Sentence Similarity
Transformers
Safetensors
sentence-transformers
English
modernbert
feature-extraction
embeddings
retrieval
agents
memory
rag
semantic-search
ai-agents
llm-memory
vector-search
Eval Results (legacy)
text-embeddings-inference
Instructions to use vrushket/agentrank-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vrushket/agentrank-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vrushket/agentrank-base") model = AutoModel.from_pretrained("vrushket/agentrank-base") - sentence-transformers
How to use vrushket/agentrank-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vrushket/agentrank-base") 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
Upload AgentRank model
Browse files
README.md
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## 🤝 Community & Support
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- 🐛 **Issues**: [GitHub Issues](https://github.com/
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- 💬 **Discussions**: [HuggingFace Community](https://huggingface.co/vrushket/agentrank-base/discussions)
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- 📧 **Contact**:
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## 🤝 Community & Support
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- 🐛 **Issues**: [GitHub Issues](https://github.com/vmore2/AgentRank-base/issues)
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- 💬 **Discussions**: [HuggingFace Community](https://huggingface.co/vrushket/agentrank-base/discussions)
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- 📧 **Contact**: vrushket2604@gmail.com
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