Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dataset_size:100K<n<1M
loss:SoftmaxLoss
text-embeddings-inference
Instructions to use emonnsl/embed_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use emonnsl/embed_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("emonnsl/embed_model") sentences = [ "সব কথার মিল আছে।", "অন্য সবার মতো একই কাজ করেছেন।", "কাজের জন্য কোনও টাকা বরাদ্দ নেই।", "তার মাসিক আয় কমে গেছে।" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |