.46 | 565 | 0.6931 | 0.5592 | 0.5339 |
167
+ | 0.6936 | 43.85 | 570 | 0.6931 | 0.5625 | 0.5350 |
168
+ | 0.6936 | 44.23 | 575 | 0.6931 | 0.5625 | 0.5358 |
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+ | 0.6936 | 44.62 | 580 | 0.6931 | 0.5617 | 0.5337 |
170
+ | 0.6936 | 45.0 | 585 | 0.6931 | 0.5633 | 0.5355 |
171
+ | 0.6936 | 45.38 | 590 | 0.6931 | 0.56 | 0.5344 |
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+ | 0.6936 | 45.77 | 595 | 0.6931 | 0.5625 | 0.5350 |
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+ | 0.6936 | 46.15 | 600 | 0.6931 | 0.555 | 0.5258 |
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+ | 0.6936 | 46.54 | 605 | 0.6931 | 0.5625 | 0.5350 |
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+ | 0.6936 | 46.92 | 610 | 0.6931 | 0.5592 | 0.5289 |
176
 
177
 
178
  ### Framework versions
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