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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4151
- loss:TripletLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
- source_sentence: flow computer tags
sentences:
- "What is an Uncertainty Curve Point?\nAn Uncertainty Curve Point represents a\
\ data point used to construct the uncertainty curve of a measurement system.\
\ These curves help analyze how measurement uncertainty behaves under different\
\ flow rate conditions, ensuring accuracy and reliability in uncertainty assessments.\n\
\nKey Aspects of an Uncertainty Curve Point:\n- Uncertainty File ID: Links the\
\ point to the specific uncertainty dataset, ensuring traceability.\nEquipment\
\ Tag ID: Identifies the equipment associated with the uncertainty measurement,\
\ crucial for system validation.\n- Uncertainty Points: Represent a list uncertainty\
\ values recorded at specific conditions, forming part of the overall uncertainty\
\ curve. Do not confuse this uncertainty points with the calculated uncertainty.\
\ \n- Flow Rate Points: Corresponding flow rate values at which the uncertainty\
\ was measured, essential for evaluating performance under varying operational\
\ conditions.\nThese points are fundamental for generating uncertainty curves,\
\ which are used in calibration, validation, and compliance assessments to ensure\
\ measurement reliability in industrial processes.\"\n\n**IMPORTANT**: Do not\
\ confuse the two types of **Points**:\n - **Uncertainty Curve Point**: Specific\
\ to a measurement system uncertainty or uncertainty simulation or uncertainty\
\ curve.\n - **Calibration Point**: Specific to the calibration.\n - **Uncertainty\
\ values**: Do not confuse these uncertainty points with the single calculated\
\ uncertainty."
- '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).'
- 'What is a Measured Magnitude Value?
A Measured Magnitude Value represents a **DAILY** recorded physical measurement
of a variable within a monitored fluid. These values are essential for tracking
system performance, analyzing trends, and ensuring accurate monitoring of fluid
properties.
Key Aspects of a Measured Magnitude Value:
- Measurement Date: The timestamp indicating when the measurement was recorded.
- Measured Value: The daily numeric result of the recorded physical magnitude.
- Measurement System Association: Links the measured value to a specific measurement
system responsible for capturing the data.
- Variable Association: Identifies the specific variable (e.g., temperature, pressure,
flow rate) corresponding to the recorded value.
Measured magnitude values are crucial for real-time monitoring, historical analysis,
and calibration processes within measurement systems.
Database advices:
This values also are in **historics of a flow computer report**. Although, to
go directly instead querying the flow computer report you can do it by going to
the table of variables data in the database.'
- source_sentence: PTE CAPUAVA B
sentences:
- "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
\ and reliability of results obtained from equipment or measurement systems. It\
\ quantifies the potential error or margin of error in measurements.\n\nTypes\
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\
\ such as temperature or pressure.\n - It is calculated after calibrating a\
\ device or obtained from the **equipment** manufacturer's manual.\n - This\
\ uncertainty serves as a starting point for further calculations related to the\
\ equipment.\n\n2. Uncertainty of the measurement system:\n - Refers to the\
\ uncertainty calculated for the overall flow measurement.\n - It depends on\
\ the uncertainties of the individual variables (magnitudes) and represents the\
\ combined margin of error for the entire system.\n\nKey points:\n- The uncertainties\
\ of magnitudes (variables) are the foundation for calculating the uncertainty\
\ of the measurement system. Think of them as the \"building blocks.\"\n- Do not\
\ confuse the two types of uncertainty:\n - **Uncertainty of magnitudes/variables**:\
\ Specific to individual variables (e.g., temperature, pressure).\n - **Uncertainty\
\ of the measurement system**: Specific to the overall flow measurement."
- 'What is a Measurement Unit?
A Measurement Unit defines the standard for quantifying a physical magnitude (e.g.,
temperature, pressure, volume). It establishes a consistent reference for interpreting
values recorded in a measurement system.
Each measurement unit is associated with a specific magnitude, ensuring that values
are correctly interpreted within their context. For example:
- °C (Celsius) → Used for temperature
- psi (pounds per square inch) → Used for pressure
- m³ (cubic meters) → Used for volume
Measurement units are essential for maintaining consistency across recorded data,
ensuring comparability, and enabling accurate calculations within measurement
systems.'
- "What is a Measurement Type?\nMeasurement 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. \n\n- **Fiscal measurements** are used for tax and regulatory\
\ reporting, ensuring accurate financial transactions based on measured quantities.\
\ \n- **Appropriation measurements** track resource allocation and ownership\
\ distribution among stakeholders. \n- **Operational measurements** support real-time\
\ monitoring and process optimization within industrial operations. \n- **Custody\
\ measurements** are essential for legal and contractual transactions, ensuring\
\ precise handover of fluids between parties. \n\nThese classifications play\
\ a crucial role in compliance, financial accuracy, and operational efficiency\
\ across industries such as oil and gas, water management, and energy distribution.\
\ "
- source_sentence: PTE PARACAMBI A
sentences:
- '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 uncertainty?\nUncertainty is a measure of confidence in the precision\
\ and reliability of results obtained from equipment or measurement systems. It\
\ quantifies the potential error or margin of error in measurements.\n\nTypes\
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\
\ such as temperature or pressure.\n - It is calculated after calibrating a\
\ device or obtained from the **equipment** manufacturer's manual.\n - This\
\ uncertainty serves as a starting point for further calculations related to the\
\ equipment.\n\n2. Uncertainty of the measurement system:\n - Refers to the\
\ uncertainty calculated for the overall flow measurement.\n - It depends on\
\ the uncertainties of the individual variables (magnitudes) and represents the\
\ combined margin of error for the entire system.\n\nKey points:\n- The uncertainties\
\ of magnitudes (variables) are the foundation for calculating the uncertainty\
\ of the measurement system. Think of them as the \"building blocks.\"\n- Do not\
\ confuse the two types of uncertainty:\n - **Uncertainty of magnitudes/variables**:\
\ Specific to individual variables (e.g., temperature, pressure).\n - **Uncertainty\
\ of the measurement system**: Specific to the overall flow measurement."
- 'What is an Equipment Tag?
An Equipment Tag is a unique label string identifier assigned to equipment that
is actively installed and in use within a measurement system. It differentiates
between equipment in general (which may be in storage or inactive) and equipment
that is currently operational in a system.
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.'
- source_sentence: PTE UTE BAIXADA FLUMINENSE A
sentences:
- 'What are Flow Computer Types?
Flow computer types categorize different models of flow computers used in measurement
systems, such as OMNI, KROHNE, ROC, FC302, S600, FLOWBOSS, F407, F107, and ThermoFisher.
Each type is defined by its capabilities, functionalities, and applications, determining
how it processes measurement data, performs calculations, and enables real-time
monitoring. Understanding these types is essential for selecting the right equipment
to ensure precise flow measurement, system integration, and operational efficiency.'
- 'What is an Equipment Type?
An Equipment Type defines a category of measurement or monitoring devices used
in a system. Each type of equipment is classified based on its function, the physical
magnitude it measures, and its associated measurement unit.
Key Aspects of Equipment Types:
- Categorization: Equipment types include devices like transmitters, thermometers,
and other measurement instruments.
- Classification: Equipment can be primary (directly involved in measurement)
or secondary (supporting measurement processes).
- Measurement Unit: Each equipment type is linked to a unit of measure (e.g.,
°C for temperature, psi for pressure).
- Measured Magnitude: Defines what the equipment measures (e.g., temperature,
pressure, volume).
Understanding equipment types ensures correct data interpretation, proper calibration,
and accurate measurement within a system.'
- 'What is a measurement system?
**Measurement systems** are essential components in industrial measurement and
processing. They are identified by a unique **Tag** and are associated with a
specific **installation** and **fluid type**. These systems utilize different
**measurement technologies**, including **differential (DIF)** and **linear (LIN)**,
depending on the application. Measurement systems can be classified based on their
**application type**, such as **fiscal** or **custody transfer**. '
- source_sentence: PTE BRAGANÇA PAULISTA C
sentences:
- "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
\ and reliability of results obtained from equipment or measurement systems. It\
\ quantifies the potential error or margin of error in measurements.\n\nTypes\
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\
\ such as temperature or pressure.\n - It is calculated after calibrating a\
\ device or obtained from the equipment manufacturer's manual.\n - This uncertainty\
\ serves as a starting point for further calculations related to the equipment.\n\
\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated\
\ for the overall flow measurement.\n - It depends on the uncertainties of\
\ the individual variables (magnitudes) and represents the combined margin of\
\ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
\ (variables) are the foundation for calculating the uncertainty of the measurement\
\ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
\ types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific\
\ to individual variables (e.g., temperature, pressure).\n - **Uncertainty\
\ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
\ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
\ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores\
\ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values\
\ for the overall flow measurement system."
- 'What is a Measurement Unit?
A Measurement Unit defines the standard for quantifying a physical magnitude (e.g.,
temperature, pressure, volume). It establishes a consistent reference for interpreting
values recorded in a measurement system.
Each measurement unit is associated with a specific magnitude, ensuring that values
are correctly interpreted within their context. For example:
- °C (Celsius) → Used for temperature
- psi (pounds per square inch) → Used for pressure
- m³ (cubic meters) → Used for volume
Measurement units are essential for maintaining consistency across recorded data,
ensuring comparability, and enabling accurate calculations within measurement
systems.'
- 'What is an Uncertainty Composition?
An Uncertainty Composition represents a specific factor that contributes to the
overall uncertainty of a measurement system. These components are essential for
evaluating the accuracy and reliability of measurements by identifying and quantifying
the sources of uncertainty.
Key Aspects of an Uncertainty Component:
- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance,
covariance) influencing the measurement system.
- Value of Composition: Quantifies the component’s contribution to the total uncertainty,
helping to analyze which factors have the greatest impact.
- Uncertainty File ID: Links the component to a specific uncertainty dataset for
traceability and validation.
Understanding these components is critical for uncertainty analysis, ensuring
compliance with industry standards and improving measurement precision.'
datasets:
- Lauther/d4-embeddings-TripletLoss
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [d4-embeddings-triplet_loss](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision 274baa43b0e13e37fafa6428dbc7938e62e5c439 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [d4-embeddings-triplet_loss](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/d4-embeddings-v3.0")
# Run inference
sentences = [
'PTE BRAGANÇA PAULISTA C',
'What is an Uncertainty Composition?\nAn Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty.\n\nKey Aspects of an Uncertainty Component:\n- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.\n- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.\n- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.\nUnderstanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.',
'What is a Measurement Unit?\nA Measurement Unit defines the standard for quantifying a physical magnitude (e.g., temperature, pressure, volume). It establishes a consistent reference for interpreting values recorded in a measurement system.\n\nEach measurement unit is associated with a specific magnitude, ensuring that values are correctly interpreted within their context. For example:\n\n- °C (Celsius) → Used for temperature\n- psi (pounds per square inch) → Used for pressure\n- m³ (cubic meters) → Used for volume\nMeasurement units are essential for maintaining consistency across recorded data, ensuring comparability, and enabling accurate calculations within measurement systems.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### d4-embeddings-triplet_loss
* Dataset: [d4-embeddings-triplet_loss](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss) at [4d11c52](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss/tree/4d11c524b62bf4c6107b71cebfe0e8947f6db208)
* Size: 4,151 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 9.03 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 213.47 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 168.24 tokens</li><li>max: 406 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Orifice Diameter (mm)</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>What is an Equipment Class?<br>An Equipment Class categorizes different types of equipment based on their function or role within a measurement system. This classification helps in organizing and distinguishing equipment types for operational, maintenance, and analytical purposes.<br><br>Each Equipment Class groups related equipment under a common category. Examples include:<br><br>Primary → Main measurement device in a system.<br>Secondary → Supporting measurement device, often used for verification.<br>Tertiary → Additional measurement equipment.<br>Valves → Flow control devices used in the system.<br>By defining Equipment Classes, the system ensures proper identification, tracking, and management of measurement-related assets.</code> |
| <code>prueba_gonzalo</code> | <code>What is a measurement system?<br>**Measurement systems** are essential components in industrial measurement and processing. They are identified by a unique **Tag** and are associated with a specific **installation** and **fluid type**. These systems utilize different **measurement technologies**, including **differential (DIF)** and **linear (LIN)**, depending on the application. Measurement systems can be classified based on their **application type**, such as **fiscal** or **custody transfer**. </code> | <code>What is a Measured Magnitude Value?<br>A Measured Magnitude Value represents a **DAILY** recorded physical measurement of a variable within a monitored fluid. These values are essential for tracking system performance, analyzing trends, and ensuring accurate monitoring of fluid properties.<br><br>Key Aspects of a Measured Magnitude Value:<br>- Measurement Date: The timestamp indicating when the measurement was recorded.<br>- Measured Value: The daily numeric result of the recorded physical magnitude.<br>- Measurement System Association: Links the measured value to a specific measurement system responsible for capturing the data.<br>- Variable Association: Identifies the specific variable (e.g., temperature, pressure, flow rate) corresponding to the recorded value.<br>Measured magnitude values are crucial for real-time monitoring, historical analysis, and calibration processes within measurement systems.<br><br>Database advices:<br>This values also are in **historics of a flow computer report**. Although, to go directl...</code> |
