Sentence Similarity
sentence-transformers
Safetensors
bidirectional_pplx_qwen3
feature-extraction
retrieval
cross-lingual
agriculture
india
kisan-call-centre
custom_code
text-embeddings-inference
Instructions to use judicialmind/KCC-Embed-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use judicialmind/KCC-Embed-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("judicialmind/KCC-Embed-0.6B", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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This model is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 licence (CC BY-NC-SA 4.0): research, academic, personal and other non-commercial use is permitted with attribution; adaptations must be shared under the same licence; commercial use is not permitted. The original public source data remains under the Government Open Data License - India (GODL-India). Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.
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