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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: working |
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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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# working |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2518 |
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- Wer: 0.5902 |
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- Cer: 0.0676 |
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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: 3e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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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: 2000 |
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- training_steps: 13000 |
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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 | Wer | Cer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
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| 10.4868 | 0.15 | 100 | 13.8019 | 1.0 | 0.9317 | |
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| 4.7063 | 0.3 | 200 | 9.0935 | 1.0 | 0.9692 | |
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| 3.624 | 0.45 | 300 | 6.7956 | 1.0 | 0.9692 | |
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| 3.5139 | 0.6 | 400 | 4.9444 | 1.0 | 0.9692 | |
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| 3.4538 | 0.74 | 500 | 4.4408 | 1.0 | 0.8260 | |
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| 3.393 | 0.89 | 600 | 4.1983 | 1.0 | 0.8183 | |
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| 3.3836 | 1.04 | 700 | 3.9939 | 1.0 | 0.7414 | |
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| 3.3494 | 1.19 | 800 | 3.8468 | 1.0 | 0.7642 | |
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| 3.2885 | 1.34 | 900 | 3.4016 | 1.0 | 0.7987 | |
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| 3.2538 | 1.49 | 1000 | 3.1741 | 1.0 | 0.8071 | |
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| 3.2633 | 1.64 | 1100 | 3.1409 | 1.0008 | 0.7878 | |
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| 3.1524 | 1.79 | 1200 | 2.9351 | 1.0 | 0.7935 | |
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| 3.0518 | 1.93 | 1300 | 2.7497 | 1.0 | 0.7559 | |
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| 2.807 | 2.08 | 1400 | 2.0357 | 0.9992 | 0.6716 | |
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| 2.3203 | 2.23 | 1500 | 1.2459 | 0.9927 | 0.4256 | |
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| 1.9018 | 2.38 | 1600 | 0.8000 | 0.9513 | 0.2472 | |
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| 1.6926 | 2.53 | 1700 | 0.6204 | 0.9066 | 0.1800 | |
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| 1.5501 | 2.68 | 1800 | 0.5426 | 0.8619 | 0.1377 | |
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| 1.4654 | 2.83 | 1900 | 0.4881 | 0.8050 | 0.1199 | |
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| 1.4087 | 2.98 | 2000 | 0.4747 | 0.7961 | 0.1153 | |
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| 1.3228 | 3.12 | 2100 | 0.4334 | 0.7953 | 0.1138 | |
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| 1.3292 | 3.27 | 2200 | 0.4214 | 0.7604 | 0.1072 | |
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| 1.3123 | 3.42 | 2300 | 0.3850 | 0.7400 | 0.0998 | |
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| 1.3417 | 3.57 | 2400 | 0.4069 | 0.7595 | 0.1039 | |
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| 1.2557 | 3.72 | 2500 | 0.3867 | 0.7246 | 0.0970 | |
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| 1.2588 | 3.87 | 2600 | 0.3678 | 0.7311 | 0.0952 | |
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| 1.2602 | 4.02 | 2700 | 0.3682 | 0.7262 | 0.0937 | |
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| 1.1774 | 4.17 | 2800 | 0.3530 | 0.7043 | 0.0908 | |
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| 1.2075 | 4.32 | 2900 | 0.3343 | 0.7076 | 0.0885 | |
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| 1.1085 | 4.46 | 3000 | 0.3542 | 0.7124 | 0.0898 | |
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| 1.1451 | 4.61 | 3100 | 0.3325 | 0.6872 | 0.0870 | |
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| 1.1721 | 4.76 | 3200 | 0.3318 | 0.6848 | 0.0858 | |
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| 1.1549 | 4.91 | 3300 | 0.3145 | 0.6816 | 0.0862 | |
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| 1.1276 | 5.06 | 3400 | 0.3133 | 0.6613 | 0.0822 | |
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| 1.1193 | 5.21 | 3500 | 0.3117 | 0.6783 | 0.0832 | |
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| 1.158 | 5.36 | 3600 | 0.3111 | 0.6856 | 0.0841 | |
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| 1.1235 | 5.51 | 3700 | 0.3130 | 0.6856 | 0.0848 | |
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| 1.1075 | 5.65 | 3800 | 0.3144 | 0.6621 | 0.0831 | |
