Text Ranking
sentence-transformers
Safetensors
Transformers
new
text-classification
text-embeddings-inference
custom_code
Instructions to use Alibaba-NLP/gte-multilingual-reranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-multilingual-reranker-base with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Alibaba-NLP/gte-multilingual-reranker-base", 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) - Transformers
How to use Alibaba-NLP/gte-multilingual-reranker-base with Transformers:
# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("Alibaba-NLP/gte-multilingual-reranker-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
fine tune in sentence transformers
#7
by aari1995 - opened
Hi there, how do I fine tune it in sentence transformers, the usual cross encoder script, e.g. for sts from ST returns nan correlation
no worries it seems to be connected to sentence transformers loading in dtype float16.
aari1995 changed discussion status to closed