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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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base_model: facebook/w2v-bert-2.0 |
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datasets: |
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- generator |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-bert-fon |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: generator |
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type: generator |
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config: default |
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split: train |
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args: default |
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metrics: |
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- type: wer |
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value: 0.13241653693132677 |
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name: Wer |
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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-bert-fon |
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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 generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1612 |
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- Wer: 0.1324 |
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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: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_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: 500 |
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- num_epochs: 4 |
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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 | 0.18 | 250 | 1.2212 | 0.8079 | |
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| 2.1756 | 0.35 | 500 | 0.6697 | 0.6058 | |
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| 2.1756 | 0.53 | 750 | 0.5137 | 0.4606 | |
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| 0.5041 | 0.7 | 1000 | 0.4337 | 0.4234 | |
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| 0.5041 | 0.88 | 1250 | 0.3452 | 0.3529 | |
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| 0.426 | 1.05 | 1500 | 0.2770 | 0.2910 | |
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| 0.426 | 1.23 | 1750 | 0.2681 | 0.2439 | |
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| 0.2916 | 1.4 | 2000 | 0.2423 | 0.2155 | |
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| 0.2916 | 1.58 | 2250 | 0.2342 | 0.2077 | |
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| 0.2591 | 1.75 | 2500 | 0.1986 | 0.1791 | |
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| 0.2591 | 1.93 | 2750 | 0.1864 | 0.1597 | |
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| 0.2261 | 2.1 | 3000 | 0.1712 | 0.1419 | |
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| 0.2261 | 2.28 | 3250 | 0.1786 | 0.1497 | |
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| 0.1564 | 2.45 | 3500 | 0.1612 | 0.1324 | |
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| 0.1564 | 2.63 | 3750 | 0.1730 | 0.1591 | |
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| 0.1542 | 2.8 | 4000 | 0.1558 | 0.1364 | |
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| 0.1542 | 2.98 | 4250 | 0.1493 | 0.1581 | |
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| 0.1559 | 3.15 | 4500 | 0.1489 | 0.1347 | |
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| 0.1559 | 3.33 | 4750 | 0.2036 | 0.1486 | |
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| 0.1992 | 3.5 | 5000 | 0.2644 | 0.1582 | |
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| 0.1992 | 3.68 | 5250 | 0.2401 | 0.1878 | |
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| 0.291 | 3.85 | 5500 | 0.2409 | 0.1749 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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