Sentence Similarity
Transformers
Safetensors
sentence-transformers
English
bert
feature-extraction
embeddings
retrieval
agents
memory
rag
semantic-search
Eval Results (legacy)
text-embeddings-inference
Instructions to use vrushket/agentrank-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vrushket/agentrank-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vrushket/agentrank-small") model = AutoModel.from_pretrained("vrushket/agentrank-small") - sentence-transformers
How to use vrushket/agentrank-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vrushket/agentrank-small") 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