Sentence Similarity
sentence-transformers
Safetensors
distilbert
feature-extraction
Generated from Trainer
dataset_size:6192
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use anthonymeo/embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use anthonymeo/embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("anthonymeo/embedding") sentences = [ "how to calculate a service load", "what is the height of a lead in antenna", "types se cable", "what is the purpose of a circuit breaker" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config_sentence_transformers.json from anthonymeo/embedding: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/anthonymeo/embedding/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://anthonymeo/embedding/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/anthonymeo/embedding/resolve/main/config_sentence_transformers.json
195 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "3.0.1", | |
| "transformers": "4.39.3", | |
| "pytorch": "2.1.2" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": null | |
| } |