How to use vicky4s4s/embedding-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vicky4s4s/embedding-v1") model = AutoModelForSequenceClassification.from_pretrained("vicky4s4s/embedding-v1", device_map="auto")
How to use vicky4s4s/embedding-v1 with Transformers.js:
// npm i @huggingface/transformers import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers'; const tokenizer = await AutoTokenizer.from_pretrained('vicky4s4s/embedding-v1'); const model = await AutoModelForSequenceClassification.from_pretrained('vicky4s4s/embedding-v1'); const query = 'Which planet is known as the Red Planet?'; const documents = [ 'Mars, known for its reddish appearance, is often referred to as the Red Planet.', 'Venus is often called the twin of Earth because of its similar size and proximity.', ]; const inputs = tokenizer(new Array(documents.length).fill(query), { text_pair: documents, padding: true, truncation: true }); const { logits } = await model(inputs); console.log(logits.sigmoid().tolist()); // one relevance score per document
How to use vicky4s4s/embedding-v1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("vicky4s4s/embedding-v1") 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)
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