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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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metrics: |
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- wer |
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model-index: |
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- name: w2v-bert-2.0-wol-v1 |
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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-wol-v1 |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1008 |
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- Wer: 0.0792 |
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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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| 1.6351 | 0.6857 | 300 | 0.2974 | 0.3040 | |
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| 0.4591 | 1.3714 | 600 | 0.2215 | 0.2307 | |
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| 0.3833 | 2.0571 | 900 | 0.1950 | 0.1900 | |
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| 0.329 | 2.7429 | 1200 | 0.1637 | 0.1614 | |
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| 0.2797 | 3.4286 | 1500 | 0.1515 | 0.1479 | |
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| 0.2558 | 4.1143 | 1800 | 0.1435 | 0.1337 | |
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| 0.2166 | 4.8 | 2100 | 0.1296 | 0.1295 | |
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| 0.1876 | 5.4857 | 2400 | 0.1178 | 0.1129 | |
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| 0.1695 | 6.1714 | 2700 | 0.1107 | 0.1005 | |
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| 0.137 | 6.8571 | 3000 | 0.1064 | 0.0933 | |
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| 0.1078 | 7.5429 | 3300 | 0.1049 | 0.0929 | |
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| 0.0904 | 8.2286 | 3600 | 0.1002 | 0.0871 | |
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| 0.0685 | 8.9143 | 3900 | 0.0973 | 0.0810 | |
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| 0.049 | 9.6 | 4200 | 0.1008 | 0.0792 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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