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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: wav2vec2-300m-ft-soft-skill |
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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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# wav2vec2-300m-ft-soft-skill |
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This model is a fine-tuned version of [glob-asr/xls-r-es-test-lm](https://huggingface.co/glob-asr/xls-r-es-test-lm) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7447 |
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- Accuracy: 0.6827 |
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- F1 Micro: 0.3514 |
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- F1 Macro: 0.6827 |
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- Precision Micro: 0.6827 |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 10 |
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- seed: 42 |
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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: 10 |
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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 | Accuracy | F1 Micro | F1 Macro | Precision Micro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:| |
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| 0.823 | 0.51 | 100 | 0.6821 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.7122 | 1.02 | 200 | 0.6767 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6706 | 1.52 | 300 | 0.6768 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.7096 | 2.03 | 400 | 0.6791 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6909 | 2.54 | 500 | 0.6780 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6861 | 3.05 | 600 | 0.6779 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6842 | 3.55 | 700 | 0.6773 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6887 | 4.06 | 800 | 0.6764 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6766 | 4.57 | 900 | 0.6803 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6964 | 5.08 | 1000 | 0.6819 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6515 | 5.58 | 1100 | 0.6788 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6608 | 6.09 | 1200 | 0.6864 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6171 | 6.6 | 1300 | 0.6980 | 0.7589 | 0.2876 | 0.7589 | 0.7589 | |
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| 0.6292 | 7.11 | 1400 | 0.7172 | 0.7386 | 0.3119 | 0.7386 | 0.7386 | |
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| 0.6015 | 7.61 | 1500 | 0.6988 | 0.7462 | 0.3212 | 0.7462 | 0.7462 | |
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| 0.6236 | 8.12 | 1600 | 0.7493 | 0.6954 | 0.3432 | 0.6954 | 0.6954 | |
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| 0.5643 | 8.63 | 1700 | 0.7250 | 0.7107 | 0.3466 | 0.7107 | 0.7107 | |
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| 0.6134 | 9.14 | 1800 | 0.7561 | 0.6751 | 0.3565 | 0.6751 | 0.6751 | |
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| 0.5642 | 9.64 | 1900 | 0.7447 | 0.6827 | 0.3514 | 0.6827 | 0.6827 | |
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### Framework versions |
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- Transformers 4.21.3 |
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- Pytorch 1.8.1+cu111 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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