Measuring Embeddings V4.2

🚀 intfloat/multilingual-e5-large-instructをベースとしたSentenceTransformer

このモデルは、intfloat/multilingual-e5-large-instruct をベースに、measuring-embeddings-v4 データセットで微調整された sentence-transformers モデルです。文章や段落を1024次元の密ベクトル空間にマッピングし、意味的な文章の類似性、意味検索、言い換えマイニング、テキスト分類、クラスタリングなどに利用できます。

✨ 主な機能

• 文章や段落を1024次元の密ベクトル空間にマッピングすることができます。
• 意味的な文章の類似性、意味検索、言い換えマイニング、テキスト分類、クラスタリングなどのタスクに利用可能です。

📚 ドキュメント

モデルの詳細

モデルのソース

• ドキュメント: Sentence Transformers Documentation
• リポジトリ: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

モデルのアーキテクチャ

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()
)

💻 使用例

基本的な使用法

まず、Sentence Transformersライブラリをインストールします。

pip install -U sentence-transformers

次に、このモデルをロードして推論を実行できます。

from sentence_transformers import SentenceTransformer

# 🤗 Hubからダウンロード
model = SentenceTransformer("Lauther/measuring-embeddings-v4.2")
# 推論を実行
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]

# 埋め込みの類似度スコアを取得
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

🔧 技術詳細

学習データセット

measuring-embeddings-v4

• データセット: measuring-embeddings-v4 at 1e3ca2c
• サイズ: 3,075個の学習サンプル
• 列: sentence1、sentence2、score
• 最初の1000サンプルに基づく概算統計情報:

  	sentence1	sentence2	score
  タイプ	文字列	文字列	浮動小数点数
  詳細	最小: 3トークン 平均: 7.55トークン 最大: 17トークン	最小: 80トークン 平均: 180.22トークン 最大: 406トークン	最小: 0.07 平均: 0.21 最大: 0.95

• サンプル:

  sentence1	sentence2	score
  last calibrated span	What are historical report values? 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. Flow computer reports store two types of data values: - Hourly data values: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on an hourly basis. - Daily data values: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on a daily basis. 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? 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. - Fiscal measurements are used for tax and regulatory reporting, ensuring accurate financial transactions based on measured quantities. - Appropriation measurements track resource allocation and ownership distribution among stakeholders. - Operational measurements support real-time monitoring and process optimization within industrial operations. - Custody measurements are essential for legal and contractual transactions, ensuring precise handover of fluids between parties. 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? 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. Key Aspects of an Uncertainty Component: - Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system. - Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact. - Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation. Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.	0.1

• 損失関数: CoSENTLoss 以下のパラメータで使用:

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

評価データセット

measuring-embeddings-v4

• データセット: measuring-embeddings-v4 at 1e3ca2c
• サイズ: 659個の評価サンプル
• 列: sentence1、sentence2、score
• 最初の659サンプルに基づく概算統計情報:

  	sentence1	sentence2	score
  タイプ	文字列	文字列	浮動小数点数
  詳細	最小: 3トークン 平均: 7.63トークン 最大: 17トークン	最小: 80トークン 平均: 186.36トークン 最大: 406トークン	最小: 0.07 平均: 0.2 最大: 0.9

• サンプル:

  sentence1	sentence2	score
  measurement system details	What is an Uncertainty Composition? 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. Key Aspects of an Uncertainty Component: - Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system. - Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact. - Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation. 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? Indexes represent the recorded reports generated by flow computers, classified into two types: - Hourly reports Index: Store data for hourly events. - Daily reports Index: Strore data for daily events. 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. The index is essential for locating specific values within the report.	0.24
  static pressure	What is a Meter Stream? 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. Key Aspects of a Meter Stream: - Status: Indicates whether the meter stream is active or inactive. - Measurement System Association: Links the meter stream to a specific measurement system, ensuring that the data collected corresponds to a defined physical setup. - Flow Computer Association: Identifies the flow computer responsible for managing and recording the measurement system's data. Why is a Meter Stream Important? 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

• 損失関数: CoSENTLoss 以下のパラメータで使用:

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

学習ハイパーパラメータ

デフォルトではないハイパーパラメータ

• eval_strategy: steps
• 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

すべてのハイパーパラメータ

学習ログ

フレームワークのバージョン

• 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

📄 ライセンス

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},
}