Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
feature-extraction
text-embeddings-inference
Instructions to use Eklavya73/duplicate_sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Eklavya73/duplicate_sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Eklavya73/duplicate_sbert") 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 Eklavya73/duplicate_sbert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Eklavya73/duplicate_sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from Eklavya73/duplicate_sbert: direct link, hf CLI and curl.
- Browser
- Download file 362 Bytes
-
https://huggingface.co/Eklavya73/duplicate_sbert/resolve/main/modules.json
- Command line
-
hf download hf://Eklavya73/duplicate_sbert/modules.json
-
curl -L -o modules.json https://huggingface.co/Eklavya73/duplicate_sbert/resolve/main/modules.json
362 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "0_Transformer", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |