README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:5220
  - loss:CosineSimilarityLoss
base_model: NovaSearch/stella_en_1.5B_v5
widget:
  - source_sentence: Identify the column that stores the uncertainty value.
    sentences:
      - >-
        What is measuring equipment?

        Measuring equipment refers to the devices that make up a measurement
        system. Each piece of equipment has:

        - A unique serial number for identification.

        - A technical name, such as transmitter, plate, thermometer, etc.


        How is equipment assigned to a measurement system?

        When equipment is assigned to a measurement system, it is given a unique
        identifier called an ""Equipment Tag.""

        - If a piece of equipment has a tag, it is considered in use in a
        measurement system.

        - If it does not have a tag, it is considered spare or unused


        Equipment assignment based on technology:

        The type of equipment assigned to a measurement system depends on the
        technology used, for example:

        1. Differential technology (for gas measurement):
            - Static pressure transmitters
            - Differential pressure transmitters
            - Temperature transmitters
            - RTDs (thermometers)
            - Orifice plates
            - Straight stretch

        2. Linear technology (for gas measurement):
            - Temperature transmitters
            - RTDs
            - Static pressure transmitters
            - Ultrasonic meters

        Relationship between equipment and measurement systems:

        - A measurement system can have multiple pieces of equipment.

        - However, a piece of equipment can only be assigned to one measurement
        system.


        Database management:

        - The database includes a special table to manage the list of equipment
        assigned to measurement systems.

        - When a user refers to an ""Equipment Tag"", they are searching for
        operational equipment assigned to a measurement system.

        - If a user is looking for spare or unused equipment, they are searching
        for equipment not listed in the tagged equipment table.

        - Commonly used when user refers directly to an ""Equipment Tag"
      - >-
        What is equipment calibration?

        Calibration is a metrological verification process used to ensure the
        accuracy of measurement equipment. It is performed periodically, based
        on intervals set by the company or a regulatory body.


        Purpose of calibration:

        The calibration process corrects any deviations in how the equipment
        measures physical magnitudes (variables). This ensures the equipment
        provides accurate and reliable data.


        Calibration cycles:

        There are two main calibration cycles:

        1. As-found: Represents the equipment's measurement accuracy before any
        adjustments are made. This cycle is almost always implemented.

        2. As-left: Represents the equipment's measurement accuracy after
        adjustments are made. This cycle is used depending on regulatory
        requirements.


        Calibration uncertainty:

        - Uncertainty is included in the results of a calibration.

        - Calibration uncertainty refers to the margin of error in the device's
        measurements, which also affects the uncertainty of the measured
        variable or magnitude.
      - >-
        What kind of data store an equipment?

        Equipments can capture meteorological data, such as pressure,
        temperature, and volume (magnitudes). This data is essential for users
        to perform various calculations.


        Data storage:

        - The measured values are stored in a special table in the database for
        magnitudes. This table contains the values of the variables captured by
        the equipments.

        - These values are **direct measurements** from the fluid (e.g., raw
        pressure, temperature, or volume readings). **They are not calculated
        values**, such as uncertainty.

        - The values stored in the variable values table are **different** from
        variable uncertainty values, which are calculated separately and
        represent the margin of error.


        Accessing the data:

        - Users typically access the data by referring to the readings from the
        measurement system, not directly from the individual equipments.

        - The readings are stored in a "variable values" table within the
        database.


        Linking variable names:

        If the user needs to know the name of a variable, they must link the
        data to another table that stores information about the types of
        variables.
  - source_sentence: SELECT * FROM EquipmentType LIMIT 1
    sentences:
      - >-
        What kind of data store an equipment?

        Equipments can capture meteorological data, such as pressure,
        temperature, and volume (magnitudes). This data is essential for users
        to perform various calculations.


        Data storage:

        - The measured values are stored in a special table in the database for
        magnitudes. This table contains the values of the variables captured by
        the equipments.

        - These values are **direct measurements** from the fluid (e.g., raw
        pressure, temperature, or volume readings). **They are not calculated
        values**, such as uncertainty.

        - The values stored in the variable values table are **different** from
        variable uncertainty values, which are calculated separately and
        represent the margin of error.


        Accessing the data:

        - Users typically access the data by referring to the readings from the
        measurement system, not directly from the individual equipments.

        - The readings are stored in a "variable values" table within the
        database.


        Linking variable names:

        If the user needs to know the name of a variable, they must link the
        data to another table that stores information about the types of
        variables.
      - >-
        How does a flow computer generate and store reports?