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| 1.0834 | 5.8 | 3900 | 0.3228 | 0.6694 | 0.0873 | |
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| 1.0751 | 5.95 | 4000 | 0.3116 | 0.6734 | 0.0823 | |
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| 1.1114 | 6.1 | 4100 | 0.3022 | 0.6718 | 0.0812 | |
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| 1.0025 | 6.25 | 4200 | 0.3149 | 0.6807 | 0.0826 | |
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| 1.0488 | 6.4 | 4300 | 0.2957 | 0.6377 | 0.0799 | |
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| 1.0602 | 6.55 | 4400 | 0.3002 | 0.6604 | 0.0800 | |
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| 1.0694 | 6.7 | 4500 | 0.3003 | 0.6580 | 0.0799 | |
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| 1.0583 | 6.85 | 4600 | 0.2850 | 0.6182 | 0.0777 | |
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| 1.0737 | 6.99 | 4700 | 0.2876 | 0.6288 | 0.0785 | |
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| 1.0123 | 7.14 | 4800 | 0.3064 | 0.6791 | 0.0806 | |
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| 1.0305 | 7.29 | 4900 | 0.2925 | 0.6271 | 0.0796 | |
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| 1.0269 | 7.44 | 5000 | 0.2978 | 0.6271 | 0.0796 | |
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| 0.9634 | 7.59 | 5100 | 0.2943 | 0.6353 | 0.0791 | |
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| 0.986 | 7.74 | 5200 | 0.2947 | 0.6556 | 0.0795 | |
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| 1.0039 | 7.89 | 5300 | 0.2891 | 0.6580 | 0.0793 | |
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| 0.9928 | 8.04 | 5400 | 0.2901 | 0.6426 | 0.0786 | |
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| 0.9799 | 8.18 | 5500 | 0.2827 | 0.6385 | 0.0775 | |
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| 0.9952 | 8.33 | 5600 | 0.3063 | 0.6677 | 0.0791 | |
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| 0.9874 | 8.48 | 5700 | 0.2922 | 0.6401 | 0.0780 | |
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| 0.9716 | 8.63 | 5800 | 0.2806 | 0.6060 | 0.0748 | |
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| 1.0133 | 8.78 | 5900 | 0.2824 | 0.6328 | 0.0759 | |
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| 0.9665 | 8.93 | 6000 | 0.2849 | 0.6442 | 0.0782 | |
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| 0.9516 | 9.08 | 6100 | 0.2795 | 0.6158 | 0.0766 | |
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| 0.9864 | 9.23 | 6200 | 0.2998 | 0.6336 | 0.0788 | |
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| 0.9652 | 9.38 | 6300 | 0.2812 | 0.6174 | 0.0760 | |
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| 0.9518 | 9.52 | 6400 | 0.2824 | 0.6182 | 0.0764 | |
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| 0.9447 | 9.67 | 6500 | 0.2872 | 0.6434 | 0.0777 | |
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| 1.0079 | 9.82 | 6600 | 0.2668 | 0.6158 | 0.0753 | |
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| 0.9996 | 9.97 | 6700 | 0.2721 | 0.6084 | 0.0749 | |
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| 0.9042 | 10.12 | 6800 | 0.2755 | 0.6109 | 0.0746 | |
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| 0.965 | 10.27 | 6900 | 0.2747 | 0.6093 | 0.0751 | |
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| 0.9172 | 10.42 | 7000 | 0.2799 | 0.6247 | 0.0763 | |
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| 0.9441 | 10.57 | 7100 | 0.2833 | 0.6231 | 0.0771 | |
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| 0.9417 | 10.71 | 7200 | 0.2736 | 0.6117 | 0.0754 | |
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| 0.9091 | 10.86 | 7300 | 0.2783 | 0.6182 | 0.0760 | |
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| 0.96 | 11.01 | 7400 | 0.2674 | 0.6149 | 0.0759 | |
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| 0.8866 | 11.16 | 7500 | 0.2703 | 0.6198 | 0.0749 | |
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| 0.9357 | 11.31 | 7600 | 0.2796 | 0.6117 | 0.0769 | |
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| 0.9144 | 11.46 | 7700 | 0.2743 | 0.6084 | 0.0753 | |
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| 0.9456 | 11.61 | 7800 | 0.2675 | 0.6206 | 0.0750 | |
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| 0.9022 | 11.76 | 7900 | 0.2680 | 0.6093 | 0.0736 | |
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| 0.9047 | 11.9 | 8000 | 0.2753 | 0.6158 | 0.0766 | |
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| 0.9151 | 12.05 | 8100 | 0.2772 | 0.6328 | 0.0761 | |
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| 0.9123 | 12.2 | 8200 | 0.2879 | 0.6247 | 0.0758 | |
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| 0.8731 | 12.35 | 8300 | 0.2764 | 0.6093 | 0.0743 | |
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| 0.8745 | 12.5 | 8400 | 0.2796 | 0.6214 | 0.0751 | |
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| 0.8812 | 12.65 | 8500 | 0.2684 | 0.6060 | 0.0739 | |
