Instructions to use IEETA/BioNExt-Tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IEETA/BioNExt-Tagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="IEETA/BioNExt-Tagger", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IEETA/BioNExt-Tagger", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig, AutoConfig | |
| from typing import List | |
| class BioNExtTaggerConfig(PretrainedConfig): | |
| model_type = "crf-tagger" | |
| def __init__( | |
| self, | |
| augmentation = "unk", | |
| context_size = 64, | |
| percentage_tags = 0.2, | |
| p_augmentation = 0.5, | |
| crf_reduction = "mean", | |
| version="0.1.2", | |
| **kwargs, | |
| ): | |
| self.version = version | |
| self.augmentation = augmentation | |
| self.context_size = context_size | |
| self.percentage_tags = percentage_tags | |
| self.p_augmentation = p_augmentation | |
| self.crf_reduction = crf_reduction | |
| super().__init__(**kwargs) | |