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--- |
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base_model: Osolon/wav2vec2-large-xls-r-300m-pl |
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library_name: transformers |
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license: apache-2.0 |
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metrics: |
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- wer |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-full_v2 |
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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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# wav2vec2-full_v2 |
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This model is a fine-tuned version of [Osolon/wav2vec2-large-xls-r-300m-pl](https://huggingface.co/Osolon/wav2vec2-large-xls-r-300m-pl) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: inf |
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- Wer: 0.2842 |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 100 |
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- num_epochs: 4 |
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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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| 0.2498 | 0.1241 | 400 | inf | 0.2197 | |
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| 0.2049 | 0.2481 | 800 | inf | 0.1586 | |
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| 0.1798 | 0.3722 | 1200 | inf | 0.1380 | |
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| 0.1527 | 0.4963 | 1600 | inf | 0.1344 | |
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| 0.1588 | 0.6203 | 2000 | inf | 0.1336 | |
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| 0.1401 | 0.7444 | 2400 | inf | 0.1168 | |
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| 0.1336 | 0.8685 | 2800 | inf | 0.1064 | |
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| 0.1308 | 0.9926 | 3200 | inf | 0.0983 | |
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| 0.1177 | 1.1166 | 3600 | inf | 0.0911 | |
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| 0.1261 | 1.2407 | 4000 | inf | 0.0878 | |
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| 0.2208 | 1.3648 | 4400 | inf | 0.1019 | |
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| 0.2814 | 1.4888 | 4800 | inf | 0.1836 | |
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| 0.4642 | 1.6129 | 5200 | inf | 0.2173 | |
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| 0.6645 | 1.7370 | 5600 | inf | 0.6257 | |
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| 0.8667 | 1.8610 | 6000 | inf | 0.7893 | |
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| 0.7724 | 1.9851 | 6400 | inf | 0.7634 | |
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| 0.6747 | 2.1092 | 6800 | inf | 0.5521 | |
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| 0.5989 | 2.2333 | 7200 | inf | 0.5029 | |
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| 0.5234 | 2.3573 | 7600 | inf | 0.4523 | |
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| 0.4844 | 2.4814 | 8000 | inf | 0.3895 | |
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| 0.4708 | 2.6055 | 8400 | inf | 0.3319 | |
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| 0.4701 | 2.7295 | 8800 | inf | 0.2781 | |
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| 0.4665 | 2.8536 | 9200 | inf | 0.2713 | |
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| 0.4652 | 2.9777 | 9600 | inf | 0.2844 | |
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| 0.4539 | 3.1017 | 10000 | inf | 0.2842 | |
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| 0.4692 | 3.2258 | 10400 | inf | 0.2842 | |
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| 0.4658 | 3.3499 | 10800 | inf | 0.2842 | |
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| 0.4602 | 3.4739 | 11200 | inf | 0.2842 | |
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| 0.4648 | 3.5980 | 11600 | inf | 0.2842 | |
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| 0.4608 | 3.7221 | 12000 | inf | 0.2842 | |
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| 0.4677 | 3.8462 | 12400 | inf | 0.2842 | |
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| 0.4731 | 3.9702 | 12800 | inf | 0.2842 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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