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--- |
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license: mit |
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base_model: roberta-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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- recall |
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- precision |
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model-index: |
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- name: default |
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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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# default |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1946 |
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- Accuracy: 0.9395 |
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- F1: 0.9398 |
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- Recall: 0.9395 |
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- Precision: 0.9413 |
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- Combined Score: 0.9400 |
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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: 0.0005 |
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- train_batch_size: 128 |
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- eval_batch_size: 512 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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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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- lr_scheduler_warmup_steps: 10000 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:--------------:| |
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| 1.0664 | 0.06 | 20 | 1.0651 | 0.4448 | 0.2739 | 0.4448 | 0.1978 | 0.3403 | |
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| 1.0423 | 0.12 | 40 | 1.0188 | 0.5034 | 0.4138 | 0.5034 | 0.4328 | 0.4633 | |
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| 1.0137 | 0.18 | 60 | 0.9871 | 0.5279 | 0.4596 | 0.5279 | 0.4219 | 0.4843 | |
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| 1.0027 | 0.24 | 80 | 0.9889 | 0.5308 | 0.4653 | 0.5308 | 0.4196 | 0.4866 | |
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| 0.9914 | 0.3 | 100 | 0.9763 | 0.5308 | 0.4666 | 0.5308 | 0.4176 | 0.4864 | |
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| 0.9826 | 0.36 | 120 | 0.9713 | 0.5388 | 0.4711 | 0.5388 | 0.4292 | 0.4945 | |
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| 0.9788 | 0.42 | 140 | 0.9766 | 0.5313 | 0.4674 | 0.5313 | 0.4186 | 0.4871 | |
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| 0.984 | 0.48 | 160 | 0.9590 | 0.5398 | 0.4751 | 0.5398 | 0.4243 | 0.4948 | |
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| 0.9694 | 0.54 | 180 | 0.9535 | 0.5423 | 0.4772 | 0.5423 | 0.4269 | 0.4972 | |
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| 0.9676 | 0.6 | 200 | 0.9274 | 0.5672 | 0.4991 | 0.5672 | 0.4467 | 0.5200 | |
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| 0.9753 | 0.66 | 220 | 0.9126 | 0.5736 | 0.5026 | 0.5736 | 0.4616 | 0.5279 | |
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| 0.9557 | 0.72 | 240 | 0.9053 | 0.5760 | 0.5069 | 0.5760 | 0.4532 | 0.5280 | |
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| 0.9508 | 0.78 | 260 | 0.9179 | 0.5767 | 0.5018 | 0.5767 | 0.4811 | 0.5341 | |
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| 0.9355 | 0.84 | 280 | 0.8937 | 0.5892 | 0.5183 | 0.5892 | 0.4662 | 0.5407 | |
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| 0.9 | 0.9 | 300 | 0.8469 | 0.6130 | 0.5579 | 0.6130 | 0.5855 | 0.5924 | |
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| 0.993 | 0.96 | 320 | 0.8615 | 0.6047 | 0.5352 | 0.6047 | 0.6175 | 0.5905 | |
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| 0.8527 | 1.02 | 340 | 0.7896 | 0.6439 | 0.6212 | 0.6439 | 0.6240 | 0.6333 | |
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| 0.966 | 1.08 | 360 | 1.0124 | 0.5316 | 0.4944 | 0.5316 | 0.6428 | 0.5501 | |
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| 0.8441 | 1.14 | 380 | 0.7911 | 0.6489 | 0.6371 | 0.6489 | 0.6584 | 0.6483 | |
