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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-1_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-1_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: 0.5592 |
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- F1: 0.5289 |
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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 | 0.4917 | 0.4383 | |
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| No log | 0.77 | 10 | 0.6931 | 0.5192 | 0.5064 | |
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| 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 | |
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| 0.6936 | 38.85 | 505 | 0.6931 | 0.5425 | 0.512 | |
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| 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 | |
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| 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 | |
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| 0.6936 | 41.15 | 535 | 0.6931 | 0.5425 | 0.5205 | |
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| 0.6936 | 41.54 | 540 | 0.6931 | 0.535 | 0.5088 | |
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| 0.6936 | 41.92 | 545 | 0.6931 | 0.5342 | 0.5084 | |
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| 0.6936 | 42.31 | 550 | 0.6931 | 0.5425 | 0.5205 | |
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| 0.6936 | 42.69 | 555 | 0.6931 | 0.5475 | 0.5241 | |
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| 0.6936 | 43.08 | 560 | 0.6931 | 0.5517 | 0.5264 | |
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| 0.6936 | 43.46 | 565 | 0.6931 | 0.5592 | 0.5339 | |
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| 0.6936 | 43.85 | 570 | 0.6931 | 0.5625 | 0.5350 | |
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| 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 | |
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| 0.6936 | 45.0 | 585 | 0.6931 | 0.5633 | 0.5355 | |
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| 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 | |
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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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