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
1
#15 opened 10 months ago
by
rasyosef
Article to deepen my understanding and reference it in my work
2
#14 opened 11 months ago
by
AmiMba
Add exported onnx model 'model_O3.onnx'
#13 opened about 1 year ago
by
tomaarsen
Issue of NaN values when running predict on CPU
👍 3
3
#6 opened about 1 year ago
by
himj98
Reranker Model Performance Optimization
#5 opened over 1 year ago
by
kazmi09
Request: DOI
#3 opened almost 2 years ago
by
AIBabystep