sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:5220 loss:CosineSimilarityLoss dataset:Lauther/embeddings-train-semantic arxiv:1908.10084 base_model:intfloat/multilingual-e5-large-instruct base_model:finetune:intfloat/multilingual-e5-large-instruct autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: intfloat/multilingual-e5-large-instruct
datasets:
- Lauther/embeddings-train-semantic
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5220
- loss:CosineSimilarityLoss
widget:
- source_sentence: Identify the column that stores the uncertainty value.
  sentences:
  - "What is measuring equipment?\nMeasuring equipment refers to the devices that\
    \ make up a measurement system. Each piece of equipment has:\n- A unique serial\
    \ number for identification.\n- A technical name, such as transmitter, plate,\
    \ thermometer, etc.\n\nHow is equipment assigned to a measurement system?\nWhen\
    \ equipment is assigned to a measurement system, it is given a unique identifier\
    \ called an \"\"Equipment Tag.\"\"\n- If a piece of equipment has a tag, it is\
    \ considered in use in a measurement system.\n- If it does not have a tag, it\
    \ is considered spare or unused\n\nEquipment assignment based on technology:\n\
    The type of equipment assigned to a measurement system depends on the technology\
    \ used, for example:\n1. Differential technology (for gas measurement):\n    -\
    \ Static pressure transmitters\n    - Differential pressure transmitters\n   \
    \ - Temperature transmitters\n    - RTDs (thermometers)\n    - Orifice plates\n\
    \    - Straight stretch\n\n2. Linear technology (for gas measurement):\n    -\
    \ Temperature transmitters\n    - RTDs\n    - Static pressure transmitters\n \
    \   - Ultrasonic meters\n\nRelationship between equipment and measurement systems:\n\
    - A measurement system can have multiple pieces of equipment.\n- However, a piece\
    \ of equipment can only be assigned to one measurement system.\n\nDatabase management:\n\
    - The database includes a special table to manage the list of equipment assigned\
    \ to measurement systems.\n- When a user refers to an \"\"Equipment Tag\"\", they\
    \ are searching for operational equipment assigned to a measurement system.\n\
    - If a user is looking for spare or unused equipment, they are searching for equipment\
    \ not listed in the tagged equipment table.\n- Commonly used when user refers\
    \ directly to an \"\"Equipment Tag\""
  - 'What is equipment calibration?

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


    Purpose of calibration:

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


    Calibration cycles:

    There are two main calibration cycles:

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

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


    Calibration uncertainty:

    - Uncertainty is included in the results of a calibration.

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

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


    Data storage:

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

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

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


    Accessing the data:

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

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


    Linking variable names:

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

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


    Data storage:

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

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

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


    Accessing the data:

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

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


    Linking variable names:

    If the user needs to know the name of a variable, they must link the data to another
    table that stores information about the types of variables.'
  - "How does a flow computer generate and store reports?\nA flow computer generates\
    \ daily or hourly reports to provide users with operational data. These reports\
    \ are stored in the flow computer's memory in an organized format.\n\nReport structure:\n\
    - Each report includes:\n- Date and time of the data recording.\n- Data recorded\
    \ from flow computers.\n\nData storage in tables:\nThe reports are saved in two\
    \ tables:\n1. Main table (Index):\n    - Stores the date, time, and flow computer\
    \ identifier.\n2. Detail table:\n    - Stores the measured values associated with\
    \ the report.\n\nConnection to the Modbus table:\nThe flow computer's reports\
    \ are linked to a Modbus table. This table contains the names corresponding to\
    \ each value in the reports, making it easier to interpret the data."
  - 'What is a flow computer?

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


    Key features of a flow computer:

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

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


    Main function:

