Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,648 @@
+---
+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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "/home/azureuser/projects/embeddings-train/.models/finetuned--d4-embeddings-v1.0-TripletLoss",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 2239607176

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
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+  },
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+    "lstrip": false,
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+}

tokenizer.json

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+size 17082987

tokenizer_config.json

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+}