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 +1,1095 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:7552
+- loss:CoSENTLoss
+base_model: intfloat/multilingual-e5-large-instruct
+widget:
+- source_sentence: How are calibration points linked to equipment?
+  sentences:
+  - '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.'
+  - "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 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 is the primary key of the flow computer table?
+  sentences:
+  - '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 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.'
+  - "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."
+- source_sentence: Can you provide a sample query to test the retrieval of the uncertainty
+    result for the specified tag and date?
+  sentences:
+  - '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.'
+  - "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: How are the secondary equipment and measurement system related?
+  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.'
+  - '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.'
+  - '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: What is the table structure for secondary equipment?
+  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 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.'
+  - '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.'
+datasets:
+- Lauther/measuring-embeddings-v3
+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 [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) 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:**
+    - [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3)
+<!-- - **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/measuring-embeddings-v3-multilingual-e5-large-instruct-20e")
+# Run inference
+sentences = [
+    'What is the table structure for secondary equipment?',
+    '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 kind of data store an equipment?\nEquipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.\n\nData storage:\n- 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.\n- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.\n- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.\n\nAccessing the data:\n- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.\n- The readings are stored in a "variable values" table within the database.\n\nLinking variable names:\nIf 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.',
+]
+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
+
+#### measuring-embeddings-v3
+
+* Dataset: [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) at [1b3cbbe](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3/tree/1b3cbbeb70b63338110491cd3de2950fb40b4f87)
+* Size: 7,552 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: 9 tokens</li><li>mean: 15.96 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 255.56 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.22</li><li>max: 0.95</li></ul> |
+* Samples:
+  | sentence1                                                                                              | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | score             |
+  |:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
+  | <code>How can I combine the sub-query with the main query to fetch the last uncertainty report?</code> | <code>What do measurement equipment measure?<br>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.<br><br>Equipment classification:<br>- Primary meter: Assigned by default to equipments like orifice plates.<br>- Secondary meter: Assigned by default to equipments like transmitters.<br>- Tertiary meter: Used for other types of equipments.<br><br>Equipment types in the database:<br>The database includes a table listing all equipment types. Examples of equipment types are:<br>- Differential pressure transmitters<br>- RTDs (Resistance Temperature Detectors)<br>- Orifice plates<br>- Multivariable transmitters<br>- Ultrasonic meters<br><br>Meteorological checks for equipments:<br>Each equipment type is assigned a meteorological check, which can be either:<br>- Calibration: To ensure measurement accuracy.<br>- Inspection: To verify proper functioning.<br><br>Data storage in tables:<br>The database also includes a separate table for equipment classific...</code> | <code>0.1</code>  |
