Text Ranking
sentence-transformers
PyTorch
JAX
ONNX
Safetensors
OpenVINO
Transformers
English
bert
text-classification
text-embeddings-inference
Instructions to use cross-encoder/ms-marco-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/ms-marco-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Transformers
How to use cross-encoder/ms-marco-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/ms-marco-MiniLM-L6-v2") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/ms-marco-MiniLM-L6-v2") - Notebooks
- Google Colab
- Kaggle
MS Marco Dev number of queries and documents
#15
by rasyosef - opened
When this crossencoder was evaluated on MS Marco Dev, how many queries and documents were used?
Do all the documents (positive and negative) from the train, validation, and test set of the msmarco dataset need to be included?
https://huggingface.co/datasets/microsoft/ms_marco
how to use this