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--- |
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language: |
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- all |
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license: apache-2.0 |
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tags: |
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- fleurs-asr |
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- google/xtreme_s |
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- generated_from_trainer |
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datasets: |
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- google/xtreme_s |
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model-index: |
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- name: xtreme_s_xlsr_300m_fleurs_asr_western_european |
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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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# xtreme_s_xlsr_300m_fleurs_asr_western_european |
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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 GOOGLE/XTREME_S - FLEURS.ALL dataset. |
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It achieves the following results on the evaluation set: |
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- Cer: 0.2484 |
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- Cer Ast Es: 0.1598 |
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- Cer Bs Ba: 0.1749 |
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- Cer Ca Es: 0.1655 |
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- Cer Cy Gb: 0.2280 |
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- Cer Da Dk: 0.3616 |
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- Cer De De: 0.1287 |
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- Cer El Gr: 0.6020 |
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- Cer En Us: 0.1938 |
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- Cer Es 419: 0.1288 |
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- Cer Fi Fi: 0.2050 |
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- Cer Fr Fr: 0.1811 |
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- Cer Ga Ie: 0.4474 |
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- Cer Gl Es: 0.1324 |
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- Cer Hr Hr: 0.1555 |
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- Cer Hu Hu: 0.3911 |
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- Cer Is Is: 0.4646 |
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- Cer It It: 0.1283 |
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- Cer Kea Cv: 0.1818 |
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- Cer Lb Lu: 0.2594 |
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- Cer Mt Mt: 0.3628 |
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- Cer Nb No: 0.2254 |
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- Cer Nl Nl: 0.1790 |
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- Cer Oci Fr: 0.2159 |
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- Cer Pt Br: 0.2275 |
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- Cer Sv Se: 0.3092 |
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- Loss: 1.3089 |
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- Loss Ast Es: 0.7715 |
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- Loss Bs Ba: 0.7378 |
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- Loss Ca Es: 0.7868 |
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- Loss Cy Gb: 1.1441 |
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- Loss Da Dk: 1.9130 |
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- Loss De De: 0.5391 |
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- Loss El Gr: 3.4904 |
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- Loss En Us: 0.9632 |
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- Loss Es 419: 0.6186 |
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- Loss Fi Fi: 0.8953 |
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- Loss Fr Fr: 0.9076 |
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- Loss Ga Ie: 3.0217 |
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- Loss Gl Es: 0.5788 |
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- Loss Hr Hr: 0.6462 |
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- Loss Hu Hu: 1.9029 |
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- Loss Is Is: 2.6551 |
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- Loss It It: 0.6052 |
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- Loss Kea Cv: 0.9107 |
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- Loss Lb Lu: 1.3705 |
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- Loss Mt Mt: 2.3651 |
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- Loss Nb No: 1.1518 |
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- Loss Nl Nl: 0.8490 |
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- Loss Oci Fr: 1.1421 |
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- Loss Pt Br: 1.1641 |
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- Loss Sv Se: 1.5910 |
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- Wer: 0.6451 |
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- Wer Ast Es: 0.4654 |
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- Wer Bs Ba: 0.5443 |
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- Wer Ca Es: 0.4979 |
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- Wer Cy Gb: 0.5962 |
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- Wer Da Dk: 0.8455 |
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- Wer De De: 0.4221 |
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- Wer El Gr: 0.9805 |
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- Wer En Us: 0.4556 |
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- Wer Es 419: 0.3928 |
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- Wer Fi Fi: 0.8116 |
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- Wer Fr Fr: 0.4690 |
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- Wer Ga Ie: 0.8519 |