+  | <code>What is the column name for the calibration date in the calibration 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>0.1</code>  |
+  | <code>What is the name of the table that contains the flow computer tags?</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>0.05</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### measuring-embeddings-v3
+
+* Dataset: [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) at [1b3cbbe](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3/tree/1b3cbbeb70b63338110491cd3de2950fb40b4f87)
+* Size: 1,618 evaluation 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: 9 tokens</li><li>mean: 15.83 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 250.41 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 0.95</li></ul> |
+* Samples:
+  | sentence1                                                                                               | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | score            |
+  |:--------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>Identify any additional tables or columns that might be needed for the query.</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>0.2</code> |
+  | <code>What columns in these tables contain the measurement system tag and the flow computer 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>0.1</code> |
+  | <code>Identify the column that stores the calibration number.</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>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 7
+- `per_device_eval_batch_size`: 7
+- `gradient_accumulation_steps`: 4
+- `learning_rate`: 3e-05
+- `num_train_epochs`: 20
+- `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`: 7
+- `per_device_eval_batch_size`: 7
+- `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`: 3e-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`: 20
+- `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
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 9.5153  | 2560 | 6.782         | -               |
+| 9.5524  | 2570 | 7.3027        | -               |
+| 9.5894  | 2580 | 7.3348        | -               |
+| 9.6265  | 2590 | 7.7864        | -               |
+| 9.6636  | 2600 | 6.3552        | -               |
+| 9.7006  | 2610 | 7.151         | -               |
+| 9.7377  | 2620 | 6.1664        | -               |
+| 9.7748  | 2630 | 6.0398        | -               |
+| 9.8119  | 2640 | 7.0452        | -               |
+| 9.8489  | 2650 | 7.2457        | -               |
+| 9.8860  | 2660 | 6.7531        | -               |
+| 9.9231  | 2670 | 6.7149        | -               |
+| 9.9601  | 2680 | 6.4635        | -               |
+| 9.9972  | 2690 | 6.2237        | -               |
+| 10.0371 | 2700 | 6.1798        | 2.9939          |
+| 10.0741 | 2710 | 7.2224        | -               |
+| 10.1112 | 2720 | 6.5327        | -               |
+| 10.1483 | 2730 | 7.4686        | -               |
+| 10.1854 | 2740 | 6.1404        | -               |
+| 10.2224 | 2750 | 7.0005        | -               |
+| 10.2595 | 2760 | 5.7726        | -               |
+| 10.2966 | 2770 | 6.5327        | -               |
+| 10.3336 | 2780 | 7.5015        | -               |
+| 10.3707 | 2790 | 6.5526        | -               |
+| 10.4078 | 2800 | 6.2078        | -               |
+| 10.4449 | 2810 | 6.1           | -               |
+| 10.4819 | 2820 | 7.1027        | -               |
+| 10.5190 | 2830 | 8.639         | -               |
+| 10.5561 | 2840 | 6.9937        | -               |
+| 10.5931 | 2850 | 7.2734        | 2.8532          |
+| 10.6302 | 2860 | 7.6321        | -               |
+| 10.6673 | 2870 | 7.5788        | -               |
+| 10.7044 | 2880 | 6.7864        | -               |
+| 10.7414 | 2890 | 7.4237        | -               |
+| 10.7785 | 2900 | 6.9813        | -               |
+| 10.8156 | 2910 | 6.6884        | -               |
+| 10.8526 | 2920 | 6.7464        | -               |
+| 10.8897 | 2930 | 7.7989        | -               |
+| 10.9268 | 2940 | 7.3568        | -               |
+| 10.9639 | 2950 | 8.6706        | -               |
+| 11.0    | 2960 | 6.5687        | -               |
+| 11.0371 | 2970 | 5.8992        | -               |
+| 11.0741 | 2980 | 6.4543        | -               |
+| 11.1112 | 2990 | 6.1386        | -               |
+| 11.1483 | 3000 | 6.9047        | 2.9147          |
