Instructions to use ChrisUPM/BioBERT_Re_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChrisUPM/BioBERT_Re_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ChrisUPM/BioBERT_Re_trained")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ChrisUPM/BioBERT_Re_trained") model = AutoModelForSequenceClassification.from_pretrained("ChrisUPM/BioBERT_Re_trained") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ChrisUPM/BioBERT_Re_trained")
model = AutoModelForSequenceClassification.from_pretrained("ChrisUPM/BioBERT_Re_trained")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
PyTorch trained model on GAD dataset for relation classification, using BioBert weights.
- Downloads last month
- 4
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ChrisUPM/BioBERT_Re_trained")