Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use mchambrec/embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mchambrec/embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mchambrec/embedding-model", 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 mchambrec/embedding-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mchambrec/embedding-model", trust_remote_code=True) model = AutoModel.from_pretrained("mchambrec/embedding-model", trust_remote_code=True, device_map="auto") - Transformers.js
How to use mchambrec/embedding-model with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'mchambrec/embedding-model'); - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from mchambrec/embedding-model: direct link, hf CLI and curl.
- Browser
- Download file 120 Bytes
-
https://huggingface.co/mchambrec/embedding-model/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://mchambrec/embedding-model/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/mchambrec/embedding-model/resolve/main/sentence_bert_config.json
120 Bytes
| { | |
| "max_seq_length": 8192, | |
| "do_lower_case": false, | |
| "model_args": { | |
| "safe_serialization": true | |
| } | |
| } |