Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use embedingHF/Sentence_Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use embedingHF/Sentence_Transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embedingHF/Sentence_Transformer") 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 embedingHF/Sentence_Transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("embedingHF/Sentence_Transformer") model = AutoModel.from_pretrained("embedingHF/Sentence_Transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from embedingHF/Sentence_Transformer: direct link, hf CLI and curl.
- Browser
- Download file 321 Bytes
-
https://huggingface.co/embedingHF/Sentence_Transformer/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://embedingHF/Sentence_Transformer/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/embedingHF/Sentence_Transformer/resolve/main/1_Pooling/config.json
321 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |