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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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language:
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- tr
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license: apache-2.0
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base_model: facebook/wav2vec2-xls-r-300m
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tags:
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_17
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model-index:
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- name: 'Wav2Vec2-XLS-TR '
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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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# Wav2Vec2-XLS-TR
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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 17 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3285
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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: 32
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) 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: 30
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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 |
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|:-------------:|:-------:|:-----:|:---------------:|
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| 5.5445 | 0.2757 | 400 | 1.2160 |
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| 0.7341 | 0.5513 | 800 | 0.6683 |
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| 0.5195 | 0.8270 | 1200 | 0.5313 |
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| 0.4561 | 1.1027 | 1600 | 0.4837 |
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| 0.4016 | 1.3784 | 2000 | 0.4725 |
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| 0.3945 | 1.6540 | 2400 | 0.4570 |
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| 0.3756 | 1.9297 | 2800 | 0.4284 |
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| 0.3341 | 2.2054 | 3200 | 0.4283 |
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| 0.3316 | 2.4810 | 3600 | 0.3945 |
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| 0.3333 | 2.7567 | 4000 | 0.4283 |
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| 0.3224 | 3.0324 | 4400 | 0.3993 |
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| 0.2924 | 3.3081 | 4800 | 0.4216 |
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| 0.3012 | 3.5837 | 5200 | 0.3729 |
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| 0.2889 | 3.8594 | 5600 | 0.3962 |
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| 0.2767 | 4.1351 | 6000 | 0.4037 |
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| 0.2714 | 4.4108 | 6400 | 0.3740 |
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| 0.2721 | 4.6864 | 6800 | 0.3821 |
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| 0.2673 | 4.9621 | 7200 | 0.3580 |
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| 0.2407 | 5.2378 | 7600 | 0.3758 |
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| 0.2525 | 5.5134 | 8000 | 0.4067 |
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| 0.2477 | 5.7891 | 8400 | 0.3675 |
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| 0.2433 | 6.0648 | 8800 | 0.3653 |
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| 0.229 | 6.3405 | 9200 | 0.3485 |
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| 0.2326 | 6.6161 | 9600 | 0.3674 |
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| 0.2288 | 6.8918 | 10000 | 0.3664 |
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| 0.2199 | 7.1675 | 10400 | 0.3656 |
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| 0.2094 | 7.4431 | 10800 | 0.3389 |
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| 0.2191 | 7.7188 | 11200 | 0.3446 |
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| 0.2149 | 7.9945 | 11600 | 0.3489 |
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| 0.1967 | 8.2702 | 12000 | 0.3482 |
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| 0.2042 | 8.5458 | 12400 | 0.3464 |
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| 0.2043 | 8.8215 | 12800 | 0.3517 |
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| 0.1919 | 9.0972 | 13200 | 0.3408 |
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| 0.1844 | 9.3728 | 13600 | 0.3465 |
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| 0.1906 | 9.6485 | 14000 | 0.3349 |
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| 0.1868 | 9.9242 | 14400 | 0.3282 |
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| 0.1732 | 10.1999 | 14800 | 0.3604 |
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| 0.1715 | 10.4755 | 15200 | 0.3413 |
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| 0.1734 | 10.7512 | 15600 | 0.3309 |
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| 0.1785 | 11.0269 | 16000 | 0.3351 |
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| 0.1643 | 11.3025 | 16400 | 0.3326 |
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| 0.1603 | 11.5782 | 16800 | 0.3205 |
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| 0.1662 | 11.8539 | 17200 | 0.3332 |
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| 0.1561 | 12.1296 | 17600 | 0.3311 |
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| 0.1512 | 12.4052 | 18000 | 0.3322 |
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| 0.1509 | 12.6809 | 18400 | 0.3227 |
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| 0.1516 | 12.9566 | 18800 | 0.3338 |
