Instructions to use pinecone/bert-mrpc-cross-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinecone/bert-mrpc-cross-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pinecone/bert-mrpc-cross-encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pinecone/bert-mrpc-cross-encoder") model = AutoModelForSequenceClassification.from_pretrained("pinecone/bert-mrpc-cross-encoder") - Notebooks
- Google Colab
- Kaggle
| library_name: sentence-transformers | |
| pipeline_tag: text-ranking | |
| # MRPC Cross Encoder | |
| Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). |