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
- Xet hash:
- 33dfee463ce6b108173fb134bc4471aace882d9005f5a6a4636b9e3961fea461
- Size of remote file:
- 438 MB
- SHA256:
- 3bc6908ecb81cfacc50868e28893a8729967ffea4a31fad9c7c2ab68c20c3936
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.