Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use RedHatAI/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 RedHatAI/nomic-embed-text-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RedHatAI/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 RedHatAI/nomic-embed-text-v1.5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RedHatAI/nomic-embed-text-v1.5", trust_remote_code=True) model = AutoModel.from_pretrained("RedHatAI/nomic-embed-text-v1.5", trust_remote_code=True) - Transformers.js
How to use RedHatAI/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', 'RedHatAI/nomic-embed-text-v1.5'); - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- 1_Pooling
- 2_Dense
- 3_Dense
- examples
- onnx
- openvino
- splits
- 2.03 kB
- 1.28 GB xet
- 1.3 GB xet
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- 547 MB xet
- 104 kB
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- 28.8 kB
- 298 MB xet
- 57 Bytes
- 90.9 MB xet
- 120 Bytes
- 695 Bytes
- 91 MB xet
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- 4.69 MB xet
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- 232 kB