    The flow computer sends the collected data to a measurement system. This allows
    measurement engineers to analyze the data and perform their tasks effectively.'
- source_sentence: What tables store measurement system data?
  sentences:
  - "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
    \ and reliability of results obtained from equipment or measurement systems. It\
    \ quantifies the potential error or margin of error in measurements.\n\nTypes\
    \ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
    \ magnitudes (variables):\n    - Refers to the uncertainty of specific variables,\
    \ such as temperature or pressure.\n    - It is calculated after calibrating a\
    \ device or obtained from the equipment manufacturer's manual.\n    - This uncertainty\
    \ serves as a starting point for further calculations related to the equipment.\n\
    \n2. Uncertainty of the measurement system:\n    - Refers to the uncertainty calculated\
    \ for the overall flow measurement.\n    - It depends on the uncertainties of\
    \ the individual variables (magnitudes) and represents the combined margin of\
    \ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
    \ (variables) are the foundation for calculating the uncertainty of the measurement\
    \ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
    \ types of uncertainty:\n    - **Uncertainty of magnitudes/variables**: Specific\
    \ to individual variables (e.g., temperature, pressure).\n    - **Uncertainty\
    \ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
    \ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
    \ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n    - Stores\
    \ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
    \n2. Uncertainty of the measurement system:\n    - Stores the uncertainty values\
    \ for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n\
    - To find the uncertainty of the measurement system, join the measurement systems\
    \ table with the uncertainty of the measurement system table.\n- To find the uncertainty\
    \ of a specific variable (magnitude), join the measurement systems table with\
    \ the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not\
    \ confuse the two types of uncertainty:\n- If the user requests the uncertainty\
    \ of the measurement system, use the first join (measurement systems table + uncertainty\
    \ of the measurement system table).\n- If the user requests the uncertainty of\
    \ a specific variable (magnitude) in a report, use the second join (measurement\
    \ systems table + uncertainty of magnitudes table)."
  - "What is a measurement system?\nA measurement system, also referred to as a delivery\
    \ point, measurement point, or reception point, is used to measure and monitor\
    \ fluids in industrial processes.\n\nKey characteristics of a measurement system:\n\
    1. Measurement technology:\n    - Differential: Used for precise measurements.\n\
    \    - Linear: Used for straightforward measurements.\n\n2. System identifier\
    \ (TAG):\n    - A unique identifier for the system.\n\n3. Fluid type:\n    - The\
    \ system can measure gases, oils, condensates, water, steam, or other fluids.\n\
    4. System type:\n    - Specifies the category or purpose of the system.\n\nMeasurement\
    \ technology by fluid type:\n- Gas measurement systems: Use both linear and differential\
    \ measurement technologies.\n- Oil measurement systems: Do not use linear or differential\
    \ technologies; they are programmed differently.\"\n\n\nClassification of measurement\
    \ systems:\nMeasurement systems are classified based on the stage of the process\
    \ in which they are used. Common classifications include:\n- Fiscal\n- Operational\n\
    - Appropriation\n- Custody\n- Production Poços"
  - 'What do measurement equipment measure?

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


    Equipment classification:

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

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

    - Tertiary meter: Used for other types of equipments.


    Equipment types in the database:

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

    - Differential pressure transmitters

    - RTDs (Resistance Temperature Detectors)

    - Orifice plates

    - Multivariable transmitters

    - Ultrasonic meters


    Meteorological checks for equipments:

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

    - Calibration: To ensure measurement accuracy.

    - Inspection: To verify proper functioning.


    Data storage in tables:

