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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:4151 |
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- loss:TripletLoss |
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base_model: intfloat/multilingual-e5-large-instruct |
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widget: |
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- source_sentence: flow computer tags |
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sentences: |
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- "What is an Uncertainty Curve Point?\nAn Uncertainty Curve Point represents a\ |
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\ data point used to construct the uncertainty curve of a measurement system.\ |
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\ These curves help analyze how measurement uncertainty behaves under different\ |
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\ flow rate conditions, ensuring accuracy and reliability in uncertainty assessments.\n\ |
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\nKey Aspects of an Uncertainty Curve Point:\n- Uncertainty File ID: Links the\ |
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|
\ point to the specific uncertainty dataset, ensuring traceability.\nEquipment\ |
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\ Tag ID: Identifies the equipment associated with the uncertainty measurement,\ |
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\ crucial for system validation.\n- Uncertainty Points: Represent a list uncertainty\ |
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\ values recorded at specific conditions, forming part of the overall uncertainty\ |
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\ curve. Do not confuse this uncertainty points with the calculated uncertainty.\ |
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\ \n- Flow Rate Points: Corresponding flow rate values at which the uncertainty\ |
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\ was measured, essential for evaluating performance under varying operational\ |
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\ conditions.\nThese points are fundamental for generating uncertainty curves,\ |
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\ which are used in calibration, validation, and compliance assessments to ensure\ |
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\ measurement reliability in industrial processes.\"\n\n**IMPORTANT**: Do not\ |
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\ confuse the two types of **Points**:\n - **Uncertainty Curve Point**: Specific\ |
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\ to a measurement system uncertainty or uncertainty simulation or uncertainty\ |
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\ curve.\n - **Calibration Point**: Specific to the calibration.\n - **Uncertainty\ |
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\ values**: Do not confuse these uncertainty points with the single calculated\ |
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\ uncertainty." |
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- 'What is a flow computer? |
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A flow computer is a device used in measurement engineering. It collects analog |
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and digital data from flow meters and other sensors. |
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Key features of a flow computer: |
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- It has a unique name, firmware version, and manufacturer information. |
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- It is designed to record and process data such as temperature, pressure, and |
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fluid volume (for gases or oils).' |
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- 'What is a Measured Magnitude Value? |
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A Measured Magnitude Value represents a **DAILY** recorded physical measurement |
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of a variable within a monitored fluid. These values are essential for tracking |
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system performance, analyzing trends, and ensuring accurate monitoring of fluid |
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properties. |
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Key Aspects of a Measured Magnitude Value: |
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- Measurement Date: The timestamp indicating when the measurement was recorded. |
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- Measured Value: The daily numeric result of the recorded physical magnitude. |
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- Measurement System Association: Links the measured value to a specific measurement |
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system responsible for capturing the data. |
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- Variable Association: Identifies the specific variable (e.g., temperature, pressure, |
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flow rate) corresponding to the recorded value. |
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Measured magnitude values are crucial for real-time monitoring, historical analysis, |
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and calibration processes within measurement systems. |
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Database advices: |
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This values also are in **historics of a flow computer report**. Although, to |
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go directly instead querying the flow computer report you can do it by going to |
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the table of variables data in the database.' |
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- source_sentence: PTE CAPUAVA B |
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sentences: |
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- "What is uncertainty?\nUncertainty is a measure of confidence in the precision\ |
