Model Details
Full Model IDmichiyasunaga/BioLinkBERT-base
Pipeline / Tasktext-classification
Librarytransformers
Downloads (all-time)18.1K
Likes45
Last Modified3/31/2022
Author / Orgmichiyasunaga
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("text-classification", model="michiyasunaga/BioLinkBERT-base")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchbertfeature-extractionexbertlinkbertbiolinkbertfill-maskquestion-answeringtext-classificationtoken-classificationendataset:pubmedarxiv:2203.15827license:apache-2.0text-embeddings-inferenceendpoints_compatibledeploy:azureregion:us
More Text Classification Models
See all →🚀 Use This Model
Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🏷️ Task: Text Classification
This model is designed for the Text Classification task. Explore more models for this use case.
All Text Classification Models →📊 Popularity
⬇ Downloads18.1K
❤️ Community Likes45
🛠️ Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.