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-80 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CAiRE/UniVaR-lambda-80 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CAiRE/UniVaR-lambda-80", 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-80 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("CAiRE/UniVaR-lambda-80", trust_remote_code=True) model = AutoModel.from_pretrained("CAiRE/UniVaR-lambda-80", trust_remote_code=True) - Transformers.js
How to use CAiRE/UniVaR-lambda-80 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'CAiRE/UniVaR-lambda-80'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3049ad98ef96c2267b0aefd0c0b128ba7be3d5b4ce63a18485f845db1e428667
- Size of remote file:
- 547 MB
- SHA256:
- f0612802f780ec4f34c93242e2a959e13f78fd4923828f8be469cb4755ac7855
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