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
Update README.md
Browse files
README.md
CHANGED
|
@@ -145,7 +145,6 @@ A native Azerbaijani retrieval benchmark (https://github.com/LocalDoc-Azerbaijan
|
|
| 145 |
## Limitations
|
| 146 |
|
| 147 |
- Optimized for Azerbaijani text retrieval. Performance on other languages may be lower than the base multilingual-e5-small model.
|
| 148 |
-
- Requires `"query: "` and `"passage: "` prefixes for optimal performance.
|
| 149 |
- Maximum input length is 512 tokens. Longer documents should be chunked.
|
| 150 |
|
| 151 |
## Citation
|
|
|
|
| 145 |
## Limitations
|
| 146 |
|
| 147 |
- Optimized for Azerbaijani text retrieval. Performance on other languages may be lower than the base multilingual-e5-small model.
|
|
|
|
| 148 |
- Maximum input length is 512 tokens. Longer documents should be chunked.
|
| 149 |
|
| 150 |
## Citation
|