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| 0.8226 | 1.2 | 400 | 0.7472 | 0.6700 | 0.6459 | 0.6700 | 0.6625 | 0.6621 | |
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| 0.7948 | 1.26 | 420 | 0.7664 | 0.6581 | 0.6095 | 0.6581 | 0.6796 | 0.6513 | |
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| 0.7428 | 1.32 | 440 | 0.6994 | 0.6992 | 0.6533 | 0.6992 | 0.7104 | 0.6905 | |
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| 0.7109 | 1.38 | 460 | 0.6511 | 0.7284 | 0.6890 | 0.7284 | 0.7345 | 0.7201 | |
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| 0.6882 | 1.44 | 480 | 0.5988 | 0.7577 | 0.7336 | 0.7577 | 0.7570 | 0.7515 | |
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| 0.7296 | 1.5 | 500 | 0.5993 | 0.7564 | 0.7669 | 0.7564 | 0.7902 | 0.7675 | |
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| 0.5677 | 1.57 | 520 | 0.5068 | 0.8126 | 0.7942 | 0.8126 | 0.8225 | 0.8105 | |
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| 0.5096 | 1.63 | 540 | 0.4273 | 0.8520 | 0.8449 | 0.8520 | 0.8511 | 0.8500 | |
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| 0.4452 | 1.69 | 560 | 0.3796 | 0.8722 | 0.8699 | 0.8722 | 0.8703 | 0.8711 | |
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| 0.3836 | 1.75 | 580 | 0.3855 | 0.8757 | 0.8758 | 0.8757 | 0.8778 | 0.8762 | |
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| 0.3783 | 1.81 | 600 | 0.3586 | 0.8894 | 0.8883 | 0.8894 | 0.8885 | 0.8889 | |
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| 0.3496 | 1.87 | 620 | 0.3210 | 0.8972 | 0.8973 | 0.8972 | 0.8974 | 0.8973 | |
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| 0.3585 | 1.93 | 640 | 0.3006 | 0.9035 | 0.9031 | 0.9035 | 0.9033 | 0.9034 | |
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| 0.345 | 1.99 | 660 | 0.3054 | 0.9014 | 0.9025 | 0.9014 | 0.9052 | 0.9026 | |
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| 0.3327 | 2.05 | 680 | 0.3174 | 0.8913 | 0.8866 | 0.8913 | 0.8955 | 0.8912 | |
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| 0.2962 | 2.11 | 700 | 0.2770 | 0.9122 | 0.9125 | 0.9122 | 0.9130 | 0.9125 | |
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| 0.3032 | 2.17 | 720 | 0.2979 | 0.9062 | 0.9055 | 0.9062 | 0.9093 | 0.9068 | |
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| 0.27 | 2.23 | 740 | 0.2973 | 0.8998 | 0.8971 | 0.8998 | 0.9045 | 0.9003 | |
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| 0.2912 | 2.29 | 760 | 0.2467 | 0.9222 | 0.9221 | 0.9222 | 0.9223 | 0.9222 | |
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| 0.2412 | 2.35 | 780 | 0.2761 | 0.9113 | 0.9128 | 0.9113 | 0.9173 | 0.9132 | |
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| 0.2746 | 2.41 | 800 | 0.2410 | 0.9260 | 0.9257 | 0.9260 | 0.9260 | 0.9259 | |
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| 0.2637 | 2.47 | 820 | 0.2447 | 0.9221 | 0.9213 | 0.9221 | 0.9228 | 0.9221 | |
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| 0.2605 | 2.53 | 840 | 0.2475 | 0.9237 | 0.9232 | 0.9237 | 0.9254 | 0.9240 | |
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| 0.2517 | 2.59 | 860 | 0.2590 | 0.9265 | 0.9259 | 0.9265 | 0.9272 | 0.9265 | |
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| 0.2453 | 2.65 | 880 | 0.2248 | 0.9300 | 0.9305 | 0.9300 | 0.9315 | 0.9305 | |
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| 0.2247 | 2.71 | 900 | 0.2285 | 0.9273 | 0.9281 | 0.9273 | 0.9299 | 0.9282 | |
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| 0.2402 | 2.77 | 920 | 0.2304 | 0.9306 | 0.9310 | 0.9306 | 0.9317 | 0.9310 | |
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| 0.2033 | 2.83 | 940 | 0.2228 | 0.9319 | 0.9316 | 0.9319 | 0.9325 | 0.9320 | |
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| 0.2315 | 2.89 | 960 | 0.2275 | 0.9271 | 0.9281 | 0.9271 | 0.9311 | 0.9283 | |
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| 0.2231 | 2.95 | 980 | 0.2115 | 0.9343 | 0.9345 | 0.9343 | 0.9350 | 0.9345 | |
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| 0.2061 | 3.01 | 1000 | 0.2156 | 0.9355 | 0.9356 | 0.9355 | 0.9361 | 0.9357 | |
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| 0.238 | 3.07 | 1020 | 0.2359 | 0.9252 | 0.9262 | 0.9252 | 0.9294 | 0.9265 | |
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| 0.1959 | 3.13 | 1040 | 0.2052 | 0.9343 | 0.9343 | 0.9343 | 0.9351 | 0.9345 | |
