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--- |
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library_name: transformers |
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license: cc-by-nc-sa-4.0 |
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base_model: amiriparian/ExHuBERT |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: ExHubert-fine-tuned-persian_v2 |
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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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# ExHubert-fine-tuned-persian_v2 |
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This model is a fine-tuned version of [amiriparian/ExHuBERT](https://huggingface.co/amiriparian/ExHuBERT) 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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- Accuracy: 0.8444 |
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- Precision: 0.8209 |
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- Recall: 0.7483 |
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- F1: 0.7829 |
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- Precision Neutral: 0.8566 |
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- Recall Neutral: 0.9020 |
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- F1 Neutral: 0.8787 |
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- Precision Anger: 0.8209 |
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- Recall Anger: 0.7483 |
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- F1 Anger: 0.7829 |
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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: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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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 | Precision | Recall | F1 | Precision Neutral | Recall Neutral | F1 Neutral | Precision Anger | Recall Anger | F1 Anger | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:| |
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| 0.8717 | 1.0 | 393 | 0.7755 | 0.6454 | 0.5145 | 0.9660 | 0.6714 | 0.9569 | 0.4531 | 0.6150 | 0.5145 | 0.9660 | 0.6714 | |
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| 0.4644 | 2.0 | 786 | 0.4577 | 0.8495 | 0.7785 | 0.8367 | 0.8066 | 0.8974 | 0.8571 | 0.8768 | 0.7785 | 0.8367 | 0.8066 | |
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| 0.4535 | 3.0 | 1179 | 0.4818 | 0.8546 | 0.8 | 0.8163 | 0.8081 | 0.8884 | 0.8776 | 0.8830 | 0.8 | 0.8163 | 0.8081 | |
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| 0.4773 | 4.0 | 1572 | 0.5514 | 0.8189 | 0.7289 | 0.8231 | 0.7732 | 0.8850 | 0.8163 | 0.8493 | 0.7289 | 0.8231 | 0.7732 | |
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| 0.3337 | 5.0 | 1965 | 0.5680 | 0.8112 | 0.7417 | 0.7619 | 0.7517 | 0.8548 | 0.8408 | 0.8477 | 0.7417 | 0.7619 | 0.7517 | |
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| 0.2774 | 6.0 | 2358 | 0.6004 | 0.8367 | 0.8879 | 0.6463 | 0.7480 | 0.8175 | 0.9510 | 0.8792 | 0.8879 | 0.6463 | 0.7480 | |
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| 0.2007 | 7.0 | 2751 | 0.5529 | 0.8546 | 0.8629 | 0.7279 | 0.7897 | 0.8507 | 0.9306 | 0.8889 | 0.8629 | 0.7279 | 0.7897 | |
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| 0.2025 | 8.0 | 3144 | 0.5655 | 0.8444 | 0.8162 | 0.7551 | 0.7845 | 0.8594 | 0.8980 | 0.8782 | 0.8162 | 0.7551 | 0.7845 | |
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| 0.2583 | 9.0 | 3537 | 0.5635 | 0.8444 | 0.8258 | 0.7415 | 0.7814 | 0.8538 | 0.9061 | 0.8792 | 0.8258 | 0.7415 | 0.7814 | |
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| 0.3264 | 10.0 | 3930 | 0.5637 | 0.8444 | 0.8209 | 0.7483 | 0.7829 | 0.8566 | 0.9020 | 0.8787 | 0.8209 | 0.7483 | 0.7829 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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