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README.md
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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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- 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-mongolian-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: mn
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split: test
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args: mn
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metrics:
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- name: Wer
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type: wer
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value: 0.32760495975636283
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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-mongolian-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: 0.5150
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- Wer: 0.3276
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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: 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: 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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| 3.6861 | 2.3636 | 300 | 0.6354 | 0.5386 |
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| 0.6872 | 4.7273 | 600 | 0.5736 | 0.4390 |
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| 0.3496 | 7.0870 | 900 | 0.5391 | 0.3559 |
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| 0.1483 | 9.4506 | 1200 | 0.5150 | 0.3276 |
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### Framework versions
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- Transformers 4.47.1
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- Pytorch 2.5.1+cu121
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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