+| 11.1854 | 3010 | 7.405         | -               |
+| 11.2224 | 3020 | 7.5441        | -               |
+| 11.2595 | 3030 | 6.7524        | -               |
+| 11.2966 | 3040 | 7.698         | -               |
+| 11.3336 | 3050 | 7.6167        | -               |
+| 11.3707 | 3060 | 7.1516        | -               |
+| 11.4078 | 3070 | 6.7458        | -               |
+| 11.4449 | 3080 | 6.7608        | -               |
+| 11.4819 | 3090 | 7.1508        | -               |
+| 11.5190 | 3100 | 6.9155        | -               |
+| 11.5561 | 3110 | 6.6664        | -               |
+| 11.5931 | 3120 | 8.3841        | -               |
+| 11.6302 | 3130 | 7.1934        | -               |
+| 11.6673 | 3140 | 6.9681        | -               |
+| 11.7044 | 3150 | 7.2187        | 2.7509          |
+| 11.7414 | 3160 | 7.3155        | -               |
+| 11.7785 | 3170 | 7.3103        | -               |
+| 11.8156 | 3180 | 7.1959        | -               |
+| 11.8526 | 3190 | 6.8164        | -               |
+| 11.8897 | 3200 | 7.5836        | -               |
+| 11.9268 | 3210 | 5.2671        | -               |
+| 11.9639 | 3220 | 6.4929        | -               |
+| 12.0    | 3230 | 7.0892        | -               |
+| 12.0371 | 3240 | 7.0877        | -               |
+| 12.0741 | 3250 | 5.8302        | -               |
+| 12.1112 | 3260 | 5.6145        | -               |
+| 12.1483 | 3270 | 6.5808        | -               |
+| 12.1854 | 3280 | 6.6826        | -               |
+| 12.2224 | 3290 | 5.9819        | -               |
+| 12.2595 | 3300 | 6.68          | 3.0175          |
+| 12.2966 | 3310 | 6.1685        | -               |
+| 12.3336 | 3320 | 6.4473        | -               |
+| 12.3707 | 3330 | 6.3965        | -               |
+| 12.4078 | 3340 | 6.6278        | -               |
+| 12.4449 | 3350 | 5.4575        | -               |
+| 12.4819 | 3360 | 7.3019        | -               |
+| 12.5190 | 3370 | 7.4843        | -               |
+| 12.5561 | 3380 | 6.709         | -               |
+| 12.5931 | 3390 | 6.7168        | -               |
+| 12.6302 | 3400 | 7.0223        | -               |
+| 12.6673 | 3410 | 6.5089        | -               |
+| 12.7044 | 3420 | 6.5094        | -               |
+| 12.7414 | 3430 | 7.2317        | -               |
+| 12.7785 | 3440 | 6.6885        | -               |
+| 12.8156 | 3450 | 6.9693        | 2.8462          |
+| 12.8526 | 3460 | 6.8242        | -               |
+| 12.8897 | 3470 | 6.6899        | -               |
+| 12.9268 | 3480 | 6.9113        | -               |
+| 12.9639 | 3490 | 7.1903        | -               |
+| 13.0    | 3500 | 7.3286        | -               |
+| 13.0371 | 3510 | 6.5465        | -               |
+| 13.0741 | 3520 | 5.6804        | -               |
+| 13.1112 | 3530 | 5.6412        | -               |
+| 13.1483 | 3540 | 6.6161        | -               |
+| 13.1854 | 3550 | 5.761         | -               |
+| 13.2224 | 3560 | 5.5669        | -               |
+| 13.2595 | 3570 | 5.6184        | -               |
+| 13.2966 | 3580 | 6.2996        | -               |
+| 13.3336 | 3590 | 4.99          | -               |
+| 13.3707 | 3600 | 5.9974        | 3.2358          |
+| 13.4078 | 3610 | 5.6962        | -               |
+| 13.4449 | 3620 | 6.3662        | -               |
+| 13.4819 | 3630 | 7.0398        | -               |
+| 13.5190 | 3640 | 7.7358        | -               |
+| 13.5561 | 3650 | 7.9063        | -               |
+| 13.5931 | 3660 | 5.7823        | -               |
+| 13.6302 | 3670 | 6.9861        | -               |
+| 13.6673 | 3680 | 7.2855        | -               |
+| 13.7044 | 3690 | 5.6785        | -               |
+| 13.7414 | 3700 | 6.4071        | -               |
+| 13.7785 | 3710 | 6.4294        | -               |
+| 13.8156 | 3720 | 6.0842        | -               |
+| 13.8526 | 3730 | 5.9422        | -               |
+| 13.8897 | 3740 | 7.0778        | -               |
+| 13.9268 | 3750 | 8.1597        | 3.0093          |
+| 13.9639 | 3760 | 6.3154        | -               |
+| 14.0    | 3770 | 6.2416        | -               |
+| 14.0371 | 3780 | 5.9958        | -               |
+| 14.0741 | 3790 | 5.7032        | -               |
+| 14.1112 | 3800 | 4.9524        | -               |
+| 14.1483 | 3810 | 5.386         | -               |
+| 14.1854 | 3820 | 5.6353        | -               |
