Instructions to use SaiPavanKumarMeruga/roberta-base-lora-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SaiPavanKumarMeruga/roberta-base-lora-text-classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("roberta-base") model = PeftModel.from_pretrained(base_model, "SaiPavanKumarMeruga/roberta-base-lora-text-classification") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SaiPavanKumarMeruga/roberta-base-lora-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.74 kB
-
https://huggingface.co/SaiPavanKumarMeruga/roberta-base-lora-text-classification/resolve/main/README.md
- Command line
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hf download hf://SaiPavanKumarMeruga/roberta-base-lora-text-classification/README.md
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curl -L -o README.md https://huggingface.co/SaiPavanKumarMeruga/roberta-base-lora-text-classification/resolve/main/README.md
1.74 kB
| base_model: roberta-base | |
| library_name: peft | |
| license: mit | |
| metrics: | |
| - accuracy | |
| tags: | |
| - generated_from_trainer | |
| 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 the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.8264 | |
| - Accuracy: {'accuracy': 0.862} | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| 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: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------------------:| | |
| | No log | 1.0 | 250 | 0.4008 | {'accuracy': 0.82} | | |
| | 0.6294 | 2.0 | 500 | 0.6326 | {'accuracy': 0.84} | | |
| | 0.6294 | 3.0 | 750 | 0.6141 | {'accuracy': 0.87} | | |
| | 0.3802 | 4.0 | 1000 | 1.2677 | {'accuracy': 0.82} | | |
| | 0.3802 | 5.0 | 1250 | 0.8264 | {'accuracy': 0.862} | | |
| ### Framework versions | |
| - PEFT 0.12.0 | |
| - Transformers 4.42.4 | |
| - Pytorch 2.3.1 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 |