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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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datasets: |
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- common_voice_8_0 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-frisian-cv-8 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_8_0 |
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type: common_voice_8_0 |
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config: fy-NL |
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split: validation |
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args: fy-NL |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.07238251678331667 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_8_0 |
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type: common_voice_8_0 |
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config: fy-NL |
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split: test |
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args: fy-NL |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.07103627691862986 |
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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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# wav2vec2-large-xls-r-300m-frisian-cv-8 |
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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 common_voice_8_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0707 |
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- Wer: 0.0724 |
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And on the test set: |
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- Wer: 0.0710 |
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## Model description |
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This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 6 where |
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I use as training set all validated data (~ 50 hours) except the test and evaluation sets (~ 4.5 hours each). |
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The number of training hours adds up to 41 hours of Frisian speech. This varies from experiment 2 because I fine-tune on the 300M/0.3B parameters version of XLS-R. |
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## Intended uses & limitations |
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The intended use is for recognizing Frisian speech. |
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Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0. |
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## Training and evaluation data |
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The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split corresponds to all of the validated data except for the recordings found in the evaluation and test splits. |
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## Training procedure |
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The script used for training this model can be found in this GitHub repository: [link](https://github.com/greenw0lf/MSc-VT-Thesis/). |
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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: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 40 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 14.7268 | 0.43 | 400 | 8.7389 | 1.0 | |
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| 5.3377 | 0.86 | 800 | 3.7016 | 1.0 | |
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| 3.343 | 1.29 | 1200 | 3.0984 | 1.0 | |
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| 3.0306 | 1.71 | 1600 | 2.9643 | 1.0 | |
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| 2.9511 | 2.14 | 2000 | 2.9273 | 1.0 | |
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| 2.9078 | 2.57 | 2400 | 2.8202 | 1.0 | |
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| 2.4965 | 3.0 | 2800 | 1.3805 | 0.8888 | |
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| 1.5378 | 3.43 | 3200 | 0.6556 | 0.5720 | |
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| 1.119 | 3.86 | 3600 | 0.4260 | 0.4077 | |
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| 0.9159 | 4.29 | 4000 | 0.3457 | 0.3322 | |
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| 0.8037 | 4.72 | 4400 | 0.2765 | 0.2850 | |
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| 0.7411 | 5.14 | 4800 | 0.2447 | 0.2473 | |
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| 0.6767 | 5.57 | 5200 | 0.2176 | 0.2234 | |
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| 0.6296 | 6.0 | 5600 | 0.1996 | 0.2078 | |
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| 0.6165 | 6.43 | 6000 | 0.1891 | 0.1977 | |
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| 0.5856 | 6.86 | 6400 | 0.1763 | 0.1855 | |
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| 0.5674 | 7.29 | 6800 | 0.1708 | 0.1797 | |
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| 0.5399 | 7.72 | 7200 | 0.1593 | 0.1694 | |
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| 0.5195 | 8.15 | 7600 | 0.1551 | 0.1660 | |
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| 0.4973 | 8.57 | 8000 | 0.1509 | 0.1583 | |
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| 0.4907 | 9.0 | 8400 | 0.1480 | 0.1525 | |
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| 0.4681 | 9.43 | 8800 | 0.1389 | 0.1494 | |
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| 0.4513 | 9.86 | 9200 | 0.1368 | 0.1414 | |
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| 0.4486 | 10.29 | 9600 | 0.1294 | 0.1390 | |
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| 0.4381 | 10.72 | 10000 | 0.1262 | 0.1354 | |
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| 0.443 | 11.15 | 10400 | 0.1234 | 0.1313 | |
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| 0.4182 | 11.58 | 10800 | 0.1196 | 0.1294 | |
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| 0.4036 | 12.0 | 11200 | 0.1194 | 0.1259 | |
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| 0.4027 | 12.43 | 11600 | 0.1170 | 0.1226 | |
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| 0.4066 | 12.86 | 12000 | 0.1156 | 0.1224 | |
