How to use Alibaba-NLP/gte-reranker-modernbert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-reranker-modernbert-base") model = AutoModelForSequenceClassification.from_pretrained("Alibaba-NLP/gte-reranker-modernbert-base", device_map="auto")
How to use Alibaba-NLP/gte-reranker-modernbert-base with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Alibaba-NLP/gte-reranker-modernbert-base") 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)
How to use Alibaba-NLP/gte-reranker-modernbert-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-ranking', 'Alibaba-NLP/gte-reranker-modernbert-base');
RT, thanks
· Sign up or log in to comment
Create short AI videos from text or first and last frames, with synchronized dialogue, effects, and ambience.
