Feature Extraction
sentence-transformers
Safetensors
English
bert
sentence-similarity
agents
rag
embeddings
llmops
text-embeddings-inference
Instructions to use hharsha/agentic-systems-minilm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hharsha/agentic-systems-minilm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hharsha/agentic-systems-minilm") sentences = [ "hybrid search and reranking for RAG", "RetrievalLab shows advanced RAG with hybrid search and cross-encoder reranking.", "A cooking recipe for pasta carbonara.", "AgentFleet runs multi-agent task DAGs with cost governance." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from hharsha/agentic-systems-minilm: direct link, hf CLI and curl.
- Browser
- Download file 241 Bytes
-
https://huggingface.co/hharsha/agentic-systems-minilm/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://hharsha/agentic-systems-minilm/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/hharsha/agentic-systems-minilm/resolve/main/sentence_bert_config.json
241 Bytes
| { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings" | |
| } |