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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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tags: |
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- automatic-speech-recognition |
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- CLEAR-Global/chichewa_34_102h |
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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: w2v-bert-2.0-chichewa_34_102h |
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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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# w2v-bert-2.0-chichewa_34_102h |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the CLEAR-GLOBAL/CHICHEWA_34_102H - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2991 |
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- Wer: 0.3874 |
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- Cer: 0.1111 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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_ratio: 0.1 |
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- training_steps: 100000 |
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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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| 2.2628 | 0.7877 | 1000 | 2.6113 | 0.9981 | 0.7664 | |
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| 0.1158 | 1.5750 | 2000 | 0.7048 | 0.6111 | 0.1786 | |
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| 0.0535 | 2.3623 | 3000 | 0.5161 | 0.5307 | 0.1527 | |
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| 0.0471 | 3.1497 | 4000 | 0.4501 | 0.4873 | 0.1434 | |
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| 0.0452 | 3.9374 | 5000 | 0.4284 | 0.4806 | 0.1410 | |
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| 0.0277 | 4.7247 | 6000 | 0.3880 | 0.4649 | 0.1387 | |
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| 0.0441 | 5.5120 | 7000 | 0.4015 | 0.4461 | 0.1294 | |
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| 0.0177 | 6.2993 | 8000 | 0.3798 | 0.4290 | 0.1209 | |
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| 0.0198 | 7.0866 | 9000 | 0.3330 | 0.4027 | 0.1171 | |
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| 0.0141 | 7.8744 | 10000 | 0.3333 | 0.4307 | 0.1213 | |
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| 0.0237 | 8.6617 | 11000 | 0.3653 | 0.4294 | 0.1259 | |
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| 0.014 | 9.4490 | 12000 | 0.3118 | 0.4048 | 0.1162 | |
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| 0.0079 | 10.2363 | 13000 | 0.2991 | 0.3874 | 0.1109 | |
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| 0.0106 | 11.0236 | 14000 | 0.3455 | 0.4008 | 0.1193 | |
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| 0.0089 | 11.8113 | 15000 | 0.3658 | 0.4091 | 0.1249 | |
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| 0.0068 | 12.5987 | 16000 | 0.3054 | 0.3918 | 0.1124 | |
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| 0.007 | 13.3860 | 17000 | 0.3255 | 0.3785 | 0.1114 | |
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| 0.0108 | 14.1733 | 18000 | 0.3393 | 0.4045 | 0.1152 | |
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### Framework versions |
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- Transformers 4.48.1 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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