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|
\ and reliability of results obtained from equipment or measurement systems. It\ |
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|
\ quantifies the potential error or margin of error in measurements.\n\nTypes\ |
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\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\ |
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\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\ |
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\ such as temperature or pressure.\n - It is calculated after calibrating a\ |
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\ device or obtained from the **equipment** manufacturer's manual.\n - This\ |
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\ uncertainty serves as a starting point for further calculations related to the\ |
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|
\ equipment.\n\n2. Uncertainty of the measurement system:\n - Refers to the\ |
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\ uncertainty calculated for the overall flow measurement.\n - It depends on\ |
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|
\ the uncertainties of the individual variables (magnitudes) and represents the\ |
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|
\ combined margin of error for the entire system.\n\nKey points:\n- The uncertainties\ |
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|
\ of magnitudes (variables) are the foundation for calculating the uncertainty\ |
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|
\ of the measurement system. Think of them as the \"building blocks.\"\n- Do not\ |
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|
\ confuse the two types of uncertainty:\n - **Uncertainty of magnitudes/variables**:\ |
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|
\ Specific to individual variables (e.g., temperature, pressure).\n - **Uncertainty\ |
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\ of the measurement system**: Specific to the overall flow measurement." |
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- 'What is a Measurement Unit? |
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|
A Measurement Unit defines the standard for quantifying a physical magnitude (e.g., |
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temperature, pressure, volume). It establishes a consistent reference for interpreting |
|
|
values recorded in a measurement system. |
|
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|
|
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|
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|
Each measurement unit is associated with a specific magnitude, ensuring that values |
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|
are correctly interpreted within their context. For example: |
|
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|
|
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|
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|
- °C (Celsius) → Used for temperature |
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|
- psi (pounds per square inch) → Used for pressure |
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|
- m³ (cubic meters) → Used for volume |
|
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|
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Measurement units are essential for maintaining consistency across recorded data, |
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|
ensuring comparability, and enabling accurate calculations within measurement |
|
|
systems.' |
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- "What is a Measurement Type?\nMeasurement types define the classification of measurements\ |
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|
\ used within a system based on their purpose and regulatory requirements. These\ |
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\ types include **fiscal**, **appropriation**, **operational**, and **custody**\ |
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\ measurements. \n\n- **Fiscal measurements** are used for tax and regulatory\ |
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\ reporting, ensuring accurate financial transactions based on measured quantities.\ |
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\ \n- **Appropriation measurements** track resource allocation and ownership\ |
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\ distribution among stakeholders. \n- **Operational measurements** support real-time\ |
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\ monitoring and process optimization within industrial operations. \n- **Custody\ |
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\ measurements** are essential for legal and contractual transactions, ensuring\ |
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\ precise handover of fluids between parties. \n\nThese classifications play\ |
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\ a crucial role in compliance, financial accuracy, and operational efficiency\ |
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\ across industries such as oil and gas, water management, and energy distribution.\ |
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\ " |
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- source_sentence: PTE PARACAMBI A |
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sentences: |
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- 'What is a Meter Stream? |
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A Meter Stream represents a measurement system configured within a flow computer. |
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It serves as the interface between the physical measurement system and the computational |
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processes that record and analyze flow data. |
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Key Aspects of a Meter Stream: |
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- Status: Indicates whether the meter stream is active or inactive. |