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

    - Primary meter

    - Secondary meter

    - Tertiary meter

    So, an equipment has equipment types and this types has classifications.'
- source_sentence: What is the table structure for equipment types?
  sentences:
  - "How does a flow computer generate and store reports?\nA flow computer generates\
    \ daily or hourly reports to provide users with operational data. These reports\
    \ are stored in the flow computer's memory in an organized format.\n\nReport structure:\n\
    - Each report includes:\n- Date and time of the data recording.\n- Data recorded\
    \ from flow computers.\n\nData storage in tables:\nThe reports are saved in two\
    \ tables:\n1. Main table (Index):\n    - Stores the date, time, and flow computer\
    \ identifier.\n2. Detail table:\n    - Stores the measured values associated with\
    \ the report.\n\nConnection to the Modbus table:\nThe flow computer's reports\
    \ are linked to a Modbus table. This table contains the names corresponding to\
    \ each value in the reports, making it easier to interpret the data."
  - "What is measuring equipment?\nMeasuring equipment refers to the devices that\
    \ make up a measurement system. Each piece of equipment has:\n- A unique serial\
    \ number for identification.\n- A technical name, such as transmitter, plate,\
    \ thermometer, etc.\n\nHow is equipment assigned to a measurement system?\nWhen\
    \ equipment is assigned to a measurement system, it is given a unique identifier\
    \ called an \"\"Equipment Tag.\"\"\n- If a piece of equipment has a tag, it is\
    \ considered in use in a measurement system.\n- If it does not have a tag, it\
    \ is considered spare or unused\n\nEquipment assignment based on technology:\n\
    The type of equipment assigned to a measurement system depends on the technology\
    \ used, for example:\n1. Differential technology (for gas measurement):\n    -\
    \ Static pressure transmitters\n    - Differential pressure transmitters\n   \
    \ - Temperature transmitters\n    - RTDs (thermometers)\n    - Orifice plates\n\
    \    - Straight stretch\n\n2. Linear technology (for gas measurement):\n    -\
    \ Temperature transmitters\n    - RTDs\n    - Static pressure transmitters\n \
    \   - Ultrasonic meters\n\nRelationship between equipment and measurement systems:\n\
    - A measurement system can have multiple pieces of equipment.\n- However, a piece\
    \ of equipment can only be assigned to one measurement system.\n\nDatabase management:\n\
    - The database includes a special table to manage the list of equipment assigned\
    \ to measurement systems.\n- When a user refers to an \"\"Equipment Tag\"\", they\
    \ are searching for operational equipment assigned to a measurement system.\n\
    - If a user is looking for spare or unused equipment, they are searching for equipment\
    \ not listed in the tagged equipment table.\n- Commonly used when user refers\
    \ directly to an \"\"Equipment Tag\""
  - "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
    \ and reliability of results obtained from equipment or measurement systems. It\
    \ quantifies the potential error or margin of error in measurements.\n\nTypes\
    \ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
    \ magnitudes (variables):\n    - Refers to the uncertainty of specific variables,\
    \ such as temperature or pressure.\n    - It is calculated after calibrating a\
    \ device or obtained from the equipment manufacturer's manual.\n    - This uncertainty\
    \ serves as a starting point for further calculations related to the equipment.\n\
    \n2. Uncertainty of the measurement system:\n    - Refers to the uncertainty calculated\
    \ for the overall flow measurement.\n    - It depends on the uncertainties of\
    \ the individual variables (magnitudes) and represents the combined margin of\
    \ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
    \ (variables) are the foundation for calculating the uncertainty of the measurement\
    \ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
    \ types of uncertainty:\n    - **Uncertainty of magnitudes/variables**: Specific\
    \ to individual variables (e.g., temperature, pressure).\n    - **Uncertainty\
    \ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
    \ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
    \ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n    - Stores\
    \ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
    \n2. Uncertainty of the measurement system:\n    - Stores the uncertainty values\
    \ for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n\
    - To find the uncertainty of the measurement system, join the measurement systems\
    \ table with the uncertainty of the measurement system table.\n- To find the uncertainty\
    \ of a specific variable (magnitude), join the measurement systems table with\
    \ the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not\
    \ confuse the two types of uncertainty:\n- If the user requests the uncertainty\
    \ of the measurement system, use the first join (measurement systems table + uncertainty\
    \ of the measurement system table).\n- If the user requests the uncertainty of\
    \ a specific variable (magnitude) in a report, use the second join (measurement\
    \ systems table + uncertainty of magnitudes table)."
- source_sentence: What columns store the uncertainty values?
  sentences:
  - "What is a measurement system?\nA measurement system, also referred to as a delivery\
    \ point, measurement point, or reception point, is used to measure and monitor\
    \ fluids in industrial processes.\n\nKey characteristics of a measurement system:\n\
    1. Measurement technology:\n    - Differential: Used for precise measurements.\n\
    \    - Linear: Used for straightforward measurements.\n\n2. System identifier\
    \ (TAG):\n    - A unique identifier for the system.\n\n3. Fluid type:\n    - The\
    \ system can measure gases, oils, condensates, water, steam, or other fluids.\n\
    4. System type:\n    - Specifies the category or purpose of the system.\n\nMeasurement\
    \ technology by fluid type:\n- Gas measurement systems: Use both linear and differential\
    \ measurement technologies.\n- Oil measurement systems: Do not use linear or differential\
    \ technologies; they are programmed differently.\"\n\n\nClassification of measurement\
    \ systems:\nMeasurement systems are classified based on the stage of the process\
    \ in which they are used. Common classifications include:\n- Fiscal\n- Operational\n\
    - Appropriation\n- Custody\n- Production Poços"
  - 'How are flow computers and measurement systems related?

