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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: 0.1896 |
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- Accuracy: 0.96 |
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- F1: 0.9601 |
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- Recall: 0.96 |
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- Precision: 0.9606 |
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- Mcc: 0.9501 |
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- Auc: 0.9939 |
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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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| 1.5599 | 1.0 | 200 | 1.5446 | 0.415 | 0.3951 | 0.4150 | 0.6762 | 0.3213 | 0.8445 | |
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| 1.1707 | 2.0 | 400 | 1.0171 | 0.7575 | 0.7502 | 0.7575 | 0.7665 | 0.7023 | 0.9487 | |
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| 0.7857 | 3.0 | 600 | 0.7125 | 0.8375 | 0.8311 | 0.8375 | 0.8453 | 0.8008 | 0.9667 | |
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| 0.5713 | 4.0 | 800 | 0.5097 | 0.88 | 0.8794 | 0.8800 | 0.8929 | 0.8536 | 0.9874 | |
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| 0.4225 | 5.0 | 1000 | 0.3919 | 0.9075 | 0.9076 | 0.9075 | 0.9116 | 0.8853 | 0.9894 | |
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| 0.5846 | 6.0 | 1200 | 0.3119 | 0.9325 | 0.9327 | 0.9325 | 0.9355 | 0.9163 | 0.9883 | |
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| 0.3004 | 7.0 | 1400 | 0.2308 | 0.9475 | 0.9477 | 0.9475 | 0.9487 | 0.9346 | 0.9925 | |
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| 0.3011 | 8.0 | 1600 | 0.1974 | 0.955 | 0.9551 | 0.9550 | 0.9557 | 0.9439 | 0.9940 | |
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| 0.138 | 9.0 | 1800 | 0.1851 | 0.96 | 0.9601 | 0.96 | 0.9606 | 0.9501 | 0.9932 | |
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| 0.1582 | 10.0 | 2000 | 0.1896 | 0.96 | 0.9601 | 0.96 | 0.9606 | 0.9501 | 0.9939 | |
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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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