Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
Transformers
English
bert
feature-extraction
agent-routing
conversation-matching
text-embeddings-inference
Instructions to use msugimura/gatekeeper_agent_responding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use msugimura/gatekeeper_agent_responding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("msugimura/gatekeeper_agent_responding") 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] - Transformers
How to use msugimura/gatekeeper_agent_responding with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("msugimura/gatekeeper_agent_responding") model = AutoModel.from_pretrained("msugimura/gatekeeper_agent_responding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from msugimura/gatekeeper_agent_responding: direct link, hf CLI and curl.
- Browser
- Download file 133 MB
-
https://huggingface.co/msugimura/gatekeeper_agent_responding/resolve/main/model.safetensors
- Command line
-
hf download hf://msugimura/gatekeeper_agent_responding/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/msugimura/gatekeeper_agent_responding/resolve/main/model.safetensors
133 MB
- Xet hash:
- d20912fe9f92313258fc61a7acc68e102b3c6f77d73bcd90d8f5cbc456241c57
- Size of remote file:
- 133 MB
- SHA256:
- cff7356b67d9d677be5bc80ea2f137b43f887729cdf274d3fa28a035f88ecc9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.