Sentence Similarity
sentence-transformers
PyTorch
ONNX
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use nomic-ai/nomic-embed-text-v1-unsupervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nomic-ai/nomic-embed-text-v1-unsupervised with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-text-v1-unsupervised", 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 nomic-ai/nomic-embed-text-v1-unsupervised with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-text-v1-unsupervised", trust_remote_code=True) model = AutoModel.from_pretrained("nomic-ai/nomic-embed-text-v1-unsupervised", trust_remote_code=True, device_map="auto") - Transformers.js
How to use nomic-ai/nomic-embed-text-v1-unsupervised with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'nomic-ai/nomic-embed-text-v1-unsupervised'); - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nomic-ai/nomic-embed-text-v1-unsupervised: direct link, hf CLI and curl.
- Browser
- Download file 547 MB
-
https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nomic-ai/nomic-embed-text-v1-unsupervised/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised/resolve/main/pytorch_model.bin
547 MB
- Xet hash:
- 0beb00c66c12cb73e9db1fb60390df6c3cb699233009b30428f762bc1e6ed917
- Size of remote file:
- 547 MB
- SHA256:
- 82f641cef285935a66d0c2b22cb373b8f3e7004fbe6bc9d9d41303239f6f7807
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