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--- |
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base_model: microsoft/unispeech-sat-base |
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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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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: unispeech-sat-base-finetuned-common_voice |
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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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# unispeech-sat-base-finetuned-common_voice |
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This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co./microsoft/unispeech-sat-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0641 |
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- Accuracy: 0.9875 |
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- F1: 0.9875 |
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- Recall: 0.9875 |
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- Precision: 0.9878 |
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- Mcc: 0.9844 |
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- Auc: 0.9999 |
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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: 8 |
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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_ratio: 0.1 |
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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 | Recall | Precision | Mcc | Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:| |
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| 0.2181 | 1.0 | 200 | 0.0440 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 0.9981 | |
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| 0.0083 | 2.0 | 400 | 0.0609 | 0.9875 | 0.9875 | 0.9875 | 0.9880 | 0.9845 | 0.9987 | |
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| 0.0035 | 3.0 | 600 | 0.0888 | 0.98 | 0.9799 | 0.9800 | 0.9806 | 0.9752 | 0.9991 | |
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| 0.2407 | 4.0 | 800 | 0.1593 | 0.9725 | 0.9726 | 0.9725 | 0.9740 | 0.9660 | 0.9997 | |
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| 0.0859 | 5.0 | 1000 | 0.1234 | 0.9775 | 0.9777 | 0.9775 | 0.9790 | 0.9722 | 0.9999 | |
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| 0.2073 | 6.0 | 1200 | 0.0851 | 0.9825 | 0.9826 | 0.9825 | 0.9832 | 0.9783 | 0.9999 | |
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| 0.0036 | 7.0 | 1400 | 0.0550 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 0.9999 | |
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| 0.0036 | 8.0 | 1600 | 0.0600 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 1.0000 | |
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| 0.0013 | 9.0 | 1800 | 0.0645 | 0.99 | 0.9900 | 0.99 | 0.9903 | 0.9876 | 1.0000 | |
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| 0.0048 | 10.0 | 2000 | 0.0641 | 0.9875 | 0.9875 | 0.9875 | 0.9878 | 0.9844 | 0.9999 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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