Instructions to use pinecone/bert-medqp-cross-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinecone/bert-medqp-cross-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pinecone/bert-medqp-cross-encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pinecone/bert-medqp-cross-encoder") model = AutoModelForSequenceClassification.from_pretrained("pinecone/bert-medqp-cross-encoder") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("pinecone/bert-medqp-cross-encoder")
model = AutoModelForSequenceClassification.from_pretrained("pinecone/bert-medqp-cross-encoder")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Med-QP Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course.
- Downloads last month
- 5
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pinecone/bert-medqp-cross-encoder")