        A flow computer generates daily or hourly reports to provide users with
        operational data. These reports are stored in the flow computer's memory
        in an organized format.


        Report structure:

        - Each report includes:

        - Date and time of the data recording.

        - Data recorded from flow computers.


        Data storage in tables:

        The reports are saved in two tables:

        1. Main table (Index):
            - Stores the date, time, and flow computer identifier.
        2. Detail table:
            - Stores the measured values associated with the report.

        Connection to the Modbus table:

        The flow computer's reports are linked to a Modbus table. This table
        contains the names corresponding to each value in the reports, making it
        easier to interpret the data.
      - >-
        What is a flow computer?

        A flow computer is a device used in measurement engineering. It collects
        analog and digital data from flow meters and other sensors.


        Key features of a flow computer:

        - It has a unique name, firmware version, and manufacturer information.

        - It is designed to record and process data such as temperature,
        pressure, and fluid volume (for gases or oils).


        Main function:

        The flow computer sends the collected data to a measurement system. This
        allows measurement engineers to analyze the data and perform their tasks
        effectively.
  - source_sentence: What tables store measurement system data?
    sentences:
      - >-
        What is uncertainty?

        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.


        Types of uncertainty:

        There are two main types of uncertainty:

        1. Uncertainty of magnitudes (variables):
            - Refers to the uncertainty of specific variables, such as temperature or pressure.
            - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.
            - This uncertainty serves as a starting point for further calculations related to the equipment.

        2. Uncertainty of the measurement system:
            - Refers to the uncertainty calculated for the overall flow measurement.
            - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.

        Key points:

        - The uncertainties of magnitudes (variables) are the foundation for
        calculating the uncertainty of the measurement system. Think of them as
        the "building blocks."

        - Do not confuse the two types of uncertainty:
            - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).
            - **Uncertainty of the measurement system**: Specific to the overall flow measurement.

        Database storage for uncertainties:

        In the database, uncertainty calculations are stored in two separate
        tables:

        1. Uncertainty of magnitudes (variables):
            - Stores the uncertainty values for specific variables (e.g., temperature, pressure).

        2. Uncertainty of the measurement system:
            - Stores the uncertainty values for the overall flow measurement system.

        How to retrieve uncertainty data:

        - To find the uncertainty of the measurement system, join the
        measurement systems table with the uncertainty of the measurement system
        table.

        - To find the uncertainty of a specific variable (magnitude), join the
        measurement systems table with the uncertainty of magnitudes (variables)
        table.


        Important note:

        Do not confuse the two types of uncertainty:

        - If the user requests the uncertainty of the measurement system, use
        the first join (measurement systems table + uncertainty of the
        measurement system table).

        - If the user requests the uncertainty of a specific variable
        (magnitude) in a report, use the second join (measurement systems table
        + uncertainty of magnitudes table).
      - >-
        What is a measurement system?

        A measurement system, also referred to as a delivery point, measurement
        point, or reception point, is used to measure and monitor fluids in
        industrial processes.


        Key characteristics of a measurement system:

        1. Measurement technology:
            - Differential: Used for precise measurements.
            - Linear: Used for straightforward measurements.

        2. System identifier (TAG):
            - A unique identifier for the system.

        3. Fluid type:
            - The system can measure gases, oils, condensates, water, steam, or other fluids.
        4. System type:
            - Specifies the category or purpose of the system.

        Measurement technology by fluid type:

        - Gas measurement systems: Use both linear and differential measurement
        technologies.

        - Oil measurement systems: Do not use linear or differential
        technologies; they are programmed differently."



        Classification of measurement systems:

        Measurement systems are classified based on the stage of the process in
        which they are used. Common classifications include:

        - Fiscal

        - Operational

        - Appropriation

        - Custody

        - Production Poços
      - >-
        What do measurement equipment measure?

        Each equipment measures a physical magnitude, also known as a variable.
        Based on the type of variable they measure, devices are classified into
        different categories.


        Equipment classification:

        - Primary meter: Assigned by default to equipments like orifice plates.

        - Secondary meter: Assigned by default to equipments like transmitters.

        - Tertiary meter: Used for other types of equipments.


        Equipment types in the database:

        The database includes a table listing all equipment types. Examples of
        equipment types are:

        - Differential pressure transmitters

        - RTDs (Resistance Temperature Detectors)

        - Orifice plates

        - Multivariable transmitters

        - Ultrasonic meters


        Meteorological checks for equipments:

        Each equipment type is assigned a meteorological check, which can be
        either:

        - Calibration: To ensure measurement accuracy.

        - Inspection: To verify proper functioning.


        Data storage in tables:

        The database also includes a separate table for equipment
        classifications, which are:

        - Primary meter

        - Secondary meter

        - Tertiary meter

        So, an equipment has equipment types and this types has classifications.
  - source_sentence: What is the table structure for equipment types?
    sentences:
      - >-
        How does a flow computer generate and store reports?

        A flow computer generates daily or hourly reports to provide users with
        operational data. These reports are stored in the flow computer's memory
        in an organized format.


        Report structure:

        - Each report includes:

        - Date and time of the data recording.

        - Data recorded from flow computers.


        Data storage in tables:

        The reports are saved in two tables:

        1. Main table (Index):
            - Stores the date, time, and flow computer identifier.
        2. Detail table:
            - Stores the measured values associated with the report.

        Connection to the Modbus table:

        The flow computer's reports are linked to a Modbus table. This table
        contains the names corresponding to each value in the reports, making it
        easier to interpret the data.
      - >-
        What is measuring equipment?

        Measuring equipment refers to the devices that make up a measurement
        system. Each piece of equipment has:

        - A unique serial number for identification.

        - A technical name, such as transmitter, plate, thermometer, etc.


        How is equipment assigned to a measurement system?

        When equipment is assigned to a measurement system, it is given a unique
        identifier called an ""Equipment Tag.""

        - If a piece of equipment has a tag, it is considered in use in a
        measurement system.

        - If it does not have a tag, it is considered spare or unused


        Equipment assignment based on technology:

        The type of equipment assigned to a measurement system depends on the
        technology used, for example:

        1. Differential technology (for gas measurement):
            - Static pressure transmitters
            - Differential pressure transmitters
            - Temperature transmitters
            - RTDs (thermometers)
            - Orifice plates
            - Straight stretch

        2. Linear technology (for gas measurement):
            - Temperature transmitters
            - RTDs
            - Static pressure transmitters
            - Ultrasonic meters

        Relationship between equipment and measurement systems:

        - A measurement system can have multiple pieces of equipment.

        - However, a piece of equipment can only be assigned to one measurement
        system.


        Database management:

        - The database includes a special table to manage the list of equipment
        assigned to measurement systems.

        - When a user refers to an ""Equipment Tag"", they are searching for
        operational equipment assigned to a measurement system.

        - If a user is looking for spare or unused equipment, they are searching
        for equipment not listed in the tagged equipment table.

        - Commonly used when user refers directly to an ""Equipment Tag"
      - >-
        What is uncertainty?

        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.


        Types of uncertainty:

        There are two main types of uncertainty:

        1. Uncertainty of magnitudes (variables):
            - Refers to the uncertainty of specific variables, such as temperature or pressure.
            - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.
            - This uncertainty serves as a starting point for further calculations related to the equipment.

        2. Uncertainty of the measurement system:
            - Refers to the uncertainty calculated for the overall flow measurement.
            - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.

        Key points:

        - The uncertainties of magnitudes (variables) are the foundation for
        calculating the uncertainty of the measurement system. Think of them as
        the "building blocks."

        - Do not confuse the two types of uncertainty:
            - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).
            - **Uncertainty of the measurement system**: Specific to the overall flow measurement.

        Database storage for uncertainties:

        In the database, uncertainty calculations are stored in two separate
        tables:

        1. Uncertainty of magnitudes (variables):
            - Stores the uncertainty values for specific variables (e.g., temperature, pressure).

        2. Uncertainty of the measurement system:
            - Stores the uncertainty values for the overall flow measurement system.

        How to retrieve uncertainty data:

        - To find the uncertainty of the measurement system, join the
        measurement systems table with the uncertainty of the measurement system
        table.

        - To find the uncertainty of a specific variable (magnitude), join the
        measurement systems table with the uncertainty of magnitudes (variables)
        table.


        Important note:

        Do not confuse the two types of uncertainty:

        - If the user requests the uncertainty of the measurement system, use
        the first join (measurement systems table + uncertainty of the
        measurement system table).

        - If the user requests the uncertainty of a specific variable
        (magnitude) in a report, use the second join (measurement systems table
        + uncertainty of magnitudes table).
  - source_sentence: What columns store the uncertainty values?
    sentences:
      - >-
        What is a measurement system?

        A measurement system, also referred to as a delivery point, measurement
        point, or reception point, is used to measure and monitor fluids in
        industrial processes.


        Key characteristics of a measurement system:

        1. Measurement technology:
            - Differential: Used for precise measurements.
            - Linear: Used for straightforward measurements.

        2. System identifier (TAG):
            - A unique identifier for the system.

        3. Fluid type:
            - The system can measure gases, oils, condensates, water, steam, or other fluids.
        4. System type:
            - Specifies the category or purpose of the system.

        Measurement technology by fluid type:

        - Gas measurement systems: Use both linear and differential measurement
        technologies.

        - Oil measurement systems: Do not use linear or differential
        technologies; they are programmed differently."



        Classification of measurement systems:

        Measurement systems are classified based on the stage of the process in
        which they are used. Common classifications include:

        - Fiscal

        - Operational

        - Appropriation

        - Custody

        - Production Poços
      - >-
        How are flow computers and measurement systems related?

        Flow computers can have multiple systems assigned to them. However, a
        measurement system can only be assigned to one flow computer.


        Database terminology:

        In the database, this relationship is referred to as:

        - Meter streams

        - Meter runs

        - Sections


        Storage of the relationship:

        The relationship between a flow computer and its assigned measurement
        system is stored in a special table.


        User context:

        When a user refers to a "meter stream," they are indicating that they
        are searching for a measurement system assigned to a specific flow
        computer.
      - >-
        What is uncertainty?

        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.


        Types of uncertainty:

        There are two main types of uncertainty:

        1. Uncertainty of magnitudes (variables):
            - Refers to the uncertainty of specific variables, such as temperature or pressure.
            - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.
            - This uncertainty serves as a starting point for further calculations related to the equipment.

        2. Uncertainty of the measurement system:
            - Refers to the uncertainty calculated for the overall flow measurement.
            - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.

        Key points:

        - The uncertainties of magnitudes (variables) are the foundation for
        calculating the uncertainty of the measurement system. Think of them as
        the "building blocks."

        - Do not confuse the two types of uncertainty:
            - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).
            - **Uncertainty of the measurement system**: Specific to the overall flow measurement.

        Database storage for uncertainties:

        In the database, uncertainty calculations are stored in two separate
        tables:

        1. Uncertainty of magnitudes (variables):
            - Stores the uncertainty values for specific variables (e.g., temperature, pressure).

        2. Uncertainty of the measurement system:
            - Stores the uncertainty values for the overall flow measurement system.

        How to retrieve uncertainty data:

        - To find the uncertainty of the measurement system, join the
        measurement systems table with the uncertainty of the measurement system
        table.

        - To find the uncertainty of a specific variable (magnitude), join the
        measurement systems table with the uncertainty of magnitudes (variables)
        table.


        Important note:

        Do not confuse the two types of uncertainty:

        - If the user requests the uncertainty of the measurement system, use
        the first join (measurement systems table + uncertainty of the
        measurement system table).

        - If the user requests the uncertainty of a specific variable
        (magnitude) in a report, use the second join (measurement systems table
        + uncertainty of magnitudes table).
datasets:
  - Lauther/embeddings-train-semantic
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on NovaSearch/stella_en_1.5B_v5

This is a sentence-transformers model finetuned from NovaSearch/stella_en_1.5B_v5 on the embeddings-train-semantic 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.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: NovaSearch/stella_en_1.5B_v5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • embeddings-train-semantic

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 1536, '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): Dense({'in_features': 1536, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/emb-stella_en_1.5B_v5-1e")
# Run inference
sentences = [
    'What columns store the uncertainty values?',
    'How are flow computers and measurement systems related?\nFlow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.\n\nDatabase terminology:\nIn the database, this relationship is referred to as:\n- Meter streams\n- Meter runs\n- Sections\n\nStorage of the relationship:\nThe relationship between a flow computer and its assigned measurement system is stored in a special table.\n\nUser context:\nWhen a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.',
    '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.\n\nHow to retrieve uncertainty data:\n- To find the uncertainty of the measurement system, join the measurement systems table with the uncertainty of the measurement system table.\n- To find the uncertainty of a specific variable (magnitude), join the measurement systems table with the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not confuse the two types of uncertainty:\n- If the user requests the uncertainty of the measurement system, use the first join (measurement systems table + uncertainty of the measurement system table).\n- If the user requests the uncertainty of a specific variable (magnitude) in a report, use the second join (measurement systems table + uncertainty of magnitudes table).',
]
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]

Training Details

Training Dataset

embeddings-train-semantic

• Dataset: embeddings-train-semantic at ce90f53
• Size: 5,220 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 5 tokens mean: 14.22 tokens max: 69 tokens	min: 105 tokens mean: 219.9 tokens max: 447 tokens	min: 0.0 mean: 0.23 max: 1.0

