Sentence Similarity
Transformers
Safetensors
sentence-transformers
English
roberta
feature-extraction
sbert
text-embeddings-inference
Instructions to use aynaval2003/echo-sbert-domain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aynaval2003/echo-sbert-domain with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aynaval2003/echo-sbert-domain") model = AutoModel.from_pretrained("aynaval2003/echo-sbert-domain", device_map="auto") - sentence-transformers
How to use aynaval2003/echo-sbert-domain with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aynaval2003/echo-sbert-domain") 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] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from aynaval2003/echo-sbert-domain: direct link, hf CLI and curl.
- Browser
- Download file 3.56 MB
-
https://huggingface.co/aynaval2003/echo-sbert-domain/resolve/main/tokenizer.json
- Command line
-
hf download hf://aynaval2003/echo-sbert-domain/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/aynaval2003/echo-sbert-domain/resolve/main/tokenizer.json
3.56 MB
File too large to display, you can check the raw version instead.