Langcache Embed Medical V1

🚀 基于Alibaba-NLP/gte-modernbert-base的Redis语义缓存嵌入模型

本模型是基于 sentence-transformers 框架，从 Alibaba-NLP/gte-modernbert-base 微调而来，在 Medical 数据集上进行训练。它能将句子和段落映射到768维的密集向量空间，可用于医疗领域语义缓存的语义文本相似度任务。

🚀 快速开始

安装依赖

首先，你需要安装 sentence-transformers 库：

pip install -U sentence-transformers

加载模型并推理

安装完成后，你可以加载本模型并进行推理：

from sentence_transformers import SentenceTransformer

# 从Hugging Face Hub下载模型
model = SentenceTransformer("redis/langcache-embed-medical-v1")
# 进行推理
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]

# 获取嵌入向量的相似度分数
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)

✨ 主要特性

• 高精度：在医疗领域的二分类任务中，余弦准确率达到0.92，余弦F1值达到0.93，表现出色。
• 长序列处理能力：最大序列长度可达8192个标记，能处理较长的句子和段落。
• 语义缓存适用：可将句子和段落映射到768维的密集向量空间，适用于医疗领域的语义缓存任务。

📦 安装指南

安装 sentence-transformers 库：

pip install -U sentence-transformers

💻 使用示例

基础用法

from sentence_transformers import SentenceTransformer

# 从Hugging Face Hub下载模型
model = SentenceTransformer("redis/langcache-embed-medical-v1")
# 进行推理
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]

# 获取嵌入向量的相似度分数
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)

📚 详细文档

模型详情

模型描述

模型来源

• 文档：Sentence Transformers Documentation
• 仓库：Sentence Transformers on GitHub
• Hugging Face：Sentence Transformers on Hugging Face

完整模型架构

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})
)

二分类指标

训练数据集

Medical

• 数据集：Medical dataset
• 大小：2438个样本
• 列：question_1，question_2 和 label

评估数据集

Medical

• 数据集：Medical dataset
• 大小：610个样本
• 列：question_1，question_2 和 label

📄 许可证

文档中未提及相关许可证信息。

📚 引用

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",
}