| <code>Vazao Instantanea</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>What is a report index or historic index?<br>Indexes represent the recorded reports generated by flow computers, classified into two types: <br>- **Hourly reports Index**: Store data for hourly events.<br>- **Daily reports Index**: Strore data for daily events.<br><br>These reports, also referred to as historical data or flow computer historical records, contain raw, first-hand measurements directly collected from the flow computer. The data has not been processed or used in any calculations, preserving its original state for analysis or validation.<br><br>The index is essential for locating specific values within the report.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.3
}
```
### Evaluation Dataset
#### d4-embeddings-triplet_loss
* Dataset: [d4-embeddings-triplet_loss](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss) at [4d11c52](https://huggingface.co/datasets/Lauther/d4-embeddings-TripletLoss/tree/4d11c524b62bf4c6107b71cebfe0e8947f6db208)
* Size: 1,038 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 9.02 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 209.21 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 172.36 tokens</li><li>max: 406 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>FQI-4301.4522B</code> | <code>What is a measurement system?<br>**Measurement systems** are essential components in industrial measurement and processing. They are identified by a unique **Tag** and are associated with a specific **installation** and **fluid type**. These systems utilize different **measurement technologies**, including **differential (DIF)** and **linear (LIN)**, depending on the application. Measurement systems can be classified based on their **application type**, such as **fiscal** or **custody transfer**. </code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the **equipment** manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...</code> |
| <code>PTE GUARATINGUETA B</code> | <code>What are historical report values?<br>These represent the recorded data points within flow computer reports. Unlike the report index, which serves as a reference to locate reports, these values contain the actual measurements and calculated data stored in the historical records.<br><br>Flow computer reports store two types of data values:<br><br>- **Hourly data values**: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on an hourly basis.<br>- **Daily data values**: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on a daily basis.<br>Each value is directly linked to its respective report index, ensuring traceability to the original flow computer record. These values maintain their raw integrity, providing a reliable source for analysis and validation.</code> | <code>What is Equipment?<br>An Equipment represents a physical device that may be used within a measurement system. Equipment can be active or inactive and is classified by type, such as transmitters, thermometers, or other measurement-related devices.<br><br>Key Aspects of Equipment:<br>- Serial Number: A unique identifier assigned to each equipment unit for tracking and reference.<br>- Current State: Indicates whether the equipment is currently in use (ACT) or inactive (INA).<br>- Associated Equipment Type: Defines the category of the equipment (e.g., transmitter, thermometer), allowing classification and management.<br>Equipment plays a critical role in measurement systems, ensuring accuracy and reliability in data collection and processing.</code> |
| <code>PTE BRAGANÇA PAULISTA B</code> | <code>What is an Uncertainty Composition?<br>An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty.<br><br>Key Aspects of an Uncertainty Component:<br>- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.<br>- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.<br>- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.<br>Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the **equipment** manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.3
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 80
- `per_device_eval_batch_size`: 80
- `weight_decay`: 0.01
- `max_grad_norm`: 0.5
- `num_train_epochs`: 15
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_num_workers`: 4
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 80
- `per_device_eval_batch_size`: 80
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 0.5
- `num_train_epochs`: 15
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `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`: 4
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Framework Versions
- Python: 3.11.0
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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