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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: results |
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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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# results |
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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: 0.7998 |
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- Accuracy: 0.7023 |
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- Precision: 0.7144 |
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- Recall: 0.7023 |
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- F1: 0.7065 |
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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: 16 |
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- eval_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.0859 | 0.04 | 10 | 1.0722 | 0.6493 | 0.6735 | 0.6493 | 0.5118 | |
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| 1.0731 | 0.07 | 20 | 1.0562 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 1.0456 | 0.11 | 30 | 1.0316 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 1.0199 | 0.15 | 40 | 0.9979 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.9613 | 0.19 | 50 | 0.9362 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.8949 | 0.22 | 60 | 0.8645 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.9151 | 0.26 | 70 | 0.8606 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.8583 | 0.3 | 80 | 0.8593 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.9604 | 0.33 | 90 | 0.8539 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.7919 | 0.37 | 100 | 0.8504 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.9365 | 0.41 | 110 | 0.8520 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.9285 | 0.45 | 120 | 0.8521 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.8564 | 0.48 | 130 | 0.8615 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.8132 | 0.52 | 140 | 0.8583 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.8911 | 0.56 | 150 | 0.8467 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.8383 | 0.59 | 160 | 0.8373 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.8387 | 0.63 | 170 | 0.8372 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.979 | 0.67 | 180 | 0.8595 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.7621 | 0.71 | 190 | 0.8642 | 0.6488 | 0.4209 | 0.6488 | 0.5105 | |
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| 0.8367 | 0.74 | 200 | 0.8276 | 0.6553 | 0.6447 | 0.6553 | 0.5271 | |
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| 0.9116 | 0.78 | 210 | 0.8466 | 0.6493 | 0.6735 | 0.6493 | 0.5118 | |
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| 0.8444 | 0.82 | 220 | 0.8171 | 0.6504 | 0.6740 | 0.6504 | 0.5143 | |
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| 0.7815 | 0.86 | 230 | 0.7919 | 0.6667 | 0.6008 | 0.6667 | 0.5615 | |
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| 0.8592 | 0.89 | 240 | 0.7907 | 0.6732 | 0.5878 | 0.6732 | 0.5962 | |
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| 0.8933 | 0.93 | 250 | 0.7963 | 0.6813 | 0.6004 | 0.6813 | 0.6102 | |
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| 0.8409 | 0.97 | 260 | 0.7812 | 0.6797 | 0.6021 | 0.6797 | 0.6066 | |
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| 0.8285 | 1.0 | 270 | 0.7794 | 0.6737 | 0.5987 | 0.6737 | 0.5940 | |
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| 0.7895 | 1.04 | 280 | 0.7893 | 0.6846 | 0.6044 | 0.6846 | 0.6168 | |
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| 0.8012 | 1.08 | 290 | 0.7617 | 0.6813 | 0.6129 | 0.6813 | 0.6002 | |
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| 0.7215 | 1.12 | 300 | 0.8029 | 0.6748 | 0.6248 | 0.6748 | 0.5764 | |
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| 0.8134 | 1.15 | 310 | 0.8294 | 0.6781 | 0.5949 | 0.6781 | 0.6294 | |
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| 0.7247 | 1.19 | 320 | 0.7944 | 0.6732 | 0.5941 | 0.6732 | 0.6290 | |
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| 0.8043 | 1.23 | 330 | 0.7978 | 0.6656 | 0.5931 | 0.6656 | 0.6268 | |
