Measuring Embeddings V4.2

🚀 基於intfloat/multilingual-e5-large-instruct的句子轉換器模型

本模型是基於 sentence-transformers 框架，在 measuring-embeddings-v4 數據集上對 intfloat/multilingual-e5-large-instruct 模型進行微調得到的。它可以將句子和段落映射到一個 1024 維的密集向量空間，可用於語義文本相似度計算、語義搜索、釋義挖掘、文本分類、聚類等任務。

🚀 快速開始

首先，你需要安裝 sentence-transformers 庫：

pip install -U sentence-transformers

然後，你可以加載該模型並進行推理：

from sentence_transformers import SentenceTransformer

# 從 🤗 Hub 下載模型
model = SentenceTransformer("Lauther/measuring-embeddings-v4.2")
# 進行推理
sentences = [
    'uncertainty points',
    'What is a Fluid?\nA Fluid is the substance measured within a measurement system. It can be a gas or liquid, such as hydrocarbons, water, or other industrial fluids. Proper classification of fluids is essential for ensuring measurement accuracy, regulatory compliance, and operational efficiency. By identifying fluids correctly, the system applies the appropriate measurement techniques, processing methods, and reporting standards.',
    'What is a Calibration Point?\nA Calibration Point represents a specific data entry in a calibration process, comparing an expected reference value to an actual measured value. These points are fundamental in ensuring measurement accuracy and identifying deviations.\n\nKey Aspects of Calibration Points:\n- Calibration Report Association: Each calibration point belongs to a specific calibration report, linking it to a broader calibration procedure.\n- Reference Values: Theoretical or expected values used as a benchmark for measurement validation.\n- Measured Values: The actual recorded values during calibration, reflecting the instrument’s response.\n- Errors: The difference between reference and measured values, indicating possible measurement inaccuracies.\nCalibration points are essential for evaluating instrument performance, ensuring compliance with standards, and maintaining measurement reliability.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 獲取嵌入向量的相似度分數
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

✨ 主要特性

• 多用途：可用於語義文本相似度計算、語義搜索、釋義挖掘、文本分類、聚類等多種自然語言處理任務。
• 高維向量空間：能夠將句子和段落映射到 1024 維的密集向量空間，有效捕捉語義信息。
• 微調模型：基於 intfloat/multilingual-e5-large-instruct 模型在特定數據集上進行微調，更適用於特定領域的任務。

📦 安裝指南

安裝 sentence-transformers 庫：

pip install -U sentence-transformers

💻 使用示例

基礎用法

from sentence_transformers import SentenceTransformer

# 從 🤗 Hub 下載模型
model = SentenceTransformer("Lauther/measuring-embeddings-v4.2")
# 進行推理
sentences = [
    'uncertainty points',
    'What is a Fluid?\nA Fluid is the substance measured within a measurement system. It can be a gas or liquid, such as hydrocarbons, water, or other industrial fluids. Proper classification of fluids is essential for ensuring measurement accuracy, regulatory compliance, and operational efficiency. By identifying fluids correctly, the system applies the appropriate measurement techniques, processing methods, and reporting standards.',
    'What is a Calibration Point?\nA Calibration Point represents a specific data entry in a calibration process, comparing an expected reference value to an actual measured value. These points are fundamental in ensuring measurement accuracy and identifying deviations.\n\nKey Aspects of Calibration Points:\n- Calibration Report Association: Each calibration point belongs to a specific calibration report, linking it to a broader calibration procedure.\n- Reference Values: Theoretical or expected values used as a benchmark for measurement validation.\n- Measured Values: The actual recorded values during calibration, reflecting the instrument’s response.\n- Errors: The difference between reference and measured values, indicating possible measurement inaccuracies.\nCalibration points are essential for evaluating instrument performance, ensuring compliance with standards, and maintaining measurement reliability.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 獲取嵌入向量的相似度分數
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

📚 詳細文檔

模型詳情

模型描述

模型來源

• 文檔：Sentence Transformers 文檔
• 倉庫：GitHub 上的 Sentence Transformers
• Hugging Face：Hugging Face 上的 Sentence Transformers

完整模型架構

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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()
)

