Instructions to use n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2") model = AutoModelForSequenceClassification.from_pretrained("n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2") - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
#2 opened about 1 year ago
by
tomaarsen
Adding `safetensors` variant of this model
#1 opened over 1 year ago
by
SFconvertbot