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- Measurement System Association: Links the meter stream to a specific measurement |
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system, ensuring that the data collected corresponds to a defined physical setup. |
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- Flow Computer Association: Identifies the flow computer responsible for managing |
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and recording the measurement system''s data. |
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Why is a Meter Stream Important? |
|
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|
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.' |
|
|
- "What is uncertainty?\nUncertainty is a measure of confidence in the precision\ |
|
|
\ and reliability of results obtained from equipment or measurement systems. It\ |
|
|
\ quantifies the potential error or margin of error in measurements.\n\nTypes\ |
|
|
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\ |
|
|
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\ |
|
|
\ such as temperature or pressure.\n - It is calculated after calibrating a\ |
|
|
\ device or obtained from the **equipment** manufacturer's manual.\n - This\ |
|
|
\ uncertainty serves as a starting point for further calculations related to the\ |
|
|
\ equipment.\n\n2. Uncertainty of the measurement system:\n - Refers to the\ |
|
|
\ uncertainty calculated for the overall flow measurement.\n - It depends on\ |
|
|
\ the uncertainties of the individual variables (magnitudes) and represents the\ |
|
|
\ combined margin of error for the entire system.\n\nKey points:\n- The uncertainties\ |
|
|
\ of magnitudes (variables) are the foundation for calculating the uncertainty\ |
|
|
\ of the measurement system. Think of them as the \"building blocks.\"\n- Do not\ |
|
|
\ confuse the two types of uncertainty:\n - **Uncertainty of magnitudes/variables**:\ |
|
|
\ Specific to individual variables (e.g., temperature, pressure).\n - **Uncertainty\ |
|
|
\ of the measurement system**: Specific to the overall flow measurement." |
|
|
- 'What is an Equipment Tag? |
|
|
|
|
|
An Equipment Tag is a unique label string identifier assigned to equipment that |
|
|
is actively installed and in use within a measurement system. It differentiates |
|
|
between equipment in general (which may be in storage or inactive) and equipment |
|
|
that is currently operational in a system. |
|
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|
|
|
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|
Key Aspects of Equipment Tags: |
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|
- Equipment-Tag: A distinct label or identifier that uniquely marks the equipment |
|
|
in operation. |
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- Equipment ID: Links the tag to the corresponding equipment unit. |
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|
- Belonging Measurement System: Specifies which measurement system the tagged |
|
|
equipment is part of. |
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- Equipment Type Name: Classifies the equipment (e.g., transmitter, thermometer), |
|
|
aiding in organization and system integration. |
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|
|
|
The Equipment Tag is essential for tracking and managing operational equipment |
|
|
within a measurement system, ensuring proper identification, monitoring, and maintenance.' |
|
|
- source_sentence: PTE UTE BAIXADA FLUMINENSE A |
|
|
sentences: |
|
|
- 'What are Flow Computer Types? |
|
|
|
|
|
Flow computer types categorize different models of flow computers used in measurement |
|
|
systems, such as OMNI, KROHNE, ROC, FC302, S600, FLOWBOSS, F407, F107, and ThermoFisher. |
|
|
Each type is defined by its capabilities, functionalities, and applications, determining |
|
|
how it processes measurement data, performs calculations, and enables real-time |
|
|
monitoring. Understanding these types is essential for selecting the right equipment |
|
|
to ensure precise flow measurement, system integration, and operational efficiency.' |
|
|
- 'What is an Equipment Type? |
|
|
|
|
|
An Equipment Type defines a category of measurement or monitoring devices used |
|
|
in a system. Each type of equipment is classified based on its function, the physical |
|
|
magnitude it measures, and its associated measurement unit. |
|
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|
|
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|
|
Key Aspects of Equipment Types: |
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|
|
|
- Categorization: Equipment types include devices like transmitters, thermometers, |
|
|
and other measurement instruments. |
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|
|
- Classification: Equipment can be primary (directly involved in measurement) |
|
|
or secondary (supporting measurement processes). |
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|
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|
- Measurement Unit: Each equipment type is linked to a unit of measure (e.g., |
|
|
°C for temperature, psi for pressure). |
|
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|
|
|
- Measured Magnitude: Defines what the equipment measures (e.g., temperature, |
|
|
pressure, volume). |
|
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|
|
|
Understanding equipment types ensures correct data interpretation, proper calibration, |
|
|
and accurate measurement within a system.' |
|
|
- 'What is a measurement system? |
|
|
|
|
|
**Measurement systems** are essential components in industrial measurement and |
|
|
processing. They are identified by a unique **Tag** and are associated with a |
|
|
specific **installation** and **fluid type**. These systems utilize different |
|
|
**measurement technologies**, including **differential (DIF)** and **linear (LIN)**, |
|
|
depending on the application. Measurement systems can be classified based on their |
|
|
**application type**, such as **fiscal** or **custody transfer**. ' |
|
|
- source_sentence: PTE BRAGANÇA PAULISTA C |
|
|
sentences: |
|
|
- "What is uncertainty?\nUncertainty is a measure of confidence in the precision\ |
|
|
\ and reliability of results obtained from equipment or measurement systems. It\ |
