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--- |
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library_name: transformers |
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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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- automatic-speech-recognition |
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- sudoping01/malian-languages-dataset |
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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-malian-languages-dogon-dataset |
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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-malian-languages-dogon-dataset |
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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 SUDOPING01/MALIAN-LANGUAGES-DATASET - DOGON dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1688 |
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- Wer: 0.1320 |
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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: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 15.0 |
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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 | |
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|:-------------:|:-------:|:----:|:---------------:|:------:| |
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| No log | 0.4890 | 100 | 3.3308 | 1.0 | |
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| No log | 0.9780 | 200 | 2.6855 | 1.0 | |
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| No log | 1.4645 | 300 | 0.4930 | 0.4569 | |
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| No log | 1.9535 | 400 | 0.2583 | 0.2946 | |
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| 2.4539 | 2.4401 | 500 | 0.2015 | 0.2439 | |
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| 2.4539 | 2.9291 | 600 | 0.1660 | 0.1912 | |
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| 2.4539 | 3.4156 | 700 | 0.1410 | 0.1676 | |
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| 2.4539 | 3.9046 | 800 | 0.1337 | 0.1610 | |
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| 2.4539 | 4.3912 | 900 | 0.1225 | 0.1531 | |
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| 0.1717 | 4.8802 | 1000 | 0.1195 | 0.1402 | |
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| 0.1717 | 5.3667 | 1100 | 0.1282 | 0.1479 | |
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| 0.1717 | 5.8557 | 1200 | 0.1198 | 0.1393 | |
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| 0.1717 | 6.3423 | 1300 | 0.1141 | 0.1334 | |
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| 0.1717 | 6.8313 | 1400 | 0.1167 | 0.1285 | |
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| 0.1059 | 7.3178 | 1500 | 0.1103 | 0.1344 | |
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| 0.1059 | 7.8068 | 1600 | 0.1121 | 0.1230 | |
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| 0.1059 | 8.2934 | 1700 | 0.1206 | 0.1343 | |
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| 0.1059 | 8.7824 | 1800 | 0.1110 | 0.1242 | |
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| 0.1059 | 9.2689 | 1900 | 0.1125 | 0.1238 | |
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| 0.073 | 9.7579 | 2000 | 0.1196 | 0.1275 | |
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| 0.073 | 10.2445 | 2100 | 0.1223 | 0.1204 | |
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| 0.073 | 10.7335 | 2200 | 0.1195 | 0.1200 | |
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| 0.073 | 11.2200 | 2300 | 0.1223 | 0.1225 | |
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| 0.073 | 11.7090 | 2400 | 0.1164 | 0.1204 | |
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| 0.0574 | 12.1956 | 2500 | 0.1211 | 0.1203 | |
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| 0.0574 | 12.6846 | 2600 | 0.1259 | 0.1213 | |
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| 0.0574 | 13.1711 | 2700 | 0.1312 | 0.1285 | |
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| 0.0574 | 13.6601 | 2800 | 0.1399 | 0.1210 | |
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| 0.0574 | 14.1467 | 2900 | 0.1486 | 0.1272 | |
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| 0.0852 | 14.6357 | 3000 | 0.1698 | 0.1280 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.3.1 |
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- Tokenizers 0.21.0 |
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