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| 0.2013 | 3.19 | 1060 | 0.2114 | 0.9337 | 0.9336 | 0.9337 | 0.9349 | 0.9340 | |
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| 0.2055 | 3.25 | 1080 | 0.1985 | 0.9354 | 0.9354 | 0.9354 | 0.9361 | 0.9356 | |
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| 0.1851 | 3.31 | 1100 | 0.2098 | 0.9340 | 0.9338 | 0.9340 | 0.9352 | 0.9342 | |
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| 0.2091 | 3.37 | 1120 | 0.2002 | 0.9350 | 0.9347 | 0.9350 | 0.9355 | 0.9350 | |
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| 0.1933 | 3.43 | 1140 | 0.2045 | 0.9367 | 0.9371 | 0.9367 | 0.9377 | 0.9370 | |
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| 0.2011 | 3.49 | 1160 | 0.2072 | 0.9350 | 0.9345 | 0.9350 | 0.9353 | 0.9349 | |
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| 0.2085 | 3.55 | 1180 | 0.2124 | 0.9327 | 0.9337 | 0.9327 | 0.9362 | 0.9338 | |
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| 0.1991 | 3.61 | 1200 | 0.1880 | 0.9396 | 0.9398 | 0.9396 | 0.9406 | 0.9399 | |
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| 0.1972 | 3.67 | 1220 | 0.2123 | 0.9347 | 0.9344 | 0.9347 | 0.9351 | 0.9347 | |
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| 0.1905 | 3.73 | 1240 | 0.2056 | 0.9351 | 0.9352 | 0.9351 | 0.9368 | 0.9355 | |
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| 0.1974 | 3.79 | 1260 | 0.2432 | 0.9285 | 0.9280 | 0.9285 | 0.9308 | 0.9289 | |
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| 0.1831 | 3.85 | 1280 | 0.1881 | 0.9392 | 0.9390 | 0.9392 | 0.9402 | 0.9394 | |
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| 0.1883 | 3.91 | 1300 | 0.1948 | 0.9413 | 0.9415 | 0.9413 | 0.9423 | 0.9416 | |
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| 0.2023 | 3.97 | 1320 | 0.2041 | 0.9335 | 0.9343 | 0.9335 | 0.9369 | 0.9346 | |
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| 0.1694 | 4.03 | 1340 | 0.2007 | 0.9360 | 0.9360 | 0.9360 | 0.9372 | 0.9363 | |
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| 0.1727 | 4.09 | 1360 | 0.1990 | 0.9411 | 0.9414 | 0.9411 | 0.9422 | 0.9414 | |
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| 0.1666 | 4.15 | 1380 | 0.1952 | 0.9397 | 0.9399 | 0.9397 | 0.9403 | 0.9399 | |
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| 0.2011 | 4.21 | 1400 | 0.1953 | 0.9384 | 0.9388 | 0.9384 | 0.9401 | 0.9389 | |
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| 0.1798 | 4.27 | 1420 | 0.2032 | 0.9325 | 0.9318 | 0.9325 | 0.9345 | 0.9328 | |
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| 0.1808 | 4.33 | 1440 | 0.2085 | 0.9308 | 0.9319 | 0.9308 | 0.9357 | 0.9323 | |
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| 0.1552 | 4.39 | 1460 | 0.2010 | 0.9371 | 0.9377 | 0.9371 | 0.9391 | 0.9377 | |
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| 0.1608 | 4.45 | 1480 | 0.2090 | 0.9342 | 0.9349 | 0.9342 | 0.9372 | 0.9351 | |
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| 0.1683 | 4.51 | 1500 | 0.2005 | 0.9374 | 0.9371 | 0.9374 | 0.9384 | 0.9376 | |
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| 0.1484 | 4.57 | 1520 | 0.1893 | 0.9380 | 0.9386 | 0.9380 | 0.9402 | 0.9387 | |
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| 0.1561 | 4.64 | 1540 | 0.1922 | 0.9401 | 0.9405 | 0.9401 | 0.9417 | 0.9406 | |
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| 0.1753 | 4.7 | 1560 | 0.1905 | 0.9416 | 0.9418 | 0.9416 | 0.9428 | 0.9419 | |
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| 0.1668 | 4.76 | 1580 | 0.1767 | 0.9420 | 0.9419 | 0.9420 | 0.9426 | 0.9421 | |
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| 0.1651 | 4.82 | 1600 | 0.1772 | 0.9429 | 0.9429 | 0.9429 | 0.9431 | 0.9429 | |
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| 0.1601 | 4.88 | 1620 | 0.1778 | 0.9433 | 0.9434 | 0.9433 | 0.9436 | 0.9434 | |
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| 0.1686 | 4.94 | 1640 | 0.1943 | 0.9414 | 0.9415 | 0.9414 | 0.9423 | 0.9416 | |
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| 0.1342 | 5.0 | 1660 | 0.1946 | 0.9395 | 0.9398 | 0.9395 | 0.9413 | 0.9400 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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