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| 0.9228 | 12.8 | 8600 | 0.2678 | 0.6149 | 0.0744 | |
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| 0.9029 | 12.95 | 8700 | 0.2718 | 0.5963 | 0.0738 | |
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| 0.934 | 13.1 | 8800 | 0.2700 | 0.6174 | 0.0737 | |
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| 0.8622 | 13.24 | 8900 | 0.2789 | 0.6231 | 0.0757 | |
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| 0.848 | 13.39 | 9000 | 0.2725 | 0.6060 | 0.0735 | |
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| 0.8727 | 13.54 | 9100 | 0.2602 | 0.6133 | 0.0737 | |
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| 0.8705 | 13.69 | 9200 | 0.2661 | 0.6247 | 0.0748 | |
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| 0.8724 | 13.84 | 9300 | 0.2658 | 0.6190 | 0.0741 | |
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| 0.8609 | 13.99 | 9400 | 0.2677 | 0.6320 | 0.0753 | |
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| 0.8836 | 14.14 | 9500 | 0.2692 | 0.6353 | 0.0753 | |
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| 0.8419 | 14.29 | 9600 | 0.2715 | 0.6158 | 0.0734 | |
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| 0.8583 | 14.43 | 9700 | 0.2596 | 0.6060 | 0.0726 | |
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| 0.882 | 14.58 | 9800 | 0.2636 | 0.6198 | 0.0749 | |
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| 0.8396 | 14.73 | 9900 | 0.2627 | 0.6125 | 0.0739 | |
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| 0.8832 | 14.88 | 10000 | 0.2676 | 0.6271 | 0.0739 | |
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| 0.8647 | 15.03 | 10100 | 0.2669 | 0.6190 | 0.0742 | |
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| 0.8674 | 15.18 | 10200 | 0.2677 | 0.6158 | 0.0741 | |
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| 0.8621 | 15.33 | 10300 | 0.2605 | 0.6028 | 0.0711 | |
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| 0.8946 | 15.48 | 10400 | 0.2630 | 0.6036 | 0.0722 | |
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| 0.8544 | 15.62 | 10500 | 0.2644 | 0.6133 | 0.0737 | |
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| 0.8684 | 15.77 | 10600 | 0.2624 | 0.5963 | 0.0731 | |
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| 0.861 | 15.92 | 10700 | 0.2626 | 0.5987 | 0.0735 | |
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| 0.8322 | 16.07 | 10800 | 0.2635 | 0.6003 | 0.0736 | |
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| 0.8277 | 16.22 | 10900 | 0.2729 | 0.6214 | 0.0742 | |
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| 0.8463 | 16.37 | 11000 | 0.2663 | 0.6068 | 0.0739 | |
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| 0.8506 | 16.52 | 11100 | 0.2629 | 0.6003 | 0.0728 | |
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| 0.8005 | 16.67 | 11200 | 0.2657 | 0.6206 | 0.0748 | |
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| 0.8336 | 16.82 | 11300 | 0.2625 | 0.6003 | 0.0721 | |
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| 0.8676 | 16.96 | 11400 | 0.2691 | 0.6174 | 0.0733 | |
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| 0.8028 | 17.11 | 11500 | 0.2635 | 0.6003 | 0.0720 | |
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| 0.85 | 17.26 | 11600 | 0.2602 | 0.6011 | 0.0726 | |
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| 0.8366 | 17.41 | 11700 | 0.2658 | 0.6068 | 0.0729 | |
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| 0.8098 | 17.56 | 11800 | 0.2621 | 0.6003 | 0.0720 | |
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| 0.8666 | 17.71 | 11900 | 0.2600 | 0.5963 | 0.0734 | |
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| 0.8271 | 17.86 | 12000 | 0.2618 | 0.6076 | 0.0734 | |
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| 0.8018 | 18.01 | 12100 | 0.2669 | 0.6117 | 0.0737 | |
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| 0.8471 | 18.15 | 12200 | 0.2630 | 0.6028 | 0.0727 | |
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| 0.8573 | 18.3 | 12300 | 0.2666 | 0.6117 | 0.0735 | |
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| 0.8331 | 18.45 | 12400 | 0.2639 | 0.6060 | 0.0727 | |
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| 0.8161 | 18.6 | 12500 | 0.2606 | 0.6044 | 0.0731 | |
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| 0.8021 | 18.75 | 12600 | 0.2591 | 0.6019 | 0.0726 | |
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| 0.8236 | 18.9 | 12700 | 0.2611 | 0.6036 | 0.0722 | |
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| 0.7332 | 19.05 | 12800 | 0.2610 | 0.6060 | 0.0732 | |
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| 0.8267 | 19.2 | 12900 | 0.2599 | 0.6028 | 0.0733 | |
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| 0.8296 | 19.35 | 13000 | 0.2608 | 0.6036 | 0.0731 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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