Rhetoribert

🚀 sentence-transformers/static-retrieval-mrl-en-v1

这是一个基于 sentence-transformers 的模型，它在 sci_gen_colbert_triplets 数据集上对 nomic-ai/nomic-embed-text-v1.5 进行了微调。该模型能够将学术文本中的句子映射到一个 768 维的密集向量空间，映射依据是句子的修辞功能（如总结结果、表达局限性等），可用于功能文本相似度分析、局限性分析、修辞功能分类、聚类等任务。

🚀 快速开始

直接使用（Sentence Transformers）

首先安装 Sentence Transformers 库：

pip install -U sentence-transformers

然后，你可以加载此模型并进行推理：

from sentence_transformers import SentenceTransformer

# 从 🤗 Hub 下载模型
model = SentenceTransformer("KaiserML/RhetoriBERT")
# 进行推理
sentences = [
    'Surveys and interviews: Introducing excerpts from interview data',
    "Through surveys and interviews, multiliterate teachers expressed a shared belief in the importance of fostering students' ability to navigate multiple discourse communities.",
    'The authors employ a constructivist approach to learning, where students build knowledge through active engagement with multimedia texts and collaborative discussions.',
]
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)
# [3, 3]

✨ 主要特性

• 能够将学术文本中的句子映射到 768 维的密集向量空间，依据是句子的修辞功能。
• 可用于功能文本相似度分析、局限性分析、修辞功能分类、聚类等多种任务。

📦 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 🤗 Hub 下载模型
model = SentenceTransformer("KaiserML/RhetoriBERT")
# 进行推理
sentences = [
    'Surveys and interviews: Introducing excerpts from interview data',
    "Through surveys and interviews, multiliterate teachers expressed a shared belief in the importance of fostering students' ability to navigate multiple discourse communities.",
    'The authors employ a constructivist approach to learning, where students build knowledge through active engagement with multimedia texts and collaborative discussions.',
]
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)
# [3, 3]

📚 详细文档

模型详情

模型描述

模型来源

• 文档：Sentence Transformers 文档
• 仓库：GitHub 上的 Sentence Transformers
• Hugging Face：Hugging Face 上的 Sentence Transformers

完整模型架构

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

评估

指标

信息检索

• 数据集：SciGen-Eval-Set
• 使用 InformationRetrievalEvaluator 进行评估

训练详情

训练数据集

sci_gen_colbert_triplets

• 数据集：sci_gen_colbert_triplets，版本为 44071bd
• 大小：35,934 个训练样本
• 列：query、positive 和 negative
• 基于前 1000 个样本的近似统计信息： | | query | positive | negative | |------|------|------|------| | 类型 | string | string | string | | 详情 |
  • 最小：5 个词元
  • 平均：10.24 个词元
  • 最大：23 个词元
  |
  • 最小：2 个词元
  • 平均：39.86 个词元
  • 最大：80 个词元
  |
  • 最小：18 个词元
  • 平均：40.41 个词元
  • 最大：88 个词元
  |
• 样本： | query | positive | negative | |------|------|------| | Previous research: highlighting negative outcomes | Despite the widespread use of seniority-based wage systems in labor contracts, previous research has highlighted their negative outcomes, such as inefficiencies and demotivating effects on workers. | This paper, published in 1974, was among the first to establish the importance of rank-order tournaments as optimal labor contracts in microeconomics. | | Synthesising sources: contrasting evidence or ideas | Despite the observed chronic enterocolitis in Interleukin-10-deficient mice, some studies suggest that this cytokine plays a protective role in intestinal inflammation in humans (Kurimoto et al., 2001). | Chronic enterocolitis developed in Interleukin-10-deficient mice, characterized by inflammatory cell infiltration, epithelial damage, and increased production of pro-inflammatory cytokines. | | Previous research: Approaches taken | Previous research on measuring patient-relevant outcomes in osteoarthritis has primarily relied on self-reported measures, such as the Western Ontario and McMaster Universities Arthritis Index (WOMAC) (Bellamy et al., 1988). | The WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) questionnaire has been widely used in physical therapy research to assess the impact of antirheumatic drug therapy on patient-reported outcomes in individuals with hip or knee osteoarthritis. |
• 损失函数：MatryoshkaLoss，参数如下：

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        384,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

评估数据集

sci_gen_colbert_triplets

• 数据集：sci_gen_colbert_triplets，版本为 44071bd
• 大小：4,492 个评估样本
• 列：query、positive 和 negative
• 基于前 1000 个样本的近似统计信息： | | query | positive | negative | |------|------|------|------| | 类型 | string | string | string | | 详情 |
  • 最小：5 个词元
  • 平均：10.23 个词元
  • 最大：23 个词元
  |
  • 最小：18 个词元
  • 平均：39.83 个词元
  • 最大：84 个词元
  |
  • 最小：8 个词元
  • 平均：39.89 个词元
  • 最大：84 个词元
  |
• 样本： | query | positive | negative | |------|------|------| | Providing background information: reference to the purpose of the study | This study aimed to investigate the impact of socioeconomic status on child development, specifically focusing on cognitive, language, and social-emotional domains. | Children from high socioeconomic status families showed significantly higher IQ scores (M = 112.5, SD = 5.6) compared to children from low socioeconomic status families (M = 104.3, SD = 6.2) in the verbal IQ subtest. | | Providing background information: reference to the literature | According to previous studies using WinGX suite for small-molecule single-crystal crystallography, the optimization of crystal structures leads to improved accuracy in determining atomic coordinates. | This paper describes the WinGX suite, a powerful tool for small-molecule single-crystal crystallography that significantly advances the field of crystallography by streamlining data collection and analysis. | | General comments on the relevant literature | Polymer brushes have gained significant attention in the field of polymer science due to their unique properties, such as controlled thickness, high surface density, and tunable interfacial properties. | Despite previous reports suggesting that polymer brushes with short grafting densities exhibit poorer performance in terms of adhesion and stability compared to those with higher grafting densities (Liu et al., 2010), our results indicate that the opposite is true for certain types of polymer brushes. |
• 损失函数：MatryoshkaLoss，参数如下：

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        384,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

训练超参数

非默认超参数

• eval_strategy: steps
• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• learning_rate: 2e-05
• num_train_epochs: 10
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True

所有超参数

训练日志

框架版本

• Python: 3.11.11
• Sentence Transformers: 3.3.1
• Transformers: 4.47.1
• PyTorch: 2.5.1+cu121
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

📄 许可证

本项目采用 apache-2.0 许可证。

📖 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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}