Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use nomic-ai/nomic-embed-text-v1.5 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-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", 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 nomic-ai/nomic-embed-text-v1.5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True) - Transformers.js
How to use nomic-ai/nomic-embed-text-v1.5 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-v1.5'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27c59e58bf3fc829d27e717a9214fe79279f6d61c423a745da6ad9ee88d24828
- Size of remote file:
- 111 MB
- SHA256:
- 3dbab6709d866709a8f9df237fe9a008bd9ccfc30e32e4b8f7f5e085cdb68bf1
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