Ft Ms Marco MiniLM L12 V2 Claims Reranker V2

🚀 基於 cross-encoder/ms-marco-MiniLM-L12-v2 的交叉編碼器

這是一個基於 sentence-transformers 庫，從 cross-encoder/ms-marco-MiniLM-L12-v2 微調而來的 交叉編碼器 模型。它可以計算文本對的得分，可用於文本重排序和語義搜索。

🚀 快速開始

本模型可用於計算文本對的得分，進而實現文本重排序和語義搜索。下面將詳細介紹使用方法。

✨ 主要特性

• 基於 cross-encoder/ms-marco-MiniLM-L12-v2 微調，能夠精準計算文本對得分。
• 可用於文本重排序和語義搜索任務。

📦 安裝指南

首先，你需要安裝 sentence-transformers 庫：

pip install -U sentence-transformers

💻 使用示例

基礎用法

from sentence_transformers import CrossEncoder

# 從 🤗 Hub 下載模型
model = CrossEncoder("Davidsamuel101/ft-ms-marco-MiniLM-L12-v2-claims-reranker-v2")
# 獲取文本對的得分
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,)

# 或者根據與單個文本的相似度對不同文本進行排序
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': ...}, ...]

📚 詳細文檔

模型詳情

模型描述

模型來源

• 文檔：Sentence Transformers 文檔
• 文檔：Cross Encoder 文檔
• 倉庫：GitHub 上的 Sentence Transformers
• Hugging Face：Hugging Face 上的 Cross Encoders

評估

指標

交叉編碼器重排序

• 數據集：claims-evidence-dev
• 使用 CrossEncoderRerankingEvaluator 進行評估，參數如下：

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

訓練詳情

訓練數據集

未命名數據集

• 大小：23,770 個訓練樣本
• 列：text1、text2 和 label
• 基於前 1000 個樣本的近似統計信息：

  	text1	text2	label
  類型	字符串	字符串	整數
  詳情	最小：38 個字符 平均：118.57 個字符 最大：226 個字符	最小：14 個字符 平均：144.96 個字符 最大：1176 個字符	0：~83.70% 1：~16.30%

• 樣本：

  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

• 損失函數：MultipleNegativesRankingLoss，參數如下：

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

訓練超參數

非默認超參數

• 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

所有超參數

訓練日誌

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",
}