Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
mpnet
feature-extraction
text-embeddings-inference
Instructions to use adil-ucsd/semantic_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adil-ucsd/semantic_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adil-ucsd/semantic_model") 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 adil-ucsd/semantic_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("adil-ucsd/semantic_model") model = AutoModel.from_pretrained("adil-ucsd/semantic_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from adil-ucsd/semantic_model: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/adil-ucsd/semantic_model/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://adil-ucsd/semantic_model/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/adil-ucsd/semantic_model/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 384, | |
| "do_lower_case": false | |
| } |