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--- |
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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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- generated_from_trainer |
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datasets: |
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- fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: w2v-bert2-pashto-augmented |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: fleurs |
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type: fleurs |
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config: ps_af |
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split: test |
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args: ps_af |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.34313876482365624 |
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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-bert2-pashto-augmented |
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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 fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5954 |
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- Wer: 0.3431 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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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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- training_steps: 700 |
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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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| 3.0422 | 1.1713 | 100 | 3.0380 | 0.9640 | |
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| 2.3141 | 2.3426 | 200 | 2.0336 | 0.9464 | |
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| 0.7365 | 3.5139 | 300 | 0.6768 | 0.4520 | |
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| 0.557 | 4.6852 | 400 | 0.6051 | 0.3913 | |
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| 0.5101 | 5.8565 | 500 | 0.6571 | 0.3853 | |
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| 0.3803 | 7.0278 | 600 | 0.5946 | 0.3497 | |
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| 0.2452 | 8.1991 | 700 | 0.5954 | 0.3431 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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