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- Wer Gl Es: 0.4245 |
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- Wer Hr Hr: 0.4895 |
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- Wer Hu Hu: 0.9099 |
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- Wer Is Is: 0.9960 |
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- Wer It It: 0.4415 |
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- Wer Kea Cv: 0.5202 |
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- Wer Lb Lu: 0.7225 |
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- Wer Mt Mt: 1.0096 |
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- Wer Nb No: 0.6541 |
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- Wer Nl Nl: 0.5257 |
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- Wer Oci Fr: 0.5770 |
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- Wer Pt Br: 0.6685 |
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- Wer Sv Se: 0.8546 |
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- Predict Samples: 20043 |
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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: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 8 |
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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: 1000 |
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- num_epochs: 20.0 |
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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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| 3.1411 | 0.49 | 500 | 3.1673 | 1.0 | 1.0 | |
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| 0.6397 | 0.97 | 1000 | 0.9039 | 0.7171 | 0.2862 | |
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| 0.4033 | 1.46 | 1500 | 0.8914 | 0.6862 | 0.2763 | |
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| 0.3473 | 1.94 | 2000 | 0.8017 | 0.6505 | 0.2536 | |
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| 0.3143 | 2.43 | 2500 | 0.8568 | 0.6566 | 0.2627 | |
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| 0.3004 | 2.91 | 3000 | 0.8898 | 0.6640 | 0.2686 | |
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| 0.282 | 3.4 | 3500 | 0.8489 | 0.6637 | 0.2571 | |
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| 0.2489 | 3.88 | 4000 | 0.8955 | 0.6744 | 0.2691 | |
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| 0.1706 | 4.37 | 4500 | 0.9190 | 0.6788 | 0.2688 | |
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| 0.3336 | 4.85 | 5000 | 0.8915 | 0.6594 | 0.2572 | |
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| 0.1426 | 5.34 | 5500 | 0.9501 | 0.6784 | 0.2686 | |
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| 0.2301 | 5.83 | 6000 | 1.0217 | 0.6719 | 0.2735 | |
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| 0.1325 | 6.31 | 6500 | 0.9578 | 0.6691 | 0.2655 | |
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| 0.1145 | 6.8 | 7000 | 0.9129 | 0.6680 | 0.2593 | |
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| 0.1202 | 7.28 | 7500 | 0.9646 | 0.6749 | 0.2619 | |
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| 0.143 | 7.77 | 8000 | 0.9200 | 0.6554 | 0.2554 | |
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| 0.1012 | 8.25 | 8500 | 0.9553 | 0.6787 | 0.2628 | |
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| 0.1018 | 8.74 | 9000 | 0.9455 | 0.6445 | 0.2511 | |
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| 0.1148 | 9.22 | 9500 | 1.0206 | 0.6725 | 0.2629 | |
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| 0.0794 | 9.71 | 10000 | 0.9305 | 0.6547 | 0.2526 | |
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| 0.2891 | 10.19 | 10500 | 1.0424 | 0.6709 | 0.2570 | |
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| 0.1665 | 10.68 | 11000 | 0.9760 | 0.6596 | 0.2507 | |
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| 0.1956 | 11.17 | 11500 | 0.9549 | 0.6340 | 0.2440 | |
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| 0.0828 | 11.65 | 12000 | 0.9598 | 0.6403 | 0.2460 | |
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| 0.059 | 12.14 | 12500 | 0.9972 | 0.6574 | 0.2531 | |
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| 0.0505 | 12.62 | 13000 | 0.9836 | 0.6534 | 0.2525 | |
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| 0.0336 | 13.11 | 13500 | 1.0619 | 0.6564 | 0.2519 | |
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| 0.0435 | 13.59 | 14000 | 1.0844 | 0.6480 | 0.2543 | |
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| 0.0216 | 14.08 | 14500 | 1.1084 | 0.6512 | 0.2521 | |
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| 0.0265 | 14.56 | 15000 | 1.1152 | 0.6607 | 0.2563 | |
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| 0.0975 | 15.05 | 15500 | 1.1060 | 0.6456 | 0.2471 | |
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| 0.1396 | 15.53 | 16000 | 1.1100 | 0.6337 | 0.2418 | |
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| 0.0701 | 16.02 | 16500 | 1.1731 | 0.6309 | 0.2415 | |
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| 0.1171 | 16.5 | 17000 | 1.1302 | 0.6315 | 0.2396 | |
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| 0.0778 | 16.99 | 17500 | 1.1485 | 0.6379 | 0.2447 | |
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| 0.0642 | 17.48 | 18000 | 1.2009 | 0.6400 | 0.2464 | |
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| 0.0322 | 17.96 | 18500 | 1.2028 | 0.6357 | 0.2425 | |
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| 0.031 | 18.45 | 19000 | 1.2381 | 0.6285 | 0.2416 | |
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| 0.0579 | 18.93 | 19500 | 1.2299 | 0.6265 | 0.2409 | |
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| 0.0628 | 19.42 | 20000 | 1.2582 | 0.6277 | 0.2395 | |
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| 0.074 | 19.9 | 20500 | 1.2572 | 0.6278 | 0.2394 | |
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### Framework versions |
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- Transformers 4.18.0.dev0 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 1.18.4.dev0 |
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- Tokenizers 0.11.6 |
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