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:
# 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
- Xet hash:
- 9243e629200c62879f937f935136ae32da3f96a8db00607cb597a2042e0a238c
- Size of remote file:
- 266 MB
- SHA256:
- 5b9131142c313dfde0780ea9b5803fea8bd474e32a97684438cf639c8a5e7243
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