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
- Xet hash:
- 2e1c898015f88bd2a0c81d2403098f9fe7930c2638c0bb9107d77e6c1299e5ba
- Size of remote file:
- 1.33 GB
- SHA256:
- e0f62f81f49e7c3d3704ea79b6c9714ce76f8eaf27c70d2b4339ded3be5aed95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.