Text Ranking
Transformers
Safetensors
sentence-transformers
Chinese
English
minicpm
text-classification
custom_code
Instructions to use openbmb/MiniCPM-Reranker-Light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Reranker-Light with Transformers:
# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("openbmb/MiniCPM-Reranker-Light", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use openbmb/MiniCPM-Reranker-Light with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("openbmb/MiniCPM-Reranker-Light", trust_remote_code=True) 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) - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking`
#1
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Update metadata to set pipeline tag to the new
text-ranking
Changes
This is an automated pull request to update the metadata of the model card. We recently introduced the text-ranking pipeline tag for models that are used for ranking tasks, and we have a suspicion that this model is one of them.
Feel free to respond if you have questions or concerns.
- Tom Aarsen
Kaguya-19 changed pull request status to merged