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


    Database terminology:

    In the database, this relationship is referred to as:

    - Meter streams

    - Meter runs

    - Sections


    Storage of the relationship:

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


    User context:

    When a user refers to a "meter stream," they are indicating that they are searching
    for a measurement system assigned to a specific flow computer.'
  - "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
    \ and reliability of results obtained from equipment or measurement systems. It\
    \ quantifies the potential error or margin of error in measurements.\n\nTypes\
    \ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
    \ magnitudes (variables):\n    - Refers to the uncertainty of specific variables,\
    \ such as temperature or pressure.\n    - It is calculated after calibrating a\
    \ device or obtained from the equipment manufacturer's manual.\n    - This uncertainty\
    \ serves as a starting point for further calculations related to the equipment.\n\
    \n2. Uncertainty of the measurement system:\n    - Refers to the uncertainty calculated\
    \ for the overall flow measurement.\n    - It depends on the uncertainties of\
    \ the individual variables (magnitudes) and represents the combined margin of\
    \ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
    \ (variables) are the foundation for calculating the uncertainty of the measurement\
    \ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
    \ types of uncertainty:\n    - **Uncertainty of magnitudes/variables**: Specific\
    \ to individual variables (e.g., temperature, pressure).\n    - **Uncertainty\
    \ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
    \ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
    \ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n    - Stores\
    \ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
    \n2. Uncertainty of the measurement system:\n    - Stores the uncertainty values\
    \ for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n\
    - To find the uncertainty of the measurement system, join the measurement systems\
    \ table with the uncertainty of the measurement system table.\n- To find the uncertainty\
    \ of a specific variable (magnitude), join the measurement systems table with\
    \ the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not\
    \ confuse the two types of uncertainty:\n- If the user requests the uncertainty\
    \ of the measurement system, use the first join (measurement systems table + uncertainty\
    \ of the measurement system table).\n- If the user requests the uncertainty of\
    \ a specific variable (magnitude) in a report, use the second join (measurement\
    \ systems table + uncertainty of magnitudes table)."
---

# 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 [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision c9e87c786ffac96aeaeb42863276930883923ecb -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic)
<!-- - **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/emb-multilingual-e5-large-instruct-3e")
# Run inference
sentences = [
    'What columns store the uncertainty values?',
    'How are flow computers and measurement systems related?\nFlow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.\n\nDatabase terminology:\nIn the database, this relationship is referred to as:\n- Meter streams\n- Meter runs\n- Sections\n\nStorage of the relationship:\nThe relationship between a flow computer and its assigned measurement system is stored in a special table.\n\nUser context:\nWhen a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.',
    'What is uncertainty?\nUncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.\n\nTypes of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of magnitudes (variables):\n    - Refers to the uncertainty of specific variables, such as temperature or pressure.\n    - It is calculated after calibrating a device or obtained from the equipment manufacturer\'s manual.\n    - This uncertainty serves as a starting point for further calculations related to the equipment.\n\n2. Uncertainty of the measurement system:\n    - Refers to the uncertainty calculated for the overall flow measurement.\n    - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of the measurement system. Think of them as the "building blocks."\n- Do not confuse the two types of uncertainty:\n    - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).\n    - **Uncertainty of the measurement system**: Specific to the overall flow measurement.\n\nDatabase storage for uncertainties:\nIn the database, uncertainty calculations are stored in two separate tables:\n1. Uncertainty of magnitudes (variables):\n    - Stores the uncertainty values for specific variables (e.g., temperature, pressure).\n\n2. Uncertainty of the measurement system:\n    - Stores the uncertainty values for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n- To find the uncertainty of the measurement system, join the measurement systems table with the uncertainty of the measurement system table.\n- To find the uncertainty of a specific variable (magnitude), join the measurement systems table with the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not confuse the two types of uncertainty:\n- If the user requests the uncertainty of the measurement system, use the first join (measurement systems table + uncertainty of the measurement system table).\n- If the user requests the uncertainty of a specific variable (magnitude) in a report, use the second join (measurement systems table + uncertainty of magnitudes table).',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### 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

