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--- |
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license: apache-2.0 |
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base_model: facebook/hubert-base-ls960 |
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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: hubert-base-ls960-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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# hubert-base-ls960-finetuned-common_voice |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4616 |
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- Accuracy: 0.9375 |
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- F1: 0.9377 |
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- Recall: 0.9375 |
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- Precision: 0.9403 |
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- Mcc: 0.9225 |
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- Auc: 0.9925 |
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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: 3e-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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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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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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| 1.601 | 0.96 | 12 | 1.5594 | 0.385 | 0.3217 | 0.3850 | 0.6064 | 0.2666 | 0.7895 | |
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| 1.5467 | 2.0 | 25 | 1.3344 | 0.67 | 0.6516 | 0.6700 | 0.7185 | 0.6030 | 0.9009 | |
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| 1.4062 | 2.96 | 37 | 1.0521 | 0.8 | 0.7964 | 0.8 | 0.8014 | 0.7521 | 0.9436 | |
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| 1.0881 | 4.0 | 50 | 0.8340 | 0.8525 | 0.8502 | 0.8525 | 0.8677 | 0.8201 | 0.9759 | |
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| 0.9348 | 4.96 | 62 | 0.7227 | 0.89 | 0.8894 | 0.89 | 0.8939 | 0.8639 | 0.9801 | |
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| 0.8596 | 6.0 | 75 | 0.5873 | 0.9275 | 0.9276 | 0.9275 | 0.9300 | 0.9100 | 0.9908 | |
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| 0.7917 | 6.96 | 87 | 0.5208 | 0.93 | 0.9298 | 0.93 | 0.9310 | 0.9128 | 0.9940 | |
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| 0.6721 | 8.0 | 100 | 0.4784 | 0.9475 | 0.9476 | 0.9475 | 0.9491 | 0.9348 | 0.9935 | |
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| 0.6297 | 8.96 | 112 | 0.4734 | 0.9325 | 0.9326 | 0.9325 | 0.9363 | 0.9166 | 0.9916 | |
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| 0.6127 | 9.6 | 120 | 0.4616 | 0.9375 | 0.9377 | 0.9375 | 0.9403 | 0.9225 | 0.9925 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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