Gguf Jina Reranker V1 Tiny En

🚀 jina-reranker-v1-tiny-en-GGUF

jina-reranker-v1-tiny-en-GGUF 模型专为实现极快的重排序速度而设计，同时保持了具有竞争力的性能。它基于 JinaBERT 模型，能够处理长达 8192 个标记的文本序列，在文本重排序任务中表现出色。

🚀 快速开始

使用 Jina AI 的 Reranker API

使用 Jina AI 的 Reranker API 是开始使用 jina-reranker-v1-tiny-en 的最简单方法。

curl https://api.jina.ai/v1/rerank \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
  "model": "jina-reranker-v1-tiny-en",
  "query": "Organic skincare products for sensitive skin",
  "documents": [
    "Eco-friendly kitchenware for modern homes",
    "Biodegradable cleaning supplies for eco-conscious consumers",
    "Organic cotton baby clothes for sensitive skin",
    "Natural organic skincare range for sensitive skin",
    "Tech gadgets for smart homes: 2024 edition",
    "Sustainable gardening tools and compost solutions",
    "Sensitive skin-friendly facial cleansers and toners",
    "Organic food wraps and storage solutions",
    "All-natural pet food for dogs with allergies",
    "Yoga mats made from recycled materials"
  ],
  "top_n": 3
}'

使用 sentence-transformers 库

你可以使用最新版本的 sentence-transformers>=0.27.0 库与模型进行交互。首先，通过以下命令安装该库：

pip install -U sentence-transformers

然后，使用以下代码与模型进行交互：

from sentence_transformers import CrossEncoder

# Load the model, here we use our tiny sized model
model = CrossEncoder("jinaai/jina-reranker-v1-tiny-en", trust_remote_code=True)

# Example query and documents
query = "Organic skincare products for sensitive skin"
documents = [
    "Eco-friendly kitchenware for modern homes",
    "Biodegradable cleaning supplies for eco-conscious consumers",
    "Organic cotton baby clothes for sensitive skin",
    "Natural organic skincare range for sensitive skin",
    "Tech gadgets for smart homes: 2024 edition",
    "Sustainable gardening tools and compost solutions",
    "Sensitive skin-friendly facial cleansers and toners",
    "Organic food wraps and storage solutions",
    "All-natural pet food for dogs with allergies",
    "Yoga mats made from recycled materials"
]

results = model.rank(query, documents, return_documents=True, top_k=3)

使用 transformers 库

你还可以使用 transformers 库以编程方式与模型进行交互。

!pip install transformers
from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(
    'jinaai/jina-reranker-v1-tiny-en', num_labels=1, trust_remote_code=True
)

# Example query and documents
query = "Organic skincare products for sensitive skin"
documents = [
    "Eco-friendly kitchenware for modern homes",
    "Biodegradable cleaning supplies for eco-conscious consumers",
    "Organic cotton baby clothes for sensitive skin",
    "Natural organic skincare range for sensitive skin",
    "Tech gadgets for smart homes: 2024 edition",
    "Sustainable gardening tools and compost solutions",
    "Sensitive skin-friendly facial cleansers and toners",
    "Organic food wraps and storage solutions",
    "All-natural pet food for dogs with allergies",
    "Yoga mats made from recycled materials"
]

# construct sentence pairs
sentence_pairs = [[query, doc] for doc in documents]

scores = model.compute_score(sentence_pairs)

使用 transformers.js 库

你可以使用 transformers.js 库直接在 JavaScript（浏览器、Node.js、Deno 等）中运行模型。首先，通过以下命令安装该库：

npm i @xenova/transformers

然后，使用以下代码与模型进行交互：

import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers';

const model_id = 'jinaai/jina-reranker-v1-tiny-en';
const model = await AutoModelForSequenceClassification.from_pretrained(model_id, { quantized: false });
const tokenizer = await AutoTokenizer.from_pretrained(model_id);

/**
 * Performs ranking with the CrossEncoder on the given query and documents. Returns a sorted list with the document indices and scores.
 * @param {string} query A single query
 * @param {string[]} documents A list of documents
 * @param {Object} options Options for ranking
 * @param {number} [options.top_k=undefined] Return the top-k documents. If undefined, all documents are returned.
 * @param {number} [options.return_documents=false] If true, also returns the documents. If false, only returns the indices and scores.
 */
async function rank(query, documents, {
    top_k = undefined,
    return_documents = false,
} = {}) {
    const inputs = tokenizer(
        new Array(documents.length).fill(query),
        { text_pair: documents, padding: true, truncation: true }
    )
    const { logits } = await model(inputs);
    return logits.sigmoid().tolist()
        .map(([score], i) => ({
            corpus_id: i,
            score,
            ...(return_documents ? { text: documents[i] } : {})
        })).sort((a, b) => b.score - a.score).slice(0, top_k);
}

// Example usage:
const query = "Organic skincare products for sensitive skin"
const documents = [
    "Eco-friendly kitchenware for modern homes",
    "Biodegradable cleaning supplies for eco-conscious consumers",
    "Organic cotton baby clothes for sensitive skin",
    "Natural organic skincare range for sensitive skin",
    "Tech gadgets for smart homes: 2024 edition",
    "Sustainable gardening tools and compost solutions",
    "Sensitive skin-friendly facial cleansers and toners",
    "Organic food wraps and storage solutions",
    "All-natural pet food for dogs with allergies",
    "Yoga mats made from recycled materials",
]

const results = await rank(query, documents, { return_documents: true, top_k: 3 });
console.log(results);

✨ 主要特性

• 极快的重排序速度：通过知识蒸馏技术，该模型在保持较高准确率的同时，实现了极快的推理速度。
• 长序列处理能力：基于 JinaBERT 模型，支持对称双向的 ALiBi，能够处理长达 8192 个标记的文本序列。
• 多种使用方式：支持通过 Jina AI 的 Reranker API、sentence-transformers 库、transformers 库和 transformers.js 库与模型进行交互。

📦 安装指南

安装 sentence-transformers 库

pip install -U sentence-transformers

安装 transformers 库

pip install transformers

安装 transformers.js 库

npm i @xenova/transformers

📚 详细文档

模型信息

重排序模型对比

评估结果

为确保顶级性能和搜索相关性，我们在 3 个关键基准测试中对 Jina Reranker 进行了评估。

⚠️ 重要提示

• NDCG@10 是衡量排序质量的指标，分数越高表示搜索结果越好。命中率 衡量的是相关文档出现在前 10 个搜索结果中的百分比。
• 由于其他模型不支持超过 512 个标记的长文档，因此无法获取它们在 LoCo 数据集上的结果。

更多详细信息，请参考我们的 基准测试表。

📄 许可证

本项目采用 Apache-2.0 许可证。

🔗 联系我们

加入我们的 Discord 社区，与其他社区成员交流想法。