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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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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-lg-pt |
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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-large-xls-r-300m-lg-pt |
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This model is a fine-tuned version of [Alvin-Nahabwe/wav2vec2-large-xls-r-300m-gn](https://huggingface.co/Alvin-Nahabwe/wav2vec2-large-xls-r-300m-gn) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2974 |
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- Wer: 0.1465 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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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 | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 0.3826 | 0.81 | 400 | 0.2260 | 0.2142 | |
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| 0.3513 | 1.61 | 800 | 0.2164 | 0.2289 | |
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| 0.3211 | 2.42 | 1200 | 0.1950 | 0.1895 | |
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| 0.2939 | 3.22 | 1600 | 0.1977 | 0.1969 | |
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| 0.2886 | 4.03 | 2000 | 0.1973 | 0.1957 | |
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| 0.2613 | 4.84 | 2400 | 0.1897 | 0.1825 | |
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| 0.2566 | 5.64 | 2800 | 0.1878 | 0.1753 | |
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| 0.2406 | 6.45 | 3200 | 0.1844 | 0.1713 | |
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| 0.2292 | 7.25 | 3600 | 0.1919 | 0.1706 | |
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| 0.2176 | 8.06 | 4000 | 0.1965 | 0.1681 | |
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| 0.2115 | 8.86 | 4400 | 0.1945 | 0.1746 | |
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| 0.1933 | 9.67 | 4800 | 0.2041 | 0.1712 | |
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| 0.1878 | 10.48 | 5200 | 0.2098 | 0.1718 | |
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| 0.1806 | 11.29 | 5600 | 0.2071 | 0.1666 | |
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| 0.1737 | 12.09 | 6000 | 0.2253 | 0.1655 | |
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| 0.1652 | 12.9 | 6400 | 0.2087 | 0.1627 | |
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| 0.1627 | 13.71 | 6800 | 0.2157 | 0.1666 | |
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| 0.1516 | 14.51 | 7200 | 0.2120 | 0.1687 | |
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| 0.1432 | 15.32 | 7600 | 0.2186 | 0.1715 | |
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| 0.1371 | 16.12 | 8000 | 0.2199 | 0.1681 | |
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| 0.1284 | 16.93 | 8400 | 0.2115 | 0.1647 | |
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| 0.1215 | 17.74 | 8800 | 0.2304 | 0.1568 | |
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| 0.115 | 18.55 | 9200 | 0.2322 | 0.1549 | |
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| 0.1122 | 19.35 | 9600 | 0.2427 | 0.1541 | |
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| 0.1041 | 20.16 | 10000 | 0.2512 | 0.1531 | |
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| 0.0999 | 20.96 | 10400 | 0.2526 | 0.1559 | |
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| 0.0929 | 21.77 | 10800 | 0.2591 | 0.1536 | |
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| 0.0877 | 22.58 | 11200 | 0.2645 | 0.1525 | |
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| 0.082 | 23.39 | 11600 | 0.2692 | 0.1494 | |
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| 0.0787 | 24.19 | 12000 | 0.2742 | 0.1530 | |
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| 0.0758 | 25.0 | 12400 | 0.2794 | 0.1484 | |
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| 0.0713 | 25.8 | 12800 | 0.2817 | 0.1493 | |
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| 0.0687 | 26.61 | 13200 | 0.2881 | 0.1491 | |
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| 0.065 | 27.42 | 13600 | 0.2945 | 0.1487 | |
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| 0.0619 | 28.22 | 14000 | 0.2955 | 0.1478 | |
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| 0.0592 | 29.03 | 14400 | 0.2965 | 0.1472 | |
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| 0.0569 | 29.84 | 14800 | 0.2974 | 0.1465 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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