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 +1,660 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:11165
+- loss:ContrastiveLoss
+base_model: intfloat/multilingual-e5-large-instruct
+widget:
+- source_sentence: PTE CRUZEIRO B
+  sentences:
+  - 'What is an Installation?
+
+    An Installation is a physical or operational site where measurement systems and
+    equipment are deployed. These locations can include processing plants, industrial
+    facilities, or other operational sites. Installations serve as key points for
+    monitoring and managing measurement processes. Examples include "Cexis" or "Processing
+    Plant XYZ."'
+  - '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 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."
+- source_sentence: ECOMP-VP-03116
+  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 Calibration Record?
+
+    A Calibration Record documents the calibration process of a specific equipment
+    tag, ensuring that its measurements remain accurate and reliable. Calibration
+    is a critical process in maintaining measurement precision and compliance with
+    standards.
+
+
+    Key Aspects of a Calibration Record:
+
+    - Calibration Date: The exact date when the calibration was performed, crucial
+    for tracking maintenance schedules.
+
+    - Certification Number: A unique identifier for the calibration certificate, providing
+    traceability and verification of compliance.
+
+    - Range Values: The minimum and maximum measurement values covered during the
+    calibration process.
+
+    - Calibration Status: Indicates whether the calibration was approved or saved
+    for further review.
+
+    - Associated Units: Specifies the measurement units used in calibration (e.g.,
+    °C, psi).
+
+    - Associated Equipment Tag ID: Links the calibration record to a specific equipment
+    tag, ensuring traceability of measurement instruments.
+
+    Calibration records play a fundamental role in quality assurance, helping maintain
+    measurement integrity and regulatory compliance.'
+  - '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.'
+- source_sentence: Resistencia
+  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 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: nitrogen composition
+  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 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.\
+    \  "
+  - '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.'
+- source_sentence: PTE SUZANO
+  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 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 Calibration Record?
+
+    A Calibration Record documents the calibration process of a specific equipment
+    tag, ensuring that its measurements remain accurate and reliable. Calibration
+    is a critical process in maintaining measurement precision and compliance with
+    standards.
+
+
+    Key Aspects of a Calibration Record:
+
+    - Calibration Date: The exact date when the calibration was performed, crucial
+    for tracking maintenance schedules.
+
+    - Certification Number: A unique identifier for the calibration certificate, providing
+    traceability and verification of compliance.
+
+    - Range Values: The minimum and maximum measurement values covered during the
+    calibration process.
+
+    - Calibration Status: Indicates whether the calibration was approved or saved
+    for further review.
+
+    - Associated Units: Specifies the measurement units used in calibration (e.g.,
+    °C, psi).
+
+    - Associated Equipment Tag ID: Links the calibration record to a specific equipment
+    tag, ensuring traceability of measurement instruments.
+
+    Calibration records play a fundamental role in quality assurance, helping maintain
+    measurement integrity and regulatory compliance.'
+datasets:
+- Lauther/d4-embeddings
+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](https://huggingface.co/datasets/Lauther/d4-embeddings) 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](https://huggingface.co/datasets/Lauther/d4-embeddings)
+<!-- - **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-v2.0")
+# Run inference
+sentences = [
+    'PTE SUZANO',
+    'What is a Calibration Record?\nA Calibration Record documents the calibration process of a specific equipment tag, ensuring that its measurements remain accurate and reliable. Calibration is a critical process in maintaining measurement precision and compliance with standards.\n\nKey Aspects of a Calibration Record:\n- Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules.\n- Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance.\n- Range Values: The minimum and maximum measurement values covered during the calibration process.\n- Calibration Status: Indicates whether the calibration was approved or saved for further review.\n- Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi).\n- Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments.\nCalibration records play a fundamental role in quality assurance, helping maintain measurement integrity and regulatory compliance.',
+    'What is a flow computer?\nA flow computer is a device used in measurement engineering. It collects analog and digital data from flow meters and other sensors.\n\nKey features of a flow computer:\n- It has a unique name, firmware version, and manufacturer information.\n- It is designed to record and process data such as temperature, pressure, and fluid volume (for gases or oils).',
+]
+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
+
+* Dataset: [d4-embeddings](https://huggingface.co/datasets/Lauther/d4-embeddings) at [09fb8a5](https://huggingface.co/datasets/Lauther/d4-embeddings/tree/09fb8a5fa7b222693e3a536b5ee892a1948b740b)
+* Size: 11,165 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                            | label                                           |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                           | string                                                                               | int                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 8.23 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 187.19 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>0: ~66.20%</li><li>1: ~33.80%</li></ul> |
+* Samples:
