Instructions to use Madi7a/deberta-absa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Madi7a/deberta-absa with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("microsoft/deberta-v3-large") model = PeftModel.from_pretrained(base_model, "Madi7a/deberta-absa") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Madi7a/deberta-absa: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
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https://huggingface.co/Madi7a/deberta-absa/resolve/main/README.md
- Command line
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hf download hf://Madi7a/deberta-absa/README.md
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curl -L -o README.md https://huggingface.co/Madi7a/deberta-absa/resolve/main/README.md
1.28 kB
| library_name: peft | |
| license: mit | |
| base_model: microsoft/deberta-v3-large | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - f1 | |
| model-index: | |
| - name: deberta-absa | |
| 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. --> | |
| # deberta-absa | |
| This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4935 | |
| - F1: 0.7190 | |
| ## 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: 5e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 7 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.51.1 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.0 |