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| 0.1493 | 13.2323 | 19200 | 0.3439 |
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| 0.1426 | 13.5079 | 19600 | 0.3447 |
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| 0.143 | 13.7836 | 20000 | 0.3299 |
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| 0.1398 | 14.0593 | 20400 | 0.3273 |
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| 0.1351 | 14.3349 | 20800 | 0.3281 |
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| 0.1384 | 14.6106 | 21200 | 0.3333 |
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| 0.1335 | 14.8863 | 21600 | 0.3311 |
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| 0.1291 | 15.1620 | 22000 | 0.3230 |
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| 0.1259 | 15.4376 | 22400 | 0.3301 |
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| 0.1294 | 15.7133 | 22800 | 0.3446 |
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| 0.1269 | 15.9890 | 23200 | 0.3271 |
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| 0.1196 | 16.2646 | 23600 | 0.3204 |
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| 0.1166 | 16.5403 | 24000 | 0.3031 |
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| 0.12 | 16.8160 | 24400 | 0.3258 |
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| 0.1163 | 17.0917 | 24800 | 0.3408 |
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| 0.1101 | 17.3673 | 25200 | 0.3246 |
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| 0.1142 | 17.6430 | 25600 | 0.3201 |
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| 0.1121 | 17.9187 | 26000 | 0.3198 |
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| 0.1044 | 18.1943 | 26400 | 0.3441 |
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| 0.105 | 18.4700 | 26800 | 0.3441 |
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| 0.1032 | 18.7457 | 27200 | 0.3252 |
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| 0.104 | 19.0214 | 27600 | 0.3170 |
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| 0.0968 | 19.2970 | 28000 | 0.3363 |
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| 0.0946 | 19.5727 | 28400 | 0.3100 |
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| 0.0974 | 19.8484 | 28800 | 0.3128 |
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| 0.0889 | 20.1241 | 29200 | 0.3325 |
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| 0.0887 | 20.3997 | 29600 | 0.3276 |
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| 0.0891 | 20.6754 | 30000 | 0.3253 |
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| 0.0937 | 20.9511 | 30400 | 0.3270 |
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| 0.0854 | 21.2267 | 30800 | 0.3294 |
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| 0.0864 | 21.5024 | 31200 | 0.3352 |
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| 0.0864 | 21.7781 | 31600 | 0.3279 |
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| 0.0829 | 22.0538 | 32000 | 0.3245 |
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| 0.0799 | 22.3294 | 32400 | 0.3329 |
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| 0.0811 | 22.6051 | 32800 | 0.3295 |
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| 0.0777 | 22.8808 | 33200 | 0.3204 |
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| 0.074 | 23.1564 | 33600 | 0.3286 |
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| 0.0762 | 23.4321 | 34000 | 0.3326 |
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| 0.0765 | 23.7078 | 34400 | 0.3061 |
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| 0.0745 | 23.9835 | 34800 | 0.3313 |
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| 0.0702 | 24.2591 | 35200 | 0.3131 |
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| 0.0684 | 24.5348 | 35600 | 0.3236 |
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| 0.0689 | 24.8105 | 36000 | 0.3152 |
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| 0.0716 | 25.0861 | 36400 | 0.3272 |
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| 0.0609 | 25.3618 | 36800 | 0.3294 |
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| 0.0607 | 25.6375 | 37200 | 0.3366 |
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| 0.0624 | 25.9132 | 37600 | 0.3215 |
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| 0.0614 | 26.1888 | 38000 | 0.3236 |
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| 0.0599 | 26.4645 | 38400 | 0.3321 |
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| 0.0608 | 26.7402 | 38800 | 0.3311 |
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| 0.0558 | 27.0159 | 39200 | 0.3368 |
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| 0.0589 | 27.2915 | 39600 | 0.3217 |
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| 0.0573 | 27.5672 | 40000 | 0.3320 |
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| 0.0546 | 27.8429 | 40400 | 0.3316 |
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| 0.0526 | 28.1185 | 40800 | 0.3285 |
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| 0.0501 | 28.3942 | 41200 | 0.3287 |
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| 0.0515 | 28.6699 | 41600 | 0.3261 |
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| 0.0507 | 28.9456 | 42000 | 0.3316 |
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| 0.0539 | 29.2212 | 42400 | 0.3285 |
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| 0.0489 | 29.4969 | 42800 | 0.3319 |
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| 0.053 | 29.7726 | 43200 | 0.3285 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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