|
|
\ quantifies the potential error or margin of error in measurements.\n\nTypes\ |
|
|
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\ |
|
|
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\ |
|
|
\ such as temperature or pressure.\n - It is calculated after calibrating a\ |
|
|
\ device or obtained from the equipment manufacturer's manual.\n - This uncertainty\ |
|
|
\ serves as a starting point for further calculations related to the equipment.\n\ |
|
|
\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated\ |
|
|
\ for the overall flow measurement.\n - It depends on the uncertainties of\ |
|
|
\ the individual variables (magnitudes) and represents the combined margin of\ |
|
|
\ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\ |
|
|
\ (variables) are the foundation for calculating the uncertainty of the measurement\ |
|
|
\ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\ |
|
|
\ types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific\ |
|
|
\ to individual variables (e.g., temperature, pressure).\n - **Uncertainty\ |
|
|
\ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\ |
|
|
\ storage for uncertainties:\nIn the database, uncertainty calculations are stored\ |
|
|
\ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores\ |
|
|
\ the uncertainty values for specific variables (e.g., temperature, pressure).\n\ |
|
|
\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values\ |
|
|
\ for the overall flow measurement system." |
|
|
- 'What is a Measurement Unit? |
|
|
|
|
|
A Measurement Unit defines the standard for quantifying a physical magnitude (e.g., |
|
|
temperature, pressure, volume). It establishes a consistent reference for interpreting |
|
|
values recorded in a measurement system. |
|
|
|
|
|
|
|
|
Each measurement unit is associated with a specific magnitude, ensuring that values |
|
|
are correctly interpreted within their context. For example: |
|
|
|
|
|
|
|
|
- °C (Celsius) → Used for temperature |
|
|
|
|
|
- psi (pounds per square inch) → Used for pressure |
|
|
|
|
|
- m³ (cubic meters) → Used for volume |
|
|
|
|
|
Measurement units are essential for maintaining consistency across recorded data, |
|
|
ensuring comparability, and enabling accurate calculations within measurement |
|
|
systems.' |
|
|
- '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.' |
|
|
datasets: |
|
|
- Lauther/d4-embeddings-TripletLoss |
|
|
pipeline_tag: sentence-similarity |
|
|
library_name: sentence-transformers |
|
|
--- |
|
|
|
|
|
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct |
|
|
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [d4-embeddings-triplet_loss](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
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|
|
|
## Model Details |
|
|
|
|
|
### Model Description |
|
|
- **Model Type:** Sentence Transformer |
|
|
- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision 274baa43b0e13e37fafa6428dbc7938e62e5c439 --> |
|
|
- **Maximum Sequence Length:** 512 tokens |
|
|
- **Output Dimensionality:** 1024 dimensions |
|
|
- **Similarity Function:** Cosine Similarity |
|
|
- **Training Dataset:** |
|
|
- [d4-embeddings-triplet_loss](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss) |
|
|
<!-- - **Language:** Unknown --> |
|
|
<!-- - **License:** Unknown --> |
|
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|
|
|
### Model Sources |
|
|
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
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|
|
|
### Full Model Architecture |
|
|
|
|
|
``` |
|
|
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() |
|
|
) |
|
|
``` |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
|
|
First install the Sentence Transformers library: |
|
|
|
|
|
```bash |
|
|
pip install -U sentence-transformers |
|
|
``` |
|
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|
|
|
Then you can load this model and run inference. |
|
|
```python |
|
|
from sentence_transformers import SentenceTransformer |
|
|
|
|
|
# Download from the 🤗 Hub |
|
|
model = SentenceTransformer("Lauther/d4-embeddings-v3.0") |
|
|
# Run inference |
|
|
sentences = [ |
|
|
'PTE BRAGANÇA PAULISTA C', |
|
|
'What is an Uncertainty Composition?\nAn 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.\n\nKey Aspects of an Uncertainty Component:\n- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.\n- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.\n- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.\nUnderstanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.', |
|
|
'What is a Measurement Unit?\nA Measurement Unit defines the standard for quantifying a physical magnitude (e.g., temperature, pressure, volume). It establishes a consistent reference for interpreting values recorded in a measurement system.\n\nEach measurement unit is associated with a specific magnitude, ensuring that values are correctly interpreted within their context. For example:\n\n- °C (Celsius) → Used for temperature\n- psi (pounds per square inch) → Used for pressure\n- m³ (cubic meters) → Used for volume\nMeasurement units are essential for maintaining consistency across recorded data, ensuring comparability, and enabling accurate calculations within measurement systems.', |
|
|
] |
|
|
embeddings = model.encode(sentences) |
|
|
print(embeddings.shape) |
|
|
# [3, 1024] |
|
|
|
|
|
# Get the similarity scores for the embeddings |
|
|
similarities = model.similarity(embeddings, embeddings) |
|
|
print(similarities.shape) |
|
|
# [3, 3] |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### Direct Usage (Transformers) |
|
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
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|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Downstream Usage (Sentence Transformers) |
|
|
|
|
|
You can finetune this model on your own dataset. |
|
|
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Out-of-Scope Use |
|
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## Bias, Risks and Limitations |
|
|
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Recommendations |
|
|
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
|
--> |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Dataset |
|
|
|
|
|