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| 0.7647 | 1.26 | 340 | 0.7571 | 0.6884 | 0.6344 | 0.6884 | 0.6063 | |
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| 0.7807 | 1.3 | 350 | 0.7958 | 0.6412 | 0.6041 | 0.6412 | 0.6167 | |
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| 0.8031 | 1.34 | 360 | 0.7261 | 0.6906 | 0.6820 | 0.6906 | 0.6680 | |
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| 0.6965 | 1.38 | 370 | 0.7287 | 0.7003 | 0.6796 | 0.7003 | 0.6813 | |
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| 0.69 | 1.41 | 380 | 0.7115 | 0.7074 | 0.6981 | 0.7074 | 0.6581 | |
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| 0.7015 | 1.45 | 390 | 0.7391 | 0.7063 | 0.6932 | 0.7063 | 0.6813 | |
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| 0.7461 | 1.49 | 400 | 0.7624 | 0.6987 | 0.6791 | 0.6987 | 0.6787 | |
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| 0.758 | 1.52 | 410 | 0.7778 | 0.6819 | 0.6893 | 0.6819 | 0.6695 | |
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| 0.7617 | 1.56 | 420 | 0.7913 | 0.6878 | 0.6906 | 0.6878 | 0.6339 | |
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| 0.7848 | 1.6 | 430 | 0.7785 | 0.6629 | 0.6806 | 0.6629 | 0.6643 | |
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| 0.8138 | 1.64 | 440 | 0.7191 | 0.6954 | 0.6763 | 0.6954 | 0.6474 | |
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| 0.7451 | 1.67 | 450 | 0.7086 | 0.7030 | 0.7061 | 0.7030 | 0.6434 | |
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| 0.788 | 1.71 | 460 | 0.7202 | 0.6840 | 0.6956 | 0.6840 | 0.6497 | |
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| 0.7107 | 1.75 | 470 | 0.7543 | 0.6835 | 0.6067 | 0.6835 | 0.6379 | |
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| 0.7047 | 1.78 | 480 | 0.7940 | 0.6862 | 0.6697 | 0.6862 | 0.6258 | |
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| 0.8561 | 1.82 | 490 | 0.7497 | 0.6802 | 0.6860 | 0.6802 | 0.6666 | |
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| 0.804 | 1.86 | 500 | 0.7247 | 0.6938 | 0.6757 | 0.6938 | 0.6555 | |
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| 0.7796 | 1.9 | 510 | 0.7239 | 0.7063 | 0.6988 | 0.7063 | 0.6702 | |
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| 0.8124 | 1.93 | 520 | 0.7693 | 0.6976 | 0.7003 | 0.6976 | 0.6621 | |
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| 0.7306 | 1.97 | 530 | 0.8395 | 0.6363 | 0.6788 | 0.6363 | 0.6329 | |
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| 0.7079 | 2.01 | 540 | 0.7051 | 0.7041 | 0.6828 | 0.7041 | 0.6811 | |
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| 0.6018 | 2.04 | 550 | 0.7327 | 0.7058 | 0.6873 | 0.7058 | 0.6849 | |
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| 0.5824 | 2.08 | 560 | 0.7819 | 0.6743 | 0.6811 | 0.6743 | 0.6774 | |
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| 0.6001 | 2.12 | 570 | 0.7547 | 0.7139 | 0.6980 | 0.7139 | 0.7023 | |
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| 0.6471 | 2.16 | 580 | 0.7617 | 0.7172 | 0.7040 | 0.7172 | 0.6848 | |
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| 0.6226 | 2.19 | 590 | 0.7421 | 0.6927 | 0.6974 | 0.6927 | 0.6909 | |
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| 0.5203 | 2.23 | 600 | 0.7935 | 0.6694 | 0.6871 | 0.6694 | 0.6748 | |
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| 0.6445 | 2.27 | 610 | 0.7722 | 0.7182 | 0.7038 | 0.7182 | 0.6947 | |
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| 0.7027 | 2.3 | 620 | 0.7517 | 0.6754 | 0.7039 | 0.6754 | 0.6814 | |
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| 0.5662 | 2.34 | 630 | 0.6804 | 0.7182 | 0.7069 | 0.7182 | 0.7090 | |
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| 0.6304 | 2.38 | 640 | 0.6965 | 0.7128 | 0.6958 | 0.7128 | 0.6904 | |
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| 0.6258 | 2.42 | 650 | 0.7053 | 0.7041 | 0.7041 | 0.7041 | 0.7041 | |
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| 0.4966 | 2.45 | 660 | 0.7300 | 0.7177 | 0.7030 | 0.7177 | 0.7033 | |
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| 0.5721 | 2.49 | 670 | 0.8330 | 0.6683 | 0.6910 | 0.6683 | 0.6737 | |