• Samples:

  sentence1	sentence2	score
  What is the data type of differential pressure in the measurement system?	What is uncertainty? 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. Types of uncertainty: There are two main types of uncertainty: 1. Uncertainty of magnitudes (variables): - Refers to the uncertainty of specific variables, such as temperature or pressure. - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual. - This uncertainty serves as a starting point for further calculations related to the equipment. 2. Uncertainty of the measurement system: - Refers to the uncertainty calculated for the overall flow measurement. - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system. Key points: - The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...	0.15000000000000002
  What is the structure of the &&&equipment_data&&& table?	How are flow computers and measurement systems related? Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer. Database terminology: In the database, this relationship is referred to as: - Meter streams - Meter runs - Sections Storage of the relationship: The relationship between a flow computer and its assigned measurement system is stored in a special table. User context: When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.	0.35000000000000003
  Find the columns in the flow computer table that identify the flow computer.	What kind of data store an equipment? Equipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations. Data storage: - The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments. - These values are direct measurements from the fluid (e.g., raw pressure, temperature, or volume readings). They are not calculated values, such as uncertainty. - The values stored in the variable values table are different from variable uncertainty values, which are calculated separately and represent the margin of error. Accessing the data: - Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments. - The readings are stored in a "variable values" table within the database. Linking variable names: If the user needs to kno...	0.1

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Evaluation Dataset

embeddings-train-semantic

• Dataset: embeddings-train-semantic at ce90f53
• Size: 652 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 652 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 5 tokens mean: 13.83 tokens max: 69 tokens	min: 105 tokens mean: 217.37 tokens max: 447 tokens	min: 0.0 mean: 0.24 max: 0.9

• Samples:

  sentence1	sentence2	score
  How can I filter uncertainty reports by equipment tag?	How does a flow computer generate and store reports? A flow computer generates daily or hourly reports to provide users with operational data. These reports are stored in the flow computer's memory in an organized format. Report structure: - Each report includes: - Date and time of the data recording. - Data recorded from flow computers. Data storage in tables: The reports are saved in two tables: 1. Main table (Index): - Stores the date, time, and flow computer identifier. 2. Detail table: - Stores the measured values associated with the report. Connection to the Modbus table: The flow computer's reports are linked to a Modbus table. This table contains the names corresponding to each value in the reports, making it easier to interpret the data.	0.09999999999999999
  What is the purpose of the flow_data table?	What is uncertainty? 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. Types of uncertainty: There are two main types of uncertainty: 1. Uncertainty of magnitudes (variables): - Refers to the uncertainty of specific variables, such as temperature or pressure. - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual. - This uncertainty serves as a starting point for further calculations related to the equipment. 2. Uncertainty of the measurement system: - Refers to the uncertainty calculated for the overall flow measurement. - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system. Key points: - The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...	0.15000000000000002
  What is the column name for the report date in the Reports table?	What is equipment calibration? Calibration is a metrological verification process used to ensure the accuracy of measurement equipment. It is performed periodically, based on intervals set by the company or a regulatory body. Purpose of calibration: The calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data. Calibration cycles: There are two main calibration cycles: 1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented. 2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements. Calibration uncertainty: - Uncertainty is included in the results of a calibration. - Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or ...	0.1

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 4
• gradient_accumulation_steps: 4
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 4
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 4
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• 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
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• 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: 0
• 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

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.0307	10	0.2817	-
0.0613	20	0.1694	-
0.0920	30	0.1173	-
0.1226	40	0.0953	-
0.1533	50	0.0959	0.0250
0.1839	60	0.0948	-
0.2146	70	0.1095	-
0.2452	80	0.1269	-
0.2759	90	0.1023	-
0.3065	100	0.0775	0.0220
0.3372	110	0.099	-
0.3678	120	0.077	-
0.3985	130	0.0837	-
0.4291	140	0.0677	-
0.4598	150	0.077	0.0198
0.4904	160	0.0793	-
0.5211	170	0.0847	-
0.5517	180	0.0786	-
0.5824	190	0.0601	-
0.6130	200	0.0474	0.0166
0.6437	210	0.0778	-
0.6743	220	0.0699	-
0.7050	230	0.066	-
0.7356	240	0.0741	-
0.7663	250	0.0576	0.0136
0.7969	260	0.0418	-
0.8276	270	0.0648	-
0.8582	280	0.0566	-
0.8889	290	0.0625	-
0.9195	300	0.0487	0.0131
0.9502	310	0.0533	-
0.9808	320	0.0405	-

Framework Versions

• Python: 3.11.0
• Sentence Transformers: 3.4.0
• Transformers: 4.48.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}