Langcache Embed Medical V1

🚀 Redis semantic caching embedding model based on Alibaba-NLP/gte-modernbert-base

This model is a sentence-transformers model fine-tuned from Alibaba-NLP/gte-modernbert-base on the Medical dataset. It maps sentences and paragraphs to a 768-dimensional dense vector space, which can be used for semantic textual similarity for semantic caching in the medical domain.

🚀 Quick Start

First, install the Sentence Transformers library:

pip install -U sentence-transformers

Then, you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("redis/langcache-embed-medical-v1")
# Run inference
sentences = [
    'Will the value of Indian rupee increase after the ban of 500 and 1000 rupee notes?',
    'What will be the implications of banning 500 and 1000 rupees currency notes on Indian economy?',
    "Are Danish Sait's prank calls fake?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)

✨ Features

• Maps sentences & paragraphs to a 768 - dimensional dense vector space.
• Suitable for semantic textual similarity in the medical domain for semantic caching.

📦 Installation

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("redis/langcache-embed-medical-v1")
# Run inference
sentences = [
    'Will the value of Indian rupee increase after the ban of 500 and 1000 rupee notes?',
    'What will be the implications of banning 500 and 1000 rupees currency notes on Indian economy?',
    "Are Danish Sait's prank calls fake?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)

📚 Documentation

Model Details

Model Description

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Binary Classification

Training Dataset

Medical

• Dataset: Medical dataset
• Size: 2438 samples
• Columns: question_1, question_2, and label

Evaluation Dataset

Medical

• Dataset: Medical dataset
• Size: 610 samples
• Columns: question_1, question_2, and label

🔧 Technical Details

The model is fine - tuned from Alibaba-NLP/gte-modernbert-base on the Medical dataset. It uses a Sentence Transformer architecture with a specific pooling layer to map text to a 768 - dimensional vector space for semantic similarity tasks.

📄 License

No license information provided in the original document.

📚 Citation

BibTeX

Redis Langcache-embed Models

@inproceedings{langcache-embed-v1,
    title = "Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data",
    author = "Gill, Cechmanek, Hutcherson, Rajamohan, Agarwal, Gulzar, Singh, Dion",
    month = "04",
    year = "2025",
    url = "https://arxiv.org/abs/2504.02268",
}

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}