Fine Tuned Embedding Model

🚀 基于sentence-transformers/all-MiniLM-L6-v2的句子转换器模型

本项目是一个基于 sentence-transformers 框架，从 sentence-transformers/all-MiniLM-L6-v2 微调而来的模型。它能将句子和段落映射到384维的密集向量空间，可用于语义文本相似度计算、语义搜索、释义挖掘、文本分类、聚类等任务。

🚀 快速开始

本模型是基于 sentence-transformers 框架微调的，以下是使用该模型的具体步骤：

安装Sentence Transformers库

pip install -U sentence-transformers

加载模型并进行推理

from sentence_transformers import SentenceTransformer

# 从🤗 Hub下载模型
model = SentenceTransformer("sentence_transformers_model_id")
# 进行推理
sentences = [
    'What does this text say about data privacy?',
    'information during GAI training and maintenance. \nHuman-AI Configuration; Obscene, \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration; \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or levels of harmful bias, intellectual property infringement, \ndata privacy violations, obscenity, extremism, violence, or CBRN information in \nsystem training data. \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety features of fine-tuned models when the negative risk exceeds \norganizational risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review GAI system outputs for validity and safety: Review generated code to \nassess risks that may arise from unreliable downstream decision-making. \nValue Chain and Component \nIntegration; Dangerous, Violent, or \nHateful Content',
    'Scheurer, J. et al. (2023) Technical report: Large language models can strategically deceive their users \nwhen put under pressure. arXiv. https://arxiv.org/abs/2311.07590 \nShelby, R. et al. (2023) Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm \nReduction. arXiv. https://arxiv.org/pdf/2210.05791 \nShevlane, T. et al. (2023) Model evaluation for extreme risks. arXiv. https://arxiv.org/pdf/2305.15324 \nShumailov, I. et al. (2023) The curse of recursion: training on generated data makes models forget. arXiv. \nhttps://arxiv.org/pdf/2305.17493v2 \nSmith, A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language \nModels. PLOS Digital Health. \nhttps://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388 \nSoice, E. et al. (2023) Can large language models democratize access to dual-use biotechnology? arXiv. \nhttps://arxiv.org/abs/2306.03809',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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

• 高维向量映射：能够将句子和段落映射到384维的密集向量空间。
• 多任务支持：可用于语义文本相似度计算、语义搜索、释义挖掘、文本分类、聚类等多种自然语言处理任务。

📦 安装指南

要使用本模型，需要安装 sentence-transformers 库，可使用以下命令进行安装：

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("sentence_transformers_model_id")
# 进行推理
sentences = [
    'What does this text say about data privacy?',
    'information during GAI training and maintenance. \nHuman-AI Configuration; Obscene, \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration; \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or levels of harmful bias, intellectual property infringement, \ndata privacy violations, obscenity, extremism, violence, or CBRN information in \nsystem training data. \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety features of fine-tuned models when the negative risk exceeds \norganizational risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review GAI system outputs for validity and safety: Review generated code to \nassess risks that may arise from unreliable downstream decision-making. \nValue Chain and Component \nIntegration; Dangerous, Violent, or \nHateful Content',
    'Scheurer, J. et al. (2023) Technical report: Large language models can strategically deceive their users \nwhen put under pressure. arXiv. https://arxiv.org/abs/2311.07590 \nShelby, R. et al. (2023) Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm \nReduction. arXiv. https://arxiv.org/pdf/2210.05791 \nShevlane, T. et al. (2023) Model evaluation for extreme risks. arXiv. https://arxiv.org/pdf/2305.15324 \nShumailov, I. et al. (2023) The curse of recursion: training on generated data makes models forget. arXiv. \nhttps://arxiv.org/pdf/2305.17493v2 \nSmith, A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language \nModels. PLOS Digital Health. \nhttps://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388 \nSoice, E. et al. (2023) Can large language models democratize access to dual-use biotechnology? arXiv. \nhttps://arxiv.org/abs/2306.03809',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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