+| 14.2224 | 3830 | 5.0873        | -               |
+| 14.2595 | 3840 | 4.9255        | -               |
+| 14.2966 | 3850 | 5.1423        | -               |
+| 14.3336 | 3860 | 6.0775        | -               |
+| 14.3707 | 3870 | 4.5073        | -               |
+| 14.4078 | 3880 | 6.8347        | -               |
+| 14.4449 | 3890 | 6.5397        | -               |
+| 14.4819 | 3900 | 7.2143        | 3.3080          |
+| 14.5190 | 3910 | 6.1123        | -               |
+| 14.5561 | 3920 | 6.6048        | -               |
+| 14.5931 | 3930 | 6.3464        | -               |
+| 14.6302 | 3940 | 6.3618        | -               |
+| 14.6673 | 3950 | 6.5718        | -               |
+| 14.7044 | 3960 | 5.9785        | -               |
+| 14.7414 | 3970 | 6.5758        | -               |
+| 14.7785 | 3980 | 6.4308        | -               |
+| 14.8156 | 3990 | 6.0208        | -               |
+| 14.8526 | 4000 | 6.0303        | -               |
+| 14.8897 | 4010 | 6.6396        | -               |
+| 14.9268 | 4020 | 6.0184        | -               |
+| 14.9639 | 4030 | 6.6248        | -               |
+| 15.0    | 4040 | 6.4538        | -               |
+| 15.0371 | 4050 | 6.4742        | 3.1761          |
+| 15.0741 | 4060 | 5.5295        | -               |
+| 15.1112 | 4070 | 6.8753        | -               |
+| 15.1483 | 4080 | 5.639         | -               |
+| 15.1854 | 4090 | 5.6232        | -               |
+| 15.2224 | 4100 | 6.3026        | -               |
+| 15.2595 | 4110 | 6.1182        | -               |
+| 15.2966 | 4120 | 5.4736        | -               |
+| 15.3336 | 4130 | 6.2961        | -               |
+| 15.3707 | 4140 | 5.4742        | -               |
+| 15.4078 | 4150 | 5.4707        | -               |
+| 15.4449 | 4160 | 4.7272        | -               |
+| 15.4819 | 4170 | 6.1026        | -               |
+| 15.5190 | 4180 | 5.0468        | -               |
+| 15.5561 | 4190 | 5.5796        | -               |
+| 15.5931 | 4200 | 6.9046        | 3.1433          |
+| 15.6302 | 4210 | 5.6123        | -               |
+| 15.6673 | 4220 | 6.7246        | -               |
+| 15.7044 | 4230 | 5.7076        | -               |
+| 15.7414 | 4240 | 6.6772        | -               |
+| 15.7785 | 4250 | 5.6038        | -               |
+| 15.8156 | 4260 | 4.9544        | -               |
+| 15.8526 | 4270 | 5.0661        | -               |
+| 15.8897 | 4280 | 5.291         | -               |
+| 15.9268 | 4290 | 6.6652        | -               |
+| 15.9639 | 4300 | 5.6797        | -               |
+| 16.0    | 4310 | 5.1129        | -               |
+| 16.0371 | 4320 | 5.4445        | -               |
+| 16.0741 | 4330 | 4.8946        | -               |
+| 16.1112 | 4340 | 6.3929        | -               |
+| 16.1483 | 4350 | 6.0633        | 3.1426          |
+| 16.1854 | 4360 | 5.522         | -               |
+| 16.2224 | 4370 | 4.7067        | -               |
+| 16.2595 | 4380 | 5.4688        | -               |
+| 16.2966 | 4390 | 5.6009        | -               |
+| 16.3336 | 4400 | 5.1376        | -               |
+| 16.3707 | 4410 | 4.5196        | -               |
+| 16.4078 | 4420 | 5.5109        | -               |
+| 16.4449 | 4430 | 5.1888        | -               |
+| 16.4819 | 4440 | 6.0305        | -               |
+| 16.5190 | 4450 | 5.2791        | -               |
+| 16.5561 | 4460 | 5.4005        | -               |
+| 16.5931 | 4470 | 5.255         | -               |
+| 16.6302 | 4480 | 6.2026        | -               |
+| 16.6673 | 4490 | 6.6388        | -               |
+| 16.7044 | 4500 | 5.6138        | 3.2812          |
+| 16.7414 | 4510 | 4.7913        | -               |
+| 16.7785 | 4520 | 5.6675        | -               |
+| 16.8156 | 4530 | 5.8975        | -               |
+| 16.8526 | 4540 | 5.4597        | -               |
+| 16.8897 | 4550 | 5.137         | -               |
+| 16.9268 | 4560 | 4.5395        | -               |
+| 16.9639 | 4570 | 4.6304        | -               |
+| 17.0    | 4580 | 5.8098        | -               |
+| 17.0371 | 4590 | 4.0267        | -               |
+| 17.0741 | 4600 | 4.9194        | -               |
+| 17.1112 | 4610 | 4.1852        | -               |
+| 17.1483 | 4620 | 5.129         | -               |
+| 17.1854 | 4630 | 4.469         | -               |
+| 17.2224 | 4640 | 5.4298        | -               |
