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
- Xet hash:
- 8742b8e4b486b15cc3c25fc0e01eb4287f05a37fd33bf3770d308fe87f8071aa
- Size of remote file:
- 5.3 kB
- SHA256:
- be3d3e0f36a844ce8ff557e524264e2cca5b6fa74022b9b0369af575bbaad6f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.