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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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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: wav2vec2_xls_r_300m_BIG-C_Bemba_20hr_v5 |
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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_xls_r_300m_BIG-C_Bemba_20hr_v5 |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5637 |
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- Model Preparation Time: 0.0067 |
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- Wer: 0.4975 |
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- Cer: 0.1239 |
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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: 0.0003 |
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- train_batch_size: 32 |
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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: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 100 |
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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 | Model Preparation Time | Wer | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:------:| |
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| 6.9607 | 1.0 | 154 | inf | 0.0067 | 1.0 | 1.0 | |
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| 2.4999 | 2.0 | 308 | inf | 0.0067 | 0.9981 | 0.2571 | |
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| 0.8598 | 3.0 | 462 | inf | 0.0067 | 0.6688 | 0.1653 | |
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| 0.6842 | 4.0 | 616 | inf | 0.0067 | 0.7770 | 0.1976 | |
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| 0.6052 | 5.0 | 770 | inf | 0.0067 | 0.5482 | 0.1443 | |
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| 0.557 | 6.0 | 924 | inf | 0.0067 | 0.5141 | 0.1334 | |
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| 0.5169 | 7.0 | 1078 | inf | 0.0067 | 0.6443 | 0.1941 | |
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| 0.4708 | 8.0 | 1232 | inf | 0.0067 | 0.5685 | 0.1573 | |
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| 0.4684 | 9.0 | 1386 | inf | 0.0067 | 0.5414 | 0.1489 | |
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| 0.5716 | 10.0 | 1540 | inf | 0.0067 | 0.6515 | 0.1929 | |
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| 1.1996 | 11.0 | 1694 | inf | 0.0067 | 0.9517 | 0.3363 | |
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| 1.4771 | 12.0 | 1848 | inf | 0.0067 | 0.9655 | 0.3621 | |
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| 1.3761 | 13.0 | 2002 | inf | 0.0067 | 0.8545 | 0.2684 | |
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| 1.6064 | 14.0 | 2156 | inf | 0.0067 | 1.0 | 0.3888 | |
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| 1.8647 | 15.0 | 2310 | inf | 0.0067 | 0.9999 | 0.5035 | |
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| 1.7455 | 16.0 | 2464 | inf | 0.0067 | 1.0 | 0.3958 | |
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| 1.6089 | 17.0 | 2618 | inf | 0.0067 | 1.0 | 0.3634 | |
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| 1.4697 | 18.0 | 2772 | inf | 0.0067 | 0.9999 | 0.3466 | |
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| 1.4016 | 19.0 | 2926 | inf | 0.0067 | 1.0 | 0.3309 | |
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| 1.3563 | 20.0 | 3080 | inf | 0.0067 | 1.0 | 0.3232 | |
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| 1.3217 | 21.0 | 3234 | inf | 0.0067 | 0.9999 | 0.3116 | |
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### Framework versions |
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- Transformers 4.43.4 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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