Sentence Similarity
sentence-transformers
Safetensors
Azerbaijani
bert
feature-extraction
retrieval
azerbaijani
embedding
Eval Results (legacy)
text-embeddings-inference
Instructions to use LocalDoc/LocRet-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LocalDoc/LocRet-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LocalDoc/LocRet-small") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from LocalDoc/LocRet-small: direct link, hf CLI and curl.
- Browser
- Download file 348 Bytes
-
https://huggingface.co/LocalDoc/LocRet-small/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://LocalDoc/LocRet-small/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/LocalDoc/LocRet-small/resolve/main/tokenizer_config.json
348 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": true, | |
| "mask_token": "<mask>", | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<unk>" | |
| } | |