Sentence Similarity
sentence-transformers
PyTorch
English
bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hum-Works/lodestone-base-4096-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hum-Works/lodestone-base-4096-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hum-Works/lodestone-base-4096-v1", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from Hum-Works/lodestone-base-4096-v1: direct link, hf CLI and curl.
- Browser
- Download file 54 Bytes
-
https://huggingface.co/Hum-Works/lodestone-base-4096-v1/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://Hum-Works/lodestone-base-4096-v1/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/Hum-Works/lodestone-base-4096-v1/resolve/main/sentence_bert_config.json
54 Bytes
| { | |
| "max_seq_length": 4096, | |
| "do_lower_case": false | |
| } |