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| 0.5507 | 2.53 | 680 | 0.8154 | 0.6857 | 0.7020 | 0.6857 | 0.6923 | |
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| 0.6392 | 2.57 | 690 | 0.8048 | 0.7166 | 0.7079 | 0.7166 | 0.6814 | |
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| 0.6128 | 2.6 | 700 | 0.7445 | 0.6786 | 0.6890 | 0.6786 | 0.6827 | |
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| 0.622 | 2.64 | 710 | 0.7029 | 0.7047 | 0.6895 | 0.7047 | 0.6870 | |
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| 0.5847 | 2.68 | 720 | 0.7911 | 0.6569 | 0.6889 | 0.6569 | 0.6677 | |
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| 0.6454 | 2.71 | 730 | 0.7062 | 0.7112 | 0.7017 | 0.7112 | 0.6797 | |
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| 0.5264 | 2.75 | 740 | 0.7419 | 0.6992 | 0.6893 | 0.6992 | 0.6870 | |
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| 0.649 | 2.79 | 750 | 0.7243 | 0.7063 | 0.7009 | 0.7063 | 0.7030 | |
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| 0.5343 | 2.83 | 760 | 0.7478 | 0.6889 | 0.7030 | 0.6889 | 0.6946 | |
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| 0.5335 | 2.86 | 770 | 0.7222 | 0.7237 | 0.7115 | 0.7237 | 0.7052 | |
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| 0.5228 | 2.9 | 780 | 0.7182 | 0.7226 | 0.7152 | 0.7226 | 0.7063 | |
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| 0.5605 | 2.94 | 790 | 0.7195 | 0.7210 | 0.7128 | 0.7210 | 0.7106 | |
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| 0.627 | 2.97 | 800 | 0.7559 | 0.6878 | 0.7135 | 0.6878 | 0.6933 | |
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| 0.6536 | 3.01 | 810 | 0.6616 | 0.7275 | 0.7141 | 0.7275 | 0.7105 | |
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| 0.4106 | 3.05 | 820 | 0.7176 | 0.7307 | 0.7209 | 0.7307 | 0.7230 | |
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| 0.3588 | 3.09 | 830 | 0.8387 | 0.7226 | 0.7230 | 0.7226 | 0.7183 | |
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| 0.404 | 3.12 | 840 | 0.8459 | 0.7117 | 0.7138 | 0.7117 | 0.7124 | |
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| 0.4313 | 3.16 | 850 | 0.8406 | 0.6992 | 0.7108 | 0.6992 | 0.7036 | |
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| 0.3407 | 3.2 | 860 | 0.8317 | 0.6916 | 0.7133 | 0.6916 | 0.6997 | |
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| 0.365 | 3.23 | 870 | 0.8310 | 0.6992 | 0.7110 | 0.6992 | 0.7035 | |
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| 0.3776 | 3.27 | 880 | 0.8376 | 0.6927 | 0.7107 | 0.6927 | 0.6986 | |
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| 0.3442 | 3.31 | 890 | 0.8554 | 0.7079 | 0.7082 | 0.7079 | 0.7079 | |
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| 0.41 | 3.35 | 900 | 0.9473 | 0.6401 | 0.7039 | 0.6401 | 0.6550 | |
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| 0.4649 | 3.38 | 910 | 0.8139 | 0.7134 | 0.7063 | 0.7134 | 0.7090 | |
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| 0.4359 | 3.42 | 920 | 0.8275 | 0.6992 | 0.7095 | 0.6992 | 0.7022 | |
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| 0.2906 | 3.46 | 930 | 0.8398 | 0.7096 | 0.7013 | 0.7096 | 0.7025 | |
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| 0.413 | 3.49 | 940 | 0.8558 | 0.6982 | 0.7049 | 0.6982 | 0.7009 | |
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| 0.3936 | 3.53 | 950 | 0.8457 | 0.7025 | 0.7058 | 0.7025 | 0.7039 | |
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| 0.3691 | 3.57 | 960 | 0.8312 | 0.7014 | 0.7102 | 0.7014 | 0.7050 | |
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| 0.3747 | 3.61 | 970 | 0.8146 | 0.7210 | 0.7074 | 0.7210 | 0.7086 | |
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| 0.4037 | 3.64 | 980 | 0.7906 | 0.7199 | 0.7132 | 0.7199 | 0.7150 | |
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| 0.4112 | 3.68 | 990 | 0.8135 | 0.7139 | 0.7145 | 0.7139 | 0.7137 | |
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| 0.3685 | 3.72 | 1000 | 0.8024 | 0.7106 | 0.7144 | 0.7106 | 0.7123 | |
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| 0.3881 | 3.75 | 1010 | 0.8339 | 0.7063 | 0.7109 | 0.7063 | 0.7063 | |
