Instructions to use openbmb/MiniCPM-Reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="openbmb/MiniCPM-Reranker", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("openbmb/MiniCPM-Reranker", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and tags to `sentence-transformers`
#2
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Update metadata to set pipeline tag to the new
text-ranking - Update metadata to set tags to
sentence-transformers
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. I also updated added metadata to specify that this model can be loaded with the sentence-transformers library, as it should be possible to load any model compatible with transformers AutoModelForSequenceClassification.
Feel free to verify that it works with the following:
pip install sentence-transformers
from sentence_transformers import CrossEncoder
model = CrossEncoder("openbmb/MiniCPM-Reranker")
scores = model.predict([
("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
("How many people live in Berlin?", "Berlin is well known for its museums."),
])
print(scores)
Feel free to respond if you have questions or concerns.
- Tom Aarsen