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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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metrics: |
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- wer |
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model-index: |
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- name: w2v-bert-punjabi |
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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-punjabi |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1810 |
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- Wer: 0.1029 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 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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- training_steps: 30000 |
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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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| 0.4419 | 0.2174 | 2000 | 0.3828 | 0.2268 | |
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| 0.3492 | 0.4348 | 4000 | 0.3401 | 0.1836 | |
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| 0.3205 | 0.6522 | 6000 | 0.2932 | 0.1712 | |
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| 0.2813 | 0.8696 | 8000 | 0.2844 | 0.1590 | |
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| 0.255 | 1.0870 | 10000 | 0.2562 | 0.1469 | |
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| 0.2451 | 1.3043 | 12000 | 0.2431 | 0.1386 | |
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| 0.2305 | 1.5217 | 14000 | 0.2299 | 0.1312 | |
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| 0.2156 | 1.7391 | 16000 | 0.2191 | 0.1274 | |
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| 0.2119 | 1.9565 | 18000 | 0.2269 | 0.1205 | |
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| 0.182 | 2.1739 | 20000 | 0.2091 | 0.1181 | |
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| 0.1789 | 2.3913 | 22000 | 0.1980 | 0.1136 | |
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| 0.1766 | 2.6087 | 24000 | 0.1945 | 0.1092 | |
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| 0.1657 | 2.8261 | 26000 | 0.1881 | 0.1079 | |
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| 0.1461 | 3.0435 | 28000 | 0.1809 | 0.1050 | |
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| 0.1454 | 3.2609 | 30000 | 0.1810 | 0.1029 | |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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