+| 17.2595 | 4650 | 4.5234        | 3.3447          |
+| 17.2966 | 4660 | 4.6856        | -               |
+| 17.3336 | 4670 | 6.3431        | -               |
+| 17.3707 | 4680 | 5.347         | -               |
+| 17.4078 | 4690 | 4.9223        | -               |
+| 17.4449 | 4700 | 5.4404        | -               |
+| 17.4819 | 4710 | 4.916         | -               |
+| 17.5190 | 4720 | 6.1744        | -               |
+| 17.5561 | 4730 | 4.8039        | -               |
+| 17.5931 | 4740 | 5.2276        | -               |
+| 17.6302 | 4750 | 4.4189        | -               |
+| 17.6673 | 4760 | 4.1434        | -               |
+| 17.7044 | 4770 | 4.9443        | -               |
+| 17.7414 | 4780 | 5.6975        | -               |
+| 17.7785 | 4790 | 4.6667        | -               |
+| 17.8156 | 4800 | 4.9876        | 3.2924          |
+| 17.8526 | 4810 | 4.4342        | -               |
+| 17.8897 | 4820 | 5.2595        | -               |
+| 17.9268 | 4830 | 5.6566        | -               |
+| 17.9639 | 4840 | 5.5452        | -               |
+| 18.0    | 4850 | 4.4986        | -               |
+| 18.0371 | 4860 | 4.8155        | -               |
+| 18.0741 | 4870 | 4.2278        | -               |
+| 18.1112 | 4880 | 5.4733        | -               |
+| 18.1483 | 4890 | 4.2394        | -               |
+| 18.1854 | 4900 | 5.1253        | -               |
+| 18.2224 | 4910 | 4.7498        | -               |
+| 18.2595 | 4920 | 4.9775        | -               |
+| 18.2966 | 4930 | 4.797         | -               |
+| 18.3336 | 4940 | 4.5694        | -               |
+| 18.3707 | 4950 | 4.6192        | 3.6615          |
+| 18.4078 | 4960 | 5.8114        | -               |
+| 18.4449 | 4970 | 4.8035        | -               |
+| 18.4819 | 4980 | 4.6944        | -               |
+| 18.5190 | 4990 | 4.8664        | -               |
+| 18.5561 | 5000 | 4.6916        | -               |
+| 18.5931 | 5010 | 4.3352        | -               |
+| 18.6302 | 5020 | 5.9779        | -               |
+| 18.6673 | 5030 | 4.7813        | -               |
+| 18.7044 | 5040 | 4.632         | -               |
+| 18.7414 | 5050 | 4.7411        | -               |
+| 18.7785 | 5060 | 3.6489        | -               |
+| 18.8156 | 5070 | 4.5373        | -               |
+| 18.8526 | 5080 | 5.6129        | -               |
+| 18.8897 | 5090 | 4.8933        | -               |
+| 18.9268 | 5100 | 4.27          | 3.6957          |
+| 18.9639 | 5110 | 4.5338        | -               |
+| 19.0    | 5120 | 5.5175        | -               |
+| 19.0371 | 5130 | 5.0835        | -               |
+| 19.0741 | 5140 | 4.6826        | -               |
+| 19.1112 | 5150 | 4.5391        | -               |
+| 19.1483 | 5160 | 5.3723        | -               |
+| 19.1854 | 5170 | 4.8095        | -               |
+| 19.2224 | 5180 | 4.7402        | -               |
+| 19.2595 | 5190 | 4.0488        | -               |
+| 19.2966 | 5200 | 3.6424        | -               |
+| 19.3336 | 5210 | 4.2256        | -               |
+| 19.3707 | 5220 | 4.4607        | -               |
+| 19.4078 | 5230 | 3.5702        | -               |
+| 19.4449 | 5240 | 4.3062        | -               |
+| 19.4819 | 5250 | 4.2919        | 3.6594          |
+| 19.5190 | 5260 | 4.6985        | -               |
+| 19.5561 | 5270 | 4.6907        | -               |
+| 19.5931 | 5280 | 4.3865        | -               |
+| 19.6302 | 5290 | 3.9818        | -               |
+| 19.6673 | 5300 | 4.3166        | -               |
+| 19.7044 | 5310 | 4.9131        | -               |
+| 19.7414 | 5320 | 4.7641        | -               |
+| 19.7785 | 5330 | 5.419         | -               |
+| 19.8156 | 5340 | 4.068         | -               |
+| 19.8526 | 5350 | 4.1094        | -               |
+| 19.8897 | 5360 | 5.2279        | -               |
+| 19.9268 | 5370 | 4.4818        | -               |
+| 19.9639 | 5380 | 4.3103        | -               |
+
+</details>
+
+### 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "intfloat/multilingual-e5-large-instruct",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0aca6f2662c641a9490874428fd31456882c4bb2907af32f94152fb87b7038c1
+size 2239607176

modules.json

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

sentence_bert_config.json

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

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}