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, AutoModel tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True) model = AutoModel.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
Wrong model name used in code examples
#41
by satorean - opened
In the section "Task instruction prefixes" v1 model is used not v1.5
Same issue in the code under "The model natively supports scaling of the sequence length past 2048 tokens. To do so,"
satorean changed discussion title from Wrong model to Wrong model name used in code examples