+  | sentence1                                     | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | label          |
+  |:----------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Ramal ESVOL - TEVOL (GASVOL 14")</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>0</code> |
+  | <code>Mol (%) CO</code>                       | <code>What is an Equipment Tag?<br>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.<br><br>Key Aspects of Equipment Tags:<br>- Equipment-Tag: A distinct label or identifier that uniquely marks the equipment in operation.<br>- Equipment ID: Links the tag to the corresponding equipment unit.<br>- Belonging Measurement System: Specifies which measurement system the tagged equipment is part of.<br>- Equipment Type Name: Classifies the equipment (e.g., transmitter, thermometer), aiding in organization and system integration.<br>The Equipment Tag is essential for tracking and managing operational equipment within a measurement system, ensuring proper identification, monitoring, and maintenance.</code> | <code>0</code> |
+  | <code>FQI-4715-1411</code>                    | <code>What is a flow computer?<br>A flow computer is a device used in measurement engineering. It collects analog and digital data from flow meters and other sensors.<br><br>Key features of a flow computer:<br>- It has a unique name, firmware version, and manufacturer information.<br>- It is designed to record and process data such as temperature, pressure, and fluid volume (for gases or oils).</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | <code>0</code> |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.5,
+      "size_average": true
+  }
+  ```
+
+### Evaluation Dataset
+
+#### d4-embeddings
+
+* Dataset: [d4-embeddings](https://huggingface.co/datasets/Lauther/d4-embeddings) at [09fb8a5](https://huggingface.co/datasets/Lauther/d4-embeddings/tree/09fb8a5fa7b222693e3a536b5ee892a1948b740b)
+* Size: 2,392 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                            | label                                           |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                           | string                                                                               | int                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 8.22 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 183.06 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>0: ~66.30%</li><li>1: ~33.70%</li></ul> |
+* Samples:
+  | sentence1                                       | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | label          |
+  |:------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>PTE UTE JUIZ DE FORA (IGREJINHA) B</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>1</code> |
+  | <code>measure type</code>                       | <code>What is a Calibration Record?<br>A Calibration Record documents the calibration process of a specific equipment tag, ensuring that its measurements remain accurate and reliable. Calibration is a critical process in maintaining measurement precision and compliance with standards.<br><br>Key Aspects of a Calibration Record:<br>- Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules.<br>- Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance.<br>- Range Values: The minimum and maximum measurement values covered during the calibration process.<br>- Calibration Status: Indicates whether the calibration was approved or saved for further review.<br>- Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi).<br>- Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments.<br>Calibration r...</code>                | <code>0</code> |
+  | <code>daily flow rate</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>1</code> |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.5,
+      "size_average": true
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 12
+- `per_device_eval_batch_size`: 12
+- `gradient_accumulation_steps`: 8
+- `weight_decay`: 0.01
+- `max_grad_norm`: 0.5
+- `num_train_epochs`: 5
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+
+#### 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`: 12
+- `per_device_eval_batch_size`: 12
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 8
+- `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`: 5
+- `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`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.4296 | 50   | 0.1345        | -               |
+| 0.8593 | 100  | 0.0512        | -               |
+| 1.2836 | 150  | 0.041         | 0.0051          |
+| 1.7132 | 200  | 0.0344        | -               |
+| 2.1375 | 250  | 0.0324        | -               |
+| 2.5671 | 300  | 0.0284        | 0.0038          |
+| 2.9968 | 350  | 0.0296        | -               |
+| 3.4211 | 400  | 0.0261        | -               |
+| 3.8507 | 450  | 0.0268        | 0.0035          |
+| 4.2750 | 500  | 0.0244        | -               |
+| 4.7046 | 550  | 0.0249        | -               |
+
+
+### 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",
+}
+```
+
+#### ContrastiveLoss
+```bibtex
+@inproceedings{hadsell2006dimensionality,
+    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
+    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
+    title={Dimensionality Reduction by Learning an Invariant Mapping},
+    year={2006},
+    volume={2},
+    number={},
+    pages={1735-1742},
+    doi={10.1109/CVPR.2006.100}
+}
+```
+
+<!--
+## 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

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+{
+  "_name_or_path": "/home/azureuser/projects/embeddings-train/.models/finetuned--d4-embeddings-v1.0-ContrastiveLoss/checkpoint-580",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
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+  "bos_token_id": 0,
+  "classifier_dropout": null,
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+  "hidden_act": "gelu",
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+  "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"
+}

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modules.json

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+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+  {
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+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+size 17082987

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+}