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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: ntu-spml/distilhubert |
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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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- precision |
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- recall |
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model-index: |
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- name: distilhubert-finetuned-mixed-data |
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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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# distilhubert-finetuned-mixed-data |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset. |
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- Loss: 0.7808755040168762, |
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- Accuracy: 0.8644688644688645, |
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- F1: 0.8641694609590086, |
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- Precision: 0.8653356589517041, |
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- Recall: 0.8644688644688645, |
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- Confusion Matrix: [[71, 9, 0, 3], [5, 42, 12, 0], [0, 7, 55, 0], [1, 0, 0, 68]] |
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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: 0.0005 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 123 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 40 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Confusion Matrix | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------------------------------------------------------:| |
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| 0.5098 | 40.0000 | 50 | 0.7809 | 0.8645 | 0.8642 | 0.8653 | 0.8645 | [[71, 9, 0, 3], [5, 42, 12, 0], [0, 7, 55, 0], [1, 0, 0, 68]] | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Tokenizers 0.19.1 |
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