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| 0.3885 | 13.29 | 12400 | 0.1136 | 0.1174 | |
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| 0.3859 | 13.72 | 12800 | 0.1121 | 0.1146 | |
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| 0.3812 | 14.15 | 13200 | 0.1097 | 0.1141 | |
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| 0.3774 | 14.58 | 13600 | 0.1059 | 0.1130 | |
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| 0.3678 | 15.01 | 14000 | 0.1058 | 0.1096 | |
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| 0.3586 | 15.43 | 14400 | 0.1026 | 0.1099 | |
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| 0.3612 | 15.86 | 14800 | 0.1010 | 0.1076 | |
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| 0.3626 | 16.29 | 15200 | 0.0993 | 0.1068 | |
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| 0.353 | 16.72 | 15600 | 0.0974 | 0.1046 | |
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| 0.3564 | 17.15 | 16000 | 0.0986 | 0.1037 | |
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| 0.3447 | 17.58 | 16400 | 0.0977 | 0.1041 | |
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| 0.3454 | 18.01 | 16800 | 0.0945 | 0.1023 | |
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| 0.3338 | 18.44 | 17200 | 0.0904 | 0.0996 | |
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| 0.3359 | 18.86 | 17600 | 0.0950 | 0.1002 | |
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| 0.3179 | 19.29 | 18000 | 0.0911 | 0.0977 | |
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| 0.3202 | 19.72 | 18400 | 0.0906 | 0.0979 | |
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| 0.3317 | 20.15 | 18800 | 0.0894 | 0.0963 | |
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| 0.3187 | 20.58 | 19200 | 0.0878 | 0.0938 | |
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| 0.3075 | 21.01 | 19600 | 0.0893 | 0.0937 | |
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| 0.3032 | 21.44 | 20000 | 0.0872 | 0.0923 | |
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| 0.3048 | 21.86 | 20400 | 0.0848 | 0.0921 | |
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| 0.3045 | 22.29 | 20800 | 0.0860 | 0.0887 | |
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| 0.316 | 22.72 | 21200 | 0.0841 | 0.0896 | |
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| 0.2986 | 23.15 | 21600 | 0.0840 | 0.0876 | |
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| 0.294 | 23.58 | 22000 | 0.0824 | 0.0862 | |
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| 0.313 | 24.01 | 22400 | 0.0814 | 0.0855 | |
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| 0.2864 | 24.44 | 22800 | 0.0816 | 0.0861 | |
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| 0.2927 | 24.87 | 23200 | 0.0807 | 0.0875 | |
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| 0.294 | 25.29 | 23600 | 0.0829 | 0.0826 | |
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| 0.2834 | 25.72 | 24000 | 0.0794 | 0.0823 | |
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| 0.2852 | 26.15 | 24400 | 0.0781 | 0.0815 | |
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| 0.2823 | 26.58 | 24800 | 0.0781 | 0.0821 | |
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| 0.2835 | 27.01 | 25200 | 0.0788 | 0.0826 | |
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| 0.2763 | 27.44 | 25600 | 0.0789 | 0.0823 | |
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| 0.2845 | 27.87 | 26000 | 0.0767 | 0.0803 | |
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| 0.2777 | 28.3 | 26400 | 0.0775 | 0.0809 | |
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| 0.275 | 28.72 | 26800 | 0.0758 | 0.0794 | |
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| 0.2707 | 29.15 | 27200 | 0.0745 | 0.0790 | |
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| 0.2734 | 29.58 | 27600 | 0.0765 | 0.0797 | |
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| 0.2716 | 30.01 | 28000 | 0.0746 | 0.0780 | |
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| 0.2626 | 30.44 | 28400 | 0.0756 | 0.0776 | |
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| 0.2671 | 30.87 | 28800 | 0.0742 | 0.0763 | |
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| 0.2592 | 31.3 | 29200 | 0.0730 | 0.0771 | |
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| 0.2685 | 31.73 | 29600 | 0.0733 | 0.0760 | |
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| 0.2727 | 32.15 | 30000 | 0.0738 | 0.0758 | |
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| 0.2564 | 32.58 | 30400 | 0.0731 | 0.0763 | |
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| 0.2528 | 33.01 | 30800 | 0.0730 | 0.0758 | |
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| 0.2573 | 33.44 | 31200 | 0.0717 | 0.0746 | |
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| 0.2597 | 33.87 | 31600 | 0.0718 | 0.0760 | |
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| 0.2511 | 34.3 | 32000 | 0.0737 | 0.0750 | |
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| 0.2551 | 34.73 | 32400 | 0.0732 | 0.0758 | |
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| 0.26 | 35.16 | 32800 | 0.0724 | 0.0746 | |
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| 0.2563 | 35.58 | 33200 | 0.0717 | 0.0730 | |
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| 0.2559 | 36.01 | 33600 | 0.0707 | 0.0734 | |
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| 0.2499 | 36.44 | 34000 | 0.0721 | 0.0729 | |
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| 0.252 | 36.87 | 34400 | 0.0716 | 0.0723 | |
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| 0.2448 | 37.3 | 34800 | 0.0711 | 0.0725 | |
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| 0.248 | 37.73 | 35200 | 0.0710 | 0.0727 | |
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| 0.2568 | 38.16 | 35600 | 0.0710 | 0.0720 | |
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| 0.2471 | 38.59 | 36000 | 0.0707 | 0.0725 | |
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| 0.2464 | 39.01 | 36400 | 0.0705 | 0.0719 | |
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| 0.2477 | 39.44 | 36800 | 0.0706 | 0.0727 | |
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| 0.2482 | 39.87 | 37200 | 0.0707 | 0.0724 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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