模型资源

• 文档：Sentence Transformers文档
• 代码仓库：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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (2): Normalize()
)

训练详情

训练数据集

未命名数据集

• 数据集大小：555个训练样本
• 列信息：<code>sentence_0</code> 和 <code>sentence_1</code>
• 近似统计信息（基于前555个样本）：

  	sentence_0	sentence_1
  类型	字符串	字符串
  详情	最小：10个词元 平均：11.2个词元 最大：12个词元	最小：156个词元 平均：199.37个词元 最大：256个词元

• 样本示例：

  sentence_0	sentence_1
  What does this text say about trustworthiness?	other systems. Information Integrity; Value Chain and Component Integration MP-2.2-002 Observe and analyze how the GAI system interacts with external networks, and identify any potential for negative externalities, particularly where content provenance might be compromised. Information Integrity AI Actor Tasks: End Users MAP 2.3: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct validation Action ID Suggested Action GAI Risks MP-2.3-001 Assess the accuracy, quality, reliability, and authenticity of GAI output by comparing it to a set of known ground truth data and by using a variety of evaluation methods (e.g., human oversight and automated evaluation, proven cryptographic techniques, review of content inputs). Information Integrity 25
  What does this text say about unclassified?	training and TEVV data; Filtering of hate speech or content in GAI system training data; Prevalence of GAI-generated data in GAI system training data. Harmful Bias and Homogenization 15 Winogender Schemas is a sample set of paired sentences which differ only by gender of the pronouns used, which can be used to evaluate gender bias in natural language processing coreference resolution systems. 37 MS-2.11-005 Assess the proportion of synthetic to non-synthetic training data and verify training data is not overly homogenous or GAI-produced to mitigate concerns of model collapse. Harmful Bias and Homogenization AI Actor Tasks: AI Deployment, AI Impact Assessment, Affected Individuals and Communities, Domain Experts, End-Users, Operation and Monitoring, TEVV MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities – as identified in the MAP function – are assessed and documented. Action ID Suggested Action GAI Risks
  What does this text say about unclassified?	Padmakumar, V. et al. (2024) Does writing with language models reduce content diversity? ICLR. https://arxiv.org/pdf/2309.05196 Park, P. et. al. (2024) AI deception: A survey of examples, risks, and potential solutions. Patterns, 5(5). arXiv. https://arxiv.org/pdf/2308.14752 Partnership on AI (2023) Building a Glossary for Synthetic Media Transparency Methods, Part 1: Indirect Disclosure. https://partnershiponai.org/glossary-for-synthetic-media-transparency-methods-part-1- indirect-disclosure/ Qu, Y. et al. (2023) Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text- To-Image Models. arXiv. https://arxiv.org/pdf/2305.13873 Rafat, K. et al. (2023) Mitigating carbon footprint for knowledge distillation based deep learning model compression. PLOS One. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285668 Said, I. et al. (2022) Nonconsensual Distribution of Intimate Images: Exploring the Role of Legal Attitudes

训练损失函数

使用 MultipleNegativesRankingLoss 损失函数，参数如下：

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

训练超参数

非默认超参数

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• multi_dataset_batch_sampler: round_robin

所有超参数

框架版本

• Python：3.11.5
• Sentence Transformers：3.1.1
• Transformers：4.44.2
• PyTorch：2.4.1+cpu
• Accelerate：0.34.2
• Datasets：3.0.0
• Tokenizers：0.19.1

🔧 技术细节

本模型基于 sentence-transformers 框架，从 sentence-transformers/all-MiniLM-L6-v2 微调而来。模型结构包含 Transformer 层、Pooling 层和 Normalize 层，具体如下：

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (2): Normalize()
)

在训练过程中，使用了 MultipleNegativesRankingLoss 损失函数，并设置了相应的超参数，以优化模型性能。

📄 许可证

文档中未提及相关许可证信息。

📖 引用

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",
}

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}
}