訓練詳情

訓練數據集

measuring-embeddings-v4

• 數據集：measuring-embeddings-v4，版本為 1e3ca2c
• 大小：3075 個訓練樣本
• 列：sentence1、sentence2 和 score
• 基於前 1000 個樣本的近似統計信息： | | sentence1 | sentence2 | score | | ---- | ---- | ---- | ---- | | 類型 | 字符串 | 字符串 | 浮點數 | | 詳情 |
  • 最小：3 個標記
  • 平均：7.55 個標記
  • 最大：17 個標記
  |
  • 最小：80 個標記
  • 平均：180.22 個標記
  • 最大：406 個標記
  |
  • 最小：0.07
  • 平均：0.21
  • 最大：0.95
  |
• 樣本： | sentence1 | sentence2 | score | | ---- | ---- | ---- | | last calibrated span | What are historical report values?<br>These represent the recorded data points within flow computer reports. Unlike the report index, which serves as a reference to locate reports, these values contain the actual measurements and calculated data stored in the historical records.<br><br>Flow computer reports store two types of data values:<br><br>- **Hourly data values**: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on an hourly basis.<br>- **Daily data values**: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on a daily basis.<br>Each value is directly linked to its respective report index, ensuring traceability to the original flow computer record. These values maintain their raw integrity, providing a reliable source for analysis and validation. | 0.1 | | flow computer configuration | What is a Measurement Type?<br>Measurement types define the classification of measurements used within a system based on their purpose and regulatory requirements. These types include **fiscal**, **appropriation**, **operational**, and **custody** measurements. <br><br>- **Fiscal measurements** are used for tax and regulatory reporting, ensuring accurate financial transactions based on measured quantities. <br>- **Appropriation measurements** track resource allocation and ownership distribution among stakeholders. <br>- **Operational measurements** support real-time monitoring and process optimization within industrial operations. <br>- **Custody measurements** are essential for legal and contractual transactions, ensuring precise handover of fluids between parties. <br><br>These classifications play a crucial role in compliance, financial accuracy, and operational efficiency across industries such as oil and gas, water management, and energy distribution. | 0.1 | | uncertainty certificate number | What is an Uncertainty Composition?<br>An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty.<br><br>Key Aspects of an Uncertainty Component:<br>- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.<br>- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.<br>- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.<br>Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision. | 0.1 |
• 損失函數：CoSENTLoss，參數如下：

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

評估數據集

measuring-embeddings-v4

• 數據集：measuring-embeddings-v4，版本為 1e3ca2c
• 大小：659 個評估樣本
• 列：sentence1、sentence2 和 score
• 基於前 659 個樣本的近似統計信息： | | sentence1 | sentence2 | score | | ---- | ---- | ---- | ---- | | 類型 | 字符串 | 字符串 | 浮點數 | | 詳情 |
  • 最小：3 個標記
  • 平均：7.63 個標記
  • 最大：17 個標記
  |
  • 最小：80 個標記
  • 平均：186.36 個標記
  • 最大：406 個標記
  |
  • 最小：0.07
  • 平均：0.2
  • 最大：0.9
  |
• 樣本： | sentence1 | sentence2 | score | | ---- | ---- | ---- | | measurement system details | What is an Uncertainty Composition?<br>An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty.<br><br>Key Aspects of an Uncertainty Component:<br>- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.<br>- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.<br>- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.<br>Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision. | 0.15 | | measurement system tag EMED-3102-02-010 | What is a report index or historic index?<br>Indexes represent the recorded reports generated by flow computers, classified into two types: <br>- **Hourly reports Index**: Store data for hourly events.<br>- **Daily reports Index**: Strore data for daily events.<br><br>These reports, also referred to as historical data or flow computer historical records, contain raw, first-hand measurements directly collected from the flow computer. The data has not been processed or used in any calculations, preserving its original state for analysis or validation.<br><br>The index is essential for locating specific values within the report. | 0.24 | | static pressure | What is a Meter Stream?<br>A Meter Stream represents a measurement system configured within a flow computer. It serves as the interface between the physical measurement system and the computational processes that record and analyze flow data.<br><br>Key Aspects of a Meter Stream:<br>- Status: Indicates whether the meter stream is active or inactive.<br>- Measurement System Association: Links the meter stream to a specific measurement system, ensuring that the data collected corresponds to a defined physical setup.<br>- Flow Computer Association: Identifies the flow computer responsible for managing and recording the measurement system's data.<br>Why is a Meter Stream Important?<br>A **meter stream** is a critical component in flow measurement, as it ensures that the measurement system is correctly integrated into the flow computer for accurate monitoring and reporting. Since each flow computer can handle multiple meter streams, proper configuration is essential for maintaining data integrity and traceability. | 0.1 |
• 損失函數：CoSENTLoss，參數如下：

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

訓練超參數

非默認超參數

• eval_strategy：按步數評估
• per_device_train_batch_size：4
• per_device_eval_batch_size：4
• gradient_accumulation_steps：4
• learning_rate：2e-05
• num_train_epochs：10
• warmup_ratio：0.1

所有超參數

訓練日誌

框架版本

• Python: 3.11.0
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.6.0+cu124
• Accelerate: 1.4.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

📄 許可證

文檔中未提及相關信息。

📖 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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}