#### d4-embeddings-triplet_loss |
|
|
|
|
|
* Dataset: [d4-embeddings-triplet_loss](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss) at [4d11c52](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss/tree/4d11c524b62bf4c6107b71cebfe0e8947f6db208) |
|
|
* Size: 4,151 training samples |
|
|
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 9.03 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 213.47 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 168.24 tokens</li><li>max: 406 tokens</li></ul> | |
|
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* Samples: |
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| anchor | positive | negative | |
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|:-----------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>Orifice Diameter (mm)</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>What is an Equipment Class?<br>An Equipment Class categorizes different types of equipment based on their function or role within a measurement system. This classification helps in organizing and distinguishing equipment types for operational, maintenance, and analytical purposes.<br><br>Each Equipment Class groups related equipment under a common category. Examples include:<br><br>Primary → Main measurement device in a system.<br>Secondary → Supporting measurement device, often used for verification.<br>Tertiary → Additional measurement equipment.<br>Valves → Flow control devices used in the system.<br>By defining Equipment Classes, the system ensures proper identification, tracking, and management of measurement-related assets.</code> | |
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| <code>prueba_gonzalo</code> | <code>What is a measurement system?<br>**Measurement systems** are essential components in industrial measurement and processing. They are identified by a unique **Tag** and are associated with a specific **installation** and **fluid type**. These systems utilize different **measurement technologies**, including **differential (DIF)** and **linear (LIN)**, depending on the application. Measurement systems can be classified based on their **application type**, such as **fiscal** or **custody transfer**. </code> | <code>What is a Measured Magnitude Value?<br>A Measured Magnitude Value represents a **DAILY** recorded physical measurement of a variable within a monitored fluid. These values are essential for tracking system performance, analyzing trends, and ensuring accurate monitoring of fluid properties.<br><br>Key Aspects of a Measured Magnitude Value:<br>- Measurement Date: The timestamp indicating when the measurement was recorded.<br>- Measured Value: The daily numeric result of the recorded physical magnitude.<br>- Measurement System Association: Links the measured value to a specific measurement system responsible for capturing the data.<br>- Variable Association: Identifies the specific variable (e.g., temperature, pressure, flow rate) corresponding to the recorded value.<br>Measured magnitude values are crucial for real-time monitoring, historical analysis, and calibration processes within measurement systems.<br><br>Database advices:<br>This values also are in **historics of a flow computer report**. Although, to go directl...</code> | |
|
|
| <code>Vazao Instantanea</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>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.</code> | |
|
|
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
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|
```json |
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{ |
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"distance_metric": "TripletDistanceMetric.COSINE", |
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"triplet_margin": 0.3 |
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|
} |
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|
``` |
|
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|
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### Evaluation Dataset |
|
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|
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#### d4-embeddings-triplet_loss |
|
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|
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* Dataset: [d4-embeddings-triplet_loss](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss) at [4d11c52](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss/tree/4d11c524b62bf4c6107b71cebfe0e8947f6db208) |
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* Size: 1,038 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
|
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* Approximate statistics based on the first 1000 samples: |
|
|
| | anchor | positive | negative | |
|
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|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
|
| type | string | string | string | |
|
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| details | <ul><li>min: 3 tokens</li><li>mean: 9.02 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 209.21 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 172.36 tokens</li><li>max: 406 tokens</li></ul> | |
|
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* Samples: |
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| anchor | positive | negative | |
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|:--------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
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| <code>FQI-4301.4522B</code> | <code>What is a measurement system?<br>**Measurement systems** are essential components in industrial measurement and processing. They are identified by a unique **Tag** and are associated with a specific **installation** and **fluid type**. These systems utilize different **measurement technologies**, including **differential (DIF)** and **linear (LIN)**, depending on the application. Measurement systems can be classified based on their **application type**, such as **fiscal** or **custody transfer**. </code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the **equipment** manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...</code> | |
|
|
