Sentence Similarity
sentence-transformers
Safetensors
Bengali
electra
retriever
bangla
bengali
retrieval
bert
Instructions to use SayedShaun/bangla-retriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SayedShaun/bangla-retriever with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SayedShaun/bangla-retriever") sentences = [ "সে একজন সুখী ব্যক্তি", "সে হ্যাপি কুকুর", "সে খুব সুখী মানুষ", "আজ একটি রৌদ্রোজ্জ্বল দিন" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from SayedShaun/bangla-retriever: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/SayedShaun/bangla-retriever/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://SayedShaun/bangla-retriever/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/SayedShaun/bangla-retriever/resolve/main/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |