Sentence Similarity
sentence-transformers
Safetensors
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use CAiRE/UniVaR-lambda-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CAiRE/UniVaR-lambda-5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CAiRE/UniVaR-lambda-5", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use CAiRE/UniVaR-lambda-5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("CAiRE/UniVaR-lambda-5", trust_remote_code=True) model = AutoModel.from_pretrained("CAiRE/UniVaR-lambda-5", trust_remote_code=True, device_map="auto") - Transformers.js
How to use CAiRE/UniVaR-lambda-5 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'CAiRE/UniVaR-lambda-5'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f75f5c97d71b56337b1fee7ba74c7fe674a4135bb43a4e7f0e77eb0b0361110c
- Size of remote file:
- 547 MB
- SHA256:
- 4a91a5a2c12462b0faa1eb4e8877c542c38fd17b75cf4d6c103dd92d43ed2ced
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