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--- |
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library_name: peft |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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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model-index: |
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- name: distilbert-base-uncased-lora-text-classification |
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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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# distilbert-base-uncased-lora-text-classification |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8405 |
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- Accuracy: {'accuracy': 0.898} |
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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: 0.001 |
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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: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------------:| |
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| No log | 1.0 | 250 | 0.7051 | {'accuracy': 0.761} | |
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| 0.4561 | 2.0 | 500 | 0.9751 | {'accuracy': 0.793} | |
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| 0.4561 | 3.0 | 750 | 0.4668 | {'accuracy': 0.882} | |
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| 0.2196 | 4.0 | 1000 | 0.5190 | {'accuracy': 0.895} | |
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| 0.2196 | 5.0 | 1250 | 0.6079 | {'accuracy': 0.905} | |
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| 0.0804 | 6.0 | 1500 | 0.7639 | {'accuracy': 0.895} | |
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| 0.0804 | 7.0 | 1750 | 0.8768 | {'accuracy': 0.892} | |
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| 0.0119 | 8.0 | 2000 | 0.8436 | {'accuracy': 0.893} | |
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| 0.0119 | 9.0 | 2250 | 0.8417 | {'accuracy': 0.897} | |
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| 0.001 | 10.0 | 2500 | 0.8405 | {'accuracy': 0.898} | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.20.3 |