| <code>PTE GUARATINGUETA B</code> | <code>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.</code> | <code>What is Equipment?<br>An Equipment represents a physical device that may be used within a measurement system. Equipment can be active or inactive and is classified by type, such as transmitters, thermometers, or other measurement-related devices.<br><br>Key Aspects of Equipment:<br>- Serial Number: A unique identifier assigned to each equipment unit for tracking and reference.<br>- Current State: Indicates whether the equipment is currently in use (ACT) or inactive (INA).<br>- Associated Equipment Type: Defines the category of the equipment (e.g., transmitter, thermometer), allowing classification and management.<br>Equipment plays a critical role in measurement systems, ensuring accuracy and reliability in data collection and processing.</code> | |
|
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| <code>PTE BRAGANÇA PAULISTA B</code> | <code>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.</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the **equipment** manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...</code> | |
|
|
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"distance_metric": "TripletDistanceMetric.COSINE", |
|
|
"triplet_margin": 0.3 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
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- `eval_strategy`: steps |
|
|
- `per_device_train_batch_size`: 80 |
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|
- `per_device_eval_batch_size`: 80 |
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|
- `weight_decay`: 0.01 |
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|
- `max_grad_norm`: 0.5 |
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- `num_train_epochs`: 15 |
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|
- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `dataloader_num_workers`: 4 |
|
|
|
|
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#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
|
|
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|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
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|
- `eval_strategy`: steps |
|
|
- `prediction_loss_only`: True |
|
|
- `per_device_train_batch_size`: 80 |
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|
- `per_device_eval_batch_size`: 80 |
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|
- `per_gpu_train_batch_size`: None |
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|
- `per_gpu_eval_batch_size`: None |
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|
- `gradient_accumulation_steps`: 1 |
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|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_steps`: None |
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|
- `learning_rate`: 5e-05 |
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|
- `weight_decay`: 0.01 |
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- `adam_beta1`: 0.9 |
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|
- `adam_beta2`: 0.999 |
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|
- `adam_epsilon`: 1e-08 |
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|
- `max_grad_norm`: 0.5 |
|
|
- `num_train_epochs`: 15 |
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|
- `max_steps`: -1 |
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|
- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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|
- `log_level_replica`: warning |
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|
- `log_on_each_node`: True |
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|
- `logging_nan_inf_filter`: True |
|
|
- `save_safetensors`: True |
|
|
- `save_on_each_node`: False |
|
|
- `save_only_model`: False |
|
|
- `restore_callback_states_from_checkpoint`: False |
|
|
- `no_cuda`: False |
|
|
- `use_cpu`: False |
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|
- `use_mps_device`: False |
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|
- `seed`: 42 |
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|
- `data_seed`: None |
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|
- `jit_mode_eval`: False |
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|
- `use_ipex`: False |
|
|
- `bf16`: False |
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|
- `fp16`: True |
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- `fp16_opt_level`: O1 |
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|
- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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|
- `fp16_full_eval`: False |
|
|
- `tf32`: None |
|
|
- `local_rank`: 0 |
|
|
- `ddp_backend`: None |
|
|
- `tpu_num_cores`: None |
|
|
- `tpu_metrics_debug`: False |
|
|
- `debug`: [] |
|
|
- `dataloader_drop_last`: False |
|
|
- `dataloader_num_workers`: 4 |
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|
- `dataloader_prefetch_factor`: None |
|
|
- `past_index`: -1 |
|
|
- `disable_tqdm`: False |
|
|
- `remove_unused_columns`: True |
|
|
- `label_names`: None |
|
|
- `load_best_model_at_end`: False |
|
|
- `ignore_data_skip`: False |
|
|
- `fsdp`: [] |
|
|
- `fsdp_min_num_params`: 0 |
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `ddp_find_unused_parameters`: None |
|
|
- `ddp_bucket_cap_mb`: None |
|
|
- `ddp_broadcast_buffers`: False |
|
|
- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: False |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: None |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `include_for_metrics`: [] |
|
|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
|
|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `dispatch_batches`: None |
|
|
- `split_batches`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- 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 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### TripletLoss |
|
|
```bibtex |
|
|
@misc{hermans2017defense, |
|
|
title={In Defense of the Triplet Loss for Person Re-Identification}, |
|
|
author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
|
|
year={2017}, |
|
|
eprint={1703.07737}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CV} |
|
|
} |
|
|
``` |
|
|
|
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