Ft Ms Marco MiniLM L12 V2 Claims Reranker V2

🚀 基于 cross-encoder/ms-marco-MiniLM-L12-v2 的交叉编码器

这是一个基于 sentence-transformers 库，从 cross-encoder/ms-marco-MiniLM-L12-v2 微调而来的 交叉编码器 模型。它可以计算文本对的得分，可用于文本重排序和语义搜索。

🚀 快速开始

本模型可用于计算文本对的得分，进而实现文本重排序和语义搜索。下面将详细介绍使用方法。

✨ 主要特性

• 基于 cross-encoder/ms-marco-MiniLM-L12-v2 微调，能够精准计算文本对得分。
• 可用于文本重排序和语义搜索任务。

📦 安装指南

首先，你需要安装 sentence-transformers 库：

pip install -U sentence-transformers

💻 使用示例

基础用法

from sentence_transformers import CrossEncoder

# 从 🤗 Hub 下载模型
model = CrossEncoder("Davidsamuel101/ft-ms-marco-MiniLM-L12-v2-claims-reranker-v2")
# 获取文本对的得分
pairs = [
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.'],
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.'],
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.'],
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', "Carbon dioxide in the Earth's atmosphere is essential to life and to most of the planetary biosphere."],
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Rennie 2009: "Claim 1: Anthropogenic CO2 can\'t be changing climate, because CO2 is only a trace gas in the atmosphere and the amount produced by humans is dwarfed by the amount from volcanoes and other natural sources.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# 或者根据与单个文本的相似度对不同文本进行排序
ranks = model.rank(
    'Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.',
    [
        'At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.',
        'Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.',
        'Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.',
        "Carbon dioxide in the Earth's atmosphere is essential to life and to most of the planetary biosphere.",
        'Rennie 2009: "Claim 1: Anthropogenic CO2 can\'t be changing climate, because CO2 is only a trace gas in the atmosphere and the amount produced by humans is dwarfed by the amount from volcanoes and other natural sources.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

📚 详细文档

模型详情

模型描述

模型来源

• 文档：Sentence Transformers 文档
• 文档：Cross Encoder 文档
• 仓库：GitHub 上的 Sentence Transformers
• Hugging Face：Hugging Face 上的 Cross Encoders

评估

指标

交叉编码器重排序

• 数据集：claims-evidence-dev
• 使用 CrossEncoderRerankingEvaluator 进行评估，参数如下：

  {
      "at_k": 5,
      "always_rerank_positives": true
  }
  

训练详情

训练数据集

未命名数据集

• 大小：23,770 个训练样本
• 列：text1、text2 和 label
• 基于前 1000 个样本的近似统计信息：

  	text1	text2	label
  类型	字符串	字符串	整数
  详情	最小：38 个字符 平均：118.57 个字符 最大：226 个字符	最小：14 个字符 平均：144.96 个字符 最大：1176 个字符	0：~83.70% 1：~16.30%

• 样本：

  text1	text2	label
  Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.	At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.	1
  Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.	Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.	1
  Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.	Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.	1

• 损失函数：MultipleNegativesRankingLoss，参数如下：

  {
      "scale": 10.0,
      "num_negatives": 4,
      "activation_fn": "torch.nn.modules.activation.Sigmoid"
  }
  

训练超参数

非默认超参数

• eval_strategy：steps
• per_device_train_batch_size：16
• learning_rate：3e-06
• num_train_epochs：5
• bf16：True
• load_best_model_at_end：True

所有超参数

训练日志

框架版本

• Python：3.13.2
• Sentence Transformers：4.1.0
• Transformers：4.51.3
• PyTorch：2.7.0+cu128
• Accelerate：1.6.0
• Datasets：3.6.0
• Tokenizers：0.21.1

📄 引用

BibTeX

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",
}