Instructions to use SproutsAI/embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SproutsAI/embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SproutsAI/embedding-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 SproutsAI/embedding-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SproutsAI/embedding-model") model = AutoModel.from_pretrained("SproutsAI/embedding-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from SproutsAI/embedding-model: direct link, hf CLI and curl.
- Browser
- Download file 52 Bytes
-
https://huggingface.co/SproutsAI/embedding-model/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://SproutsAI/embedding-model/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/SproutsAI/embedding-model/resolve/main/sentence_bert_config.json
52 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": true | |
| } |