#### embeddings-train-semantic

* Dataset: [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) at [ce90f53](https://huggingface.co/datasets/Lauther/embeddings-train-semantic/tree/ce90f531bc39037053d223b27868ad178852f330)
* Size: 5,220 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                            | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | float                                                          |
  | details | <ul><li>min: 8 tokens</li><li>mean: 18.3 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 257.3 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                 | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | score                            |
  |:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
  | <code>What is the data type of differential pressure in the measurement system?</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>0.15000000000000002</code> |
  | <code>What is the structure of the &&&equipment_data&&& table?</code>                     | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.</code>                                                                                                                                                                                                                                                                                                                                                                                                                       | <code>0.35000000000000003</code> |
  | <code>Find the columns in the flow computer table that identify the flow computer.</code> | <code>What kind of data store an equipment?<br>Equipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.<br><br>Data storage:<br>- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.<br>- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.<br>- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.<br><br>Accessing the data:<br>- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.<br>- The readings are stored in a "variable values" table within the database.<br><br>Linking variable names:<br>If the user needs to kno...</code>       | <code>0.1</code>                 |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### embeddings-train-semantic

* Dataset: [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) at [ce90f53](https://huggingface.co/datasets/Lauther/embeddings-train-semantic/tree/ce90f531bc39037053d223b27868ad178852f330)
* Size: 652 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 652 samples:
  |         | sentence1                                                                         | sentence2                                                                             | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                                | float                                                          |
  | details | <ul><li>min: 8 tokens</li><li>mean: 17.8 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 253.84 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.24</li><li>max: 0.9</li></ul> |
* Samples:
  | sentence1                                                                      | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | score                            |
  |:-------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
  | <code>How can I filter uncertainty reports by equipment tag?</code>            | <code>How does a flow computer generate and store reports?<br>A flow computer generates daily or hourly reports to provide users with operational data. These reports are stored in the flow computer's memory in an organized format.<br><br>Report structure:<br>- Each report includes:<br>- Date and time of the data recording.<br>- Data recorded from flow computers.<br><br>Data storage in tables:<br>The reports are saved in two tables:<br>1. Main table (Index):<br>    - Stores the date, time, and flow computer identifier.<br>2. Detail table:<br>    - Stores the measured values associated with the report.<br><br>Connection to the Modbus table:<br>The flow computer's reports are linked to a Modbus table. This table contains the names corresponding to each value in the reports, making it easier to interpret the data.</code>                                                                                                                                                                                                                                          | <code>0.09999999999999999</code> |
  | <code>What is the purpose of the flow_data table?</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>0.15000000000000002</code> |
  | <code>What is the column name for the report date in the Reports table?</code> | <code>What is equipment calibration?<br>Calibration is a metrological verification process used to ensure the accuracy of measurement equipment. It is performed periodically, based on intervals set by the company or a regulatory body.<br><br>Purpose of calibration:<br>The calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data.<br><br>Calibration cycles:<br>There are two main calibration cycles:<br>1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented.<br>2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements.<br><br>Calibration uncertainty:<br>- Uncertainty is included in the results of a calibration.<br>- Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or ...</code>       | <code>0.1</code>                 |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 4