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| 0.4168 | 3.79 | 1020 | 0.8261 | 0.7231 | 0.7191 | 0.7231 | 0.7206 | |
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| 0.3591 | 3.83 | 1030 | 0.8014 | 0.7340 | 0.7258 | 0.7340 | 0.7281 | |
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| 0.3632 | 3.87 | 1040 | 0.8568 | 0.6878 | 0.7206 | 0.6878 | 0.6974 | |
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| 0.259 | 3.9 | 1050 | 0.8182 | 0.7324 | 0.7226 | 0.7324 | 0.7225 | |
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| 0.3741 | 3.94 | 1060 | 0.8511 | 0.7009 | 0.7200 | 0.7009 | 0.7078 | |
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| 0.3551 | 3.98 | 1070 | 0.8283 | 0.7150 | 0.7186 | 0.7150 | 0.7159 | |
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| 0.4105 | 4.01 | 1080 | 0.7817 | 0.7204 | 0.7209 | 0.7204 | 0.7205 | |
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| 0.2411 | 4.05 | 1090 | 0.8384 | 0.7372 | 0.7272 | 0.7372 | 0.7274 | |
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| 0.2166 | 4.09 | 1100 | 0.9466 | 0.7003 | 0.7240 | 0.7003 | 0.7066 | |
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| 0.4075 | 4.13 | 1110 | 0.9255 | 0.6976 | 0.7157 | 0.6976 | 0.7042 | |
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| 0.3328 | 4.16 | 1120 | 0.9120 | 0.6922 | 0.7153 | 0.6922 | 0.7003 | |
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| 0.1584 | 4.2 | 1130 | 0.9688 | 0.6857 | 0.7100 | 0.6857 | 0.6942 | |
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| 0.1737 | 4.24 | 1140 | 1.0205 | 0.7356 | 0.7267 | 0.7356 | 0.7289 | |
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| 0.2335 | 4.28 | 1150 | 1.0734 | 0.7068 | 0.7194 | 0.7068 | 0.7116 | |
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| 0.2179 | 4.31 | 1160 | 1.0748 | 0.7085 | 0.7190 | 0.7085 | 0.7127 | |
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| 0.244 | 4.35 | 1170 | 1.0801 | 0.7030 | 0.7220 | 0.7030 | 0.7097 | |
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| 0.2151 | 4.39 | 1180 | 1.0332 | 0.7112 | 0.7176 | 0.7112 | 0.7140 | |
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| 0.2602 | 4.42 | 1190 | 1.0343 | 0.7134 | 0.7181 | 0.7134 | 0.7154 | |
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| 0.131 | 4.46 | 1200 | 1.0453 | 0.7128 | 0.7175 | 0.7128 | 0.7149 | |
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| 0.1966 | 4.5 | 1210 | 1.0673 | 0.7096 | 0.7160 | 0.7096 | 0.7121 | |
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| 0.2136 | 4.54 | 1220 | 1.0550 | 0.7166 | 0.7157 | 0.7166 | 0.7158 | |
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| 0.1625 | 4.57 | 1230 | 1.0690 | 0.7172 | 0.7148 | 0.7172 | 0.7156 | |
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| 0.2199 | 4.61 | 1240 | 1.0908 | 0.7112 | 0.7182 | 0.7112 | 0.7141 | |
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| 0.2028 | 4.65 | 1250 | 1.0991 | 0.7085 | 0.7200 | 0.7085 | 0.7130 | |
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| 0.2669 | 4.68 | 1260 | 1.0944 | 0.7134 | 0.7205 | 0.7134 | 0.7163 | |
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| 0.1408 | 4.72 | 1270 | 1.0827 | 0.7248 | 0.7198 | 0.7248 | 0.7215 | |
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| 0.2649 | 4.76 | 1280 | 1.0974 | 0.7199 | 0.7182 | 0.7199 | 0.7187 | |
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| 0.1512 | 4.8 | 1290 | 1.1159 | 0.7220 | 0.7212 | 0.7220 | 0.7214 | |
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| 0.1962 | 4.83 | 1300 | 1.1374 | 0.7161 | 0.7206 | 0.7161 | 0.7180 | |
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| 0.2322 | 4.87 | 1310 | 1.1435 | 0.7144 | 0.7226 | 0.7144 | 0.7178 | |
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| 0.2095 | 4.91 | 1320 | 1.1408 | 0.7106 | 0.7220 | 0.7106 | 0.7151 | |
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| 0.1534 | 4.94 | 1330 | 1.1466 | 0.7123 | 0.7248 | 0.7123 | 0.7170 | |
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| 0.2505 | 4.98 | 1340 | 1.1481 | 0.7123 | 0.7248 | 0.7123 | 0.7170 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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