| --- |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: roberta-base-lora-text-classification |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # roberta-base-lora-text-classification |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7451 |
| - Accuracy: {'accuracy': 0.933} |
|
|
| ## Model description |
|
|
| More information needed |
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|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 0.001 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:-------------------:| |
| | No log | 1.0 | 250 | 0.3071 | {'accuracy': 0.919} | |
| | 0.3665 | 2.0 | 500 | 0.3954 | {'accuracy': 0.922} | |
| | 0.3665 | 3.0 | 750 | 0.3318 | {'accuracy': 0.937} | |
| | 0.1483 | 4.0 | 1000 | 0.5179 | {'accuracy': 0.942} | |
| | 0.1483 | 5.0 | 1250 | 0.5112 | {'accuracy': 0.933} | |
| | 0.0829 | 6.0 | 1500 | 0.5775 | {'accuracy': 0.936} | |
| | 0.0829 | 7.0 | 1750 | 0.6473 | {'accuracy': 0.931} | |
| | 0.019 | 8.0 | 2000 | 0.6950 | {'accuracy': 0.937} | |
| | 0.019 | 9.0 | 2250 | 0.7328 | {'accuracy': 0.931} | |
| | 0.008 | 10.0 | 2500 | 0.7451 | {'accuracy': 0.933} | |
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|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.1+cu121 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
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|