- `learning_rate`: 2e-05
- `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`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0307 | 10   | 1.5374        | -               |
| 0.0613 | 20   | 1.0251        | -               |
| 0.0920 | 30   | 0.361         | -               |
| 0.1226 | 40   | 0.1819        | -               |
| 0.1533 | 50   | 0.186         | -               |
| 0.1839 | 60   | 0.1697        | -               |
| 0.2146 | 70   | 0.1437        | -               |
| 0.2452 | 80   | 0.172         | -               |
| 0.2759 | 90   | 0.1199        | -               |
| 0.3065 | 100  | 0.1278        | -               |
| 0.3372 | 110  | 0.1037        | -               |
| 0.3678 | 120  | 0.1156        | -               |
| 0.3985 | 130  | 0.0971        | -               |
| 0.4291 | 140  | 0.0911        | -               |
| 0.4598 | 150  | 0.1158        | 0.0249          |
| 0.4904 | 160  | 0.0906        | -               |
| 0.5211 | 170  | 0.106         | -               |
| 0.5517 | 180  | 0.0921        | -               |
| 0.5824 | 190  | 0.0748        | -               |
| 0.6130 | 200  | 0.0741        | -               |
| 0.6437 | 210  | 0.0894        | -               |
| 0.6743 | 220  | 0.0815        | -               |
| 0.7050 | 230  | 0.0771        | -               |
| 0.7356 | 240  | 0.1156        | -               |
| 0.7663 | 250  | 0.0857        | -               |
| 0.7969 | 260  | 0.0566        | -               |
| 0.8276 | 270  | 0.0716        | -               |
| 0.8582 | 280  | 0.0662        | -               |
| 0.8889 | 290  | 0.0963        | -               |
| 0.9195 | 300  | 0.0678        | 0.0212          |
| 0.9502 | 310  | 0.077         | -               |
| 0.9808 | 320  | 0.0642        | -               |
| 1.0092 | 330  | 0.0725        | -               |
| 1.0398 | 340  | 0.0701        | -               |
| 1.0705 | 350  | 0.0549        | -               |
| 1.1011 | 360  | 0.0699        | -               |
| 1.1318 | 370  | 0.0714        | -               |
| 1.1625 | 380  | 0.0745        | -               |
| 1.1931 | 390  | 0.0754        | -               |
| 1.2238 | 400  | 0.0486        | -               |
| 1.2544 | 410  | 0.047         | -               |
| 1.2851 | 420  | 0.076         | -               |
| 1.3157 | 430  | 0.0689        | -               |
| 1.3464 | 440  | 0.0629        | -               |
| 1.3770 | 450  | 0.0657        | 0.0178          |
| 1.4077 | 460  | 0.0622        | -               |
| 1.4383 | 470  | 0.0657        | -               |
| 1.4690 | 480  | 0.0498        | -               |
| 1.4996 | 490  | 0.0653        | -               |
| 1.5303 | 500  | 0.0715        | -               |
| 1.5609 | 510  | 0.0615        | -               |
| 1.5916 | 520  | 0.0441        | -               |
| 1.6222 | 530  | 0.0566        | -               |
| 1.6529 | 540  | 0.0524        | -               |
| 1.6835 | 550  | 0.0423        | -               |
| 1.7142 | 560  | 0.0441        | -               |
| 1.7448 | 570  | 0.0553        | -               |
| 1.7755 | 580  | 0.0572        | -               |
| 1.8061 | 590  | 0.0686        | -               |
| 1.8368 | 600  | 0.06          | 0.0146          |
| 1.8674 | 610  | 0.0562        | -               |
| 1.8981 | 620  | 0.0517        | -               |
| 1.9287 | 630  | 0.0498        | -               |
| 1.9594 | 640  | 0.0424        | -               |
| 1.9900 | 650  | 0.0729        | -               |
| 2.0184 | 660  | 0.0347        | -               |
| 2.0490 | 670  | 0.06          | -               |
| 2.0797 | 680  | 0.0441        | -               |
| 2.1103 | 690  | 0.0409        | -               |
| 2.1410 | 700  | 0.0416        | -               |
| 2.1716 | 710  | 0.0345        | -               |
| 2.2023 | 720  | 0.024         | -               |
| 2.2330 | 730  | 0.0458        | -               |
| 2.2636 | 740  | 0.0465        | -               |
| 2.2943 | 750  | 0.0494        | 0.0132          |
| 2.3249 | 760  | 0.0388        | -               |
| 2.3556 | 770  | 0.0363        | -               |
| 2.3862 | 780  | 0.0441        | -               |
| 2.4169 | 790  | 0.0378        | -               |
| 2.4475 | 800  | 0.0484        | -               |
| 2.4782 | 810  | 0.051         | -               |
| 2.5088 | 820  | 0.0464        | -               |
| 2.5395 | 830  | 0.036         | -               |
| 2.5701 | 840  | 0.0423        | -               |
| 2.6008 | 850  | 0.0278        | -               |
| 2.6314 | 860  | 0.0474        | -               |
| 2.6621 | 870  | 0.0357        | -               |
| 2.6927 | 880  | 0.0386        | -               |
| 2.7234 | 890  | 0.0334        | -               |
| 2.7540 | 900  | 0.0199        | 0.0127          |
| 2.7847 | 910  | 0.0381        | -               |
| 2.8153 | 920  | 0.0415        | -               |
| 2.8460 | 930  | 0.0274        | -               |
| 2.8766 | 940  | 0.0353        | -               |
| 2.9073 | 950  | 0.0423        | -               |
| 2.9379 | 960  | 0.0267        | -               |
| 2.9686 | 970  | 0.042         | -               |


### Framework Versions
- Python: 3.11.0
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```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",
}
```

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