Instructions to use Bharat2004/deberta-factchecker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bharat2004/deberta-factchecker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bharat2004/deberta-factchecker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Bharat2004/deberta-factchecker") model = AutoModelForSequenceClassification.from_pretrained("Bharat2004/deberta-factchecker") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: mit | |
| base_model: microsoft/deberta-v3-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: deberta-factchecker | |
| 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-factchecker | |
| This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: nan | |
| - Accuracy: 0.5 | |
| - F1 Macro: 0.3333 | |
| ## 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: 2e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 32 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 200 | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | |
| | 2.7922 | 1.0 | 593 | nan | 0.5 | 0.3333 | | |
| | 0.0 | 2.0 | 1186 | nan | 0.5 | 0.3333 | | |
| | 0.0 | 3.0 | 1779 | nan | 0.5 | 0.3333 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |