|
--- |
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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:3075 |
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- loss:CoSENTLoss |
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base_model: intfloat/multilingual-e5-large-instruct |
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widget: |
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- source_sentence: last calibrated span |
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sentences: |
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- 'What is a Calibration Point? |
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|
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A Calibration Point represents a specific data entry in a calibration process, |
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comparing an expected reference value to an actual measured value. These points |
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are fundamental in ensuring measurement accuracy and identifying deviations. |
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|
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Key Aspects of Calibration Points: |
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|
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- Calibration Report Association: Each calibration point belongs to a specific |
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calibration report, linking it to a broader calibration procedure. |
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|
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- Reference Values: Theoretical or expected values used as a benchmark for measurement |
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validation. |
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|
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- Measured Values: The actual recorded values during calibration, reflecting the |
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instrument’s response. |
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|
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- Errors: The difference between reference and measured values, indicating possible |
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measurement inaccuracies. |
|
|
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Calibration points are essential for evaluating instrument performance, ensuring |
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compliance with standards, and maintaining measurement reliability.' |
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- 'What is Equipment? |
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An Equipment represents a physical device that may be used within a measurement |
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system. Equipment can be active or inactive and is classified by type, such as |
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transmitters, thermometers, or other measurement-related devices. |
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Key Aspects of Equipment: |
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|
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- Serial Number: A unique identifier assigned to each equipment unit for tracking |
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and reference. |
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- Current State: Indicates whether the equipment is currently in use (ACT) or |
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inactive (INA). |
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|
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- Associated Equipment Type: Defines the category of the equipment (e.g., transmitter, |
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thermometer), allowing classification and management. |
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|
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Equipment plays a critical role in measurement systems, ensuring accuracy and |
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reliability in data collection and processing.' |
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- 'What is an Equipment Tag? |
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An Equipment Tag is a unique identifier assigned to equipment that is actively |
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installed and in use within a measurement system. It differentiates between equipment |
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in general (which may be in storage or inactive) and equipment that is currently |
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operational in a system. |
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Key Aspects of Equipment Tags: |
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|
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- Equipment-Tag: A distinct label or identifier that uniquely marks the equipment |
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in operation. |
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- Equipment ID: Links the tag to the corresponding equipment unit. |
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|
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- Belonging Measurement System: Specifies which measurement system the tagged |
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equipment is part of. |
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|
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- Equipment Type Name: Classifies the equipment (e.g., transmitter, thermometer), |
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aiding in organization and system integration. |
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The Equipment Tag is essential for tracking and managing operational equipment |
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within a measurement system, ensuring proper identification, monitoring, and maintenance.' |
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- source_sentence: transmitter calibration record |
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sentences: |
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- 'What are historical report values? |
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|
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These represent the recorded data points within flow computer reports. Unlike |
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the report index, which serves as a reference to locate reports, these values |
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contain the actual measurements and calculated data stored in the historical records. |
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Flow computer reports store two types of data values: |
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- **Hourly data values**: Contain measured or calculated values (e.g., operational |
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minutes, alarms set, etc.) recorded on an hourly basis. |
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|
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- **Daily data values**: Contain measured or calculated values (e.g., operational |
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minutes, alarms set, etc.) recorded on a daily basis. |
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Each value is directly linked to its respective report index, ensuring traceability |
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to the original flow computer record. These values maintain their raw integrity, |
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providing a reliable source for analysis and validation.' |
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- 'What is a Flow Computer Firmware? |
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A flow computer firmware is a software component that defines the functionality |
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and behavior of a flow computer. |
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🔹 Key Characteristics: |
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Each firmware version (e.g., F407, FB107, EMED-010) is linked to a specific flow |
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computer model. |
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Firmware versions can have a status indicating whether they are active or inactive. |
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They determine how the flow computer processes measurements, calculations, and |
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system operations. |
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📌 Database Tip: When querying firmware information, ensure the firmware version |
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is matched with the correct flow computer type for accurate results.' |
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- 'What is an Uncertainty Curve Point? |
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An Uncertainty Curve Point represents a data point used to construct the uncertainty |
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curve of a measurement system. These curves help analyze how measurement uncertainty |
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behaves under different flow rate conditions, ensuring accuracy and reliability |
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in uncertainty assessments. |
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Key Aspects of an Uncertainty Curve Point: |
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- Uncertainty File ID: Links the point to the specific uncertainty dataset, ensuring |
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traceability. |
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|
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Equipment Tag ID: Identifies the equipment associated with the uncertainty measurement, |
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crucial for system validation. |
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|
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- Uncertainty Points: Represent uncertainty values recorded at specific conditions, |
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forming part of the overall uncertainty curve. |
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|
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- Flow Rate Points: Corresponding flow rate values at which the uncertainty was |
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measured, essential for evaluating performance under varying operational conditions. |
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|
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These points are fundamental for generating uncertainty curves, which are used |
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in calibration, validation, and compliance assessments to ensure measurement reliability |
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in industrial processes.' |
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- source_sentence: measurement systems |
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sentences: |
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- 'What is a Calibration Record? |
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|
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A Calibration Record documents the calibration process of a specific equipment |
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tag, ensuring that its measurements remain accurate and reliable. Calibration |
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is a critical process in maintaining measurement precision and compliance with |
|
standards. |
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Key Aspects of a Calibration Record: |
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|
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- Calibration Date: The exact date when the calibration was performed, crucial |
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for tracking maintenance schedules. |
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|
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- Certification Number: A unique identifier for the calibration certificate, providing |
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traceability and verification of compliance. |
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|
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- Range Values: The minimum and maximum measurement values covered during the |
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calibration process. |
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- Calibration Status: Indicates whether the calibration was approved or saved |
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for further review. |
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- Associated Units: Specifies the measurement units used in calibration (e.g., |
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°C, psi). |
|
|
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- Associated Equipment Tag ID: Links the calibration record to a specific equipment |
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tag, ensuring traceability of measurement instruments. |
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|
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Calibration records play a fundamental role in quality assurance, helping maintain |
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measurement integrity and regulatory compliance.' |
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- 'What is a flow computer? |
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|
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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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|
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- It has a unique name, firmware version, and manufacturer information. |
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|
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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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|
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A Measured Magnitude Value represents a recorded physical measurement of a variable |
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within a monitored fluid. These values are essential for tracking system performance, |
|
analyzing trends, and ensuring accurate monitoring of fluid 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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|
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- Measured Value: The actual 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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|
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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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|
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Measured magnitude values are crucial for real-time monitoring, historical analysis, |
|
and calibration processes within measurement systems.' |
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- source_sentence: measurement system tag |
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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. |
|
It serves as the interface between the physical measurement system and the computational |
|
processes that record and analyze flow data. |
|
|
|
|
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Key Aspects of a Meter Stream: |
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|
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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 |
|
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.' |
|
- 'What is an Equipment Tag? |
|
|
|
An Equipment Tag is a unique 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. |
|
|
|
|
|
Key Aspects of Equipment Tags: |
|
|
|
- Equipment-Tag: A distinct label or identifier that uniquely marks the equipment |
|
in operation. |
|
|
|
- Equipment ID: Links the tag to the corresponding equipment unit. |
|
|
|
- Belonging Measurement System: Specifies which measurement system the tagged |
|
equipment is part of. |
|
|
|
- Equipment Type Name: Classifies the equipment (e.g., transmitter, thermometer), |
|
aiding in organization and system integration. |
|
|
|
The Equipment Tag is essential for tracking and managing operational equipment |
|
within a measurement system, ensuring proper identification, monitoring, and maintenance.' |
|
- '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 |
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**measurement technologies**, including **differential (DIF)** and **linear (LIN)**, |
|
depending on the application. Measurement systems can be classified based on their |
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**application type**, such as **fiscal** or **custody transfer**. ' |
|
- source_sentence: uncertainty points |
|
sentences: |
|
- 'What is a Calibration Point? |
|
|
|
A 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. |
|
|
|
|
|
Key Aspects of Calibration Points: |
|
|
|
- Calibration Report Association: Each calibration point belongs to a specific |
|
calibration report, linking it to a broader calibration procedure. |
|
|
|
- Reference Values: Theoretical or expected values used as a benchmark for measurement |
|
validation. |
|
|
|
- Measured Values: The actual recorded values during calibration, reflecting the |
|
instrument’s response. |
|
|
|
- Errors: The difference between reference and measured values, indicating possible |
|
measurement inaccuracies. |
|
|
|
Calibration points are essential for evaluating instrument performance, ensuring |
|
compliance with standards, and maintaining measurement reliability.' |
|
- '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.' |
|
- 'What is a Fluid? |
|
|
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A 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.' |
|
datasets: |
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- Lauther/measuring-embeddings-v4 |
|
pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
|
--- |
|
|
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# SentenceTransformer based on intfloat/multilingual-e5-large-instruct |
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|
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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 [measuring-embeddings-v4](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4) 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 |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
|
- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision 84344a23ee1820ac951bc365f1e91d094a911763 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [measuring-embeddings-v4](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4) |
|
<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
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(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}) |
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(2): Normalize() |
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) |
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``` |
|
|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
|
First install the Sentence Transformers library: |
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|
|
```bash |
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pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
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from sentence_transformers import SentenceTransformer |
|
|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Lauther/measuring-embeddings-v4.1") |
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# Run inference |
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sentences = [ |
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'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] |
|
|
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# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
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|
|
<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.* |
|
--> |
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|
|
<!-- |
|
## 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.* |
|
--> |
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|
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### measuring-embeddings-v4 |
|
|
|
* Dataset: [measuring-embeddings-v4](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4) at [1e3ca2c](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4/tree/1e3ca2c224ad58d1cc57b797997231e22154e471) |
|
* Size: 3,075 training samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | score | |
|
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 3 tokens</li><li>mean: 7.55 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 180.22 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 0.07</li><li>mean: 0.21</li><li>max: 0.95</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | score | |
|
|:--------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
|
| <code>last calibrated span</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>0.1</code> | |
|
| <code>flow computer configuration</code> | <code>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. </code> | <code>0.1</code> | |
|
| <code>uncertainty certificate number</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>0.1</code> | |
|
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
|
```json |
|
{ |
|
"scale": 20.0, |
|
"similarity_fct": "pairwise_cos_sim" |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
|
#### measuring-embeddings-v4 |
|
|
|
* Dataset: [measuring-embeddings-v4](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4) at [1e3ca2c](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4/tree/1e3ca2c224ad58d1cc57b797997231e22154e471) |
|
* Size: 659 evaluation samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
|
* Approximate statistics based on the first 659 samples: |
|
| | sentence1 | sentence2 | score | |
|
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 3 tokens</li><li>mean: 7.63 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 186.36 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 0.07</li><li>mean: 0.2</li><li>max: 0.9</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | score | |
|
|:-----------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------| |
|
| <code>measurement system details</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>0.15</code> | |
|
| <code>measurement system tag EMED-3102-02-010</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> | <code>0.24</code> | |
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| <code>static pressure</code> | <code>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.</code> | <code>0.1</code> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 4 |
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- `per_device_eval_batch_size`: 4 |
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- `gradient_accumulation_steps`: 4 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `warmup_ratio`: 0.1 |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 4 |
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- `per_device_eval_batch_size`: 4 |
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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`: 4 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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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`: 1.0 |
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- `num_train_epochs`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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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 |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `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 |
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- `bf16`: False |
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- `fp16`: False |
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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 |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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|
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</details> |
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|
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### Framework Versions |
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- Python: 3.11.0 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.49.0 |
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- PyTorch: 2.6.0+cu124 |
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- Accelerate: 1.4.0 |
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- Datasets: 3.3.2 |
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- Tokenizers: 0.21.0 |
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|
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## Citation |
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|
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### BibTeX |
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|
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
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#### CoSENTLoss |
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```bibtex |
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@online{kexuefm-8847, |
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title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
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author={Su Jianlin}, |
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year={2022}, |
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month={Jan}, |
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url={https://kexue.fm/archives/8847}, |
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} |
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``` |
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