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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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- common_voice_16_0
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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-krd-colab-CV16.0
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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: common_voice_16_0
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type: common_voice_16_0
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config: ckb
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split: test
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args: ckb
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metrics:
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- name: Wer
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type: wer
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value: 0.23061901252763448
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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-krd-colab-CV16.0
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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 common_voice_16_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2704
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- Wer: 0.2306
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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: 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: 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: 10
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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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| 2.283 | 0.7979 | 300 | 0.3271 | 0.3871 |
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| 0.2931 | 1.5957 | 600 | 0.2957 | 0.3468 |
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| 0.2358 | 2.3936 | 900 | 0.2746 | 0.3299 |
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| 0.1842 | 3.1915 | 1200 | 0.2473 | 0.2846 |
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| 0.1532 | 3.9894 | 1500 | 0.2257 | 0.2632 |
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| 0.1198 | 4.7872 | 1800 | 0.2403 | 0.2600 |
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| 0.1027 | 5.5851 | 2100 | 0.2239 | 0.2513 |
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| 0.0837 | 6.3830 | 2400 | 0.2310 | 0.2591 |
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| 0.0678 | 7.1809 | 2700 | 0.2295 | 0.2402 |
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| 0.0527 | 7.9787 | 3000 | 0.2428 | 0.2334 |
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| 0.0374 | 8.7766 | 3300 | 0.2448 | 0.2347 |
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| 0.0298 | 9.5745 | 3600 | 0.2704 | 0.2306 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu118
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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