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": 1536,
+  "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_Dense/config.json

@@ -0,0 +1 @@
+{"in_features": 1536, "out_features": 1024, "bias": true, "activation_function": "torch.nn.modules.linear.Identity"}

2_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:464789c715d4c8c5c7073a7b2c0c857315e11ba106fb47a504451d5bebcfc304
+size 6295712

README.md

@@ -0,0 +1,770 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:5220
+- loss:CosineSimilarityLoss
+base_model: NovaSearch/stella_en_1.5B_v5
+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)."
+datasets:
+- Lauther/embeddings-train-semantic
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on NovaSearch/stella_en_1.5B_v5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NovaSearch/stella_en_1.5B_v5](https://huggingface.co/NovaSearch/stella_en_1.5B_v5) 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:** [NovaSearch/stella_en_1.5B_v5](https://huggingface.co/NovaSearch/stella_en_1.5B_v5) <!-- at revision f10d4793289fa0d0a36978d6ecd0a9eaa6781f06 -->
+- **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: Qwen2Model 
+  (1): Pooling({'word_embedding_dimension': 1536, '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): Dense({'in_features': 1536, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
+```
+
+## 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-stella_en_1.5B_v5-1e")
+# 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: 5 tokens</li><li>mean: 14.22 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 105 tokens</li><li>mean: 219.9 tokens</li><li>max: 447 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: 5 tokens</li><li>mean: 13.83 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 105 tokens</li><li>mean: 217.37 tokens</li><li>max: 447 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
+- `num_train_epochs`: 1
+- `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`: 1
+- `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   | 0.2817        | -               |
+| 0.0613 | 20   | 0.1694        | -               |
+| 0.0920 | 30   | 0.1173        | -               |
+| 0.1226 | 40   | 0.0953        | -               |
+| 0.1533 | 50   | 0.0959        | 0.0250          |
+| 0.1839 | 60   | 0.0948        | -               |
+| 0.2146 | 70   | 0.1095        | -               |
+| 0.2452 | 80   | 0.1269        | -               |
+| 0.2759 | 90   | 0.1023        | -               |
+| 0.3065 | 100  | 0.0775        | 0.0220          |
+| 0.3372 | 110  | 0.099         | -               |
+| 0.3678 | 120  | 0.077         | -               |
+| 0.3985 | 130  | 0.0837        | -               |
+| 0.4291 | 140  | 0.0677        | -               |
+| 0.4598 | 150  | 0.077         | 0.0198          |
+| 0.4904 | 160  | 0.0793        | -               |
+| 0.5211 | 170  | 0.0847        | -               |
+| 0.5517 | 180  | 0.0786        | -               |
+| 0.5824 | 190  | 0.0601        | -               |
+| 0.6130 | 200  | 0.0474        | 0.0166          |
+| 0.6437 | 210  | 0.0778        | -               |
+| 0.6743 | 220  | 0.0699        | -               |
+| 0.7050 | 230  | 0.066         | -               |
+| 0.7356 | 240  | 0.0741        | -               |
+| 0.7663 | 250  | 0.0576        | 0.0136          |
+| 0.7969 | 260  | 0.0418        | -               |
+| 0.8276 | 270  | 0.0648        | -               |
+| 0.8582 | 280  | 0.0566        | -               |
+| 0.8889 | 290  | 0.0625        | -               |
+| 0.9195 | 300  | 0.0487        | 0.0131          |
+| 0.9502 | 310  | 0.0533        | -               |
+| 0.9808 | 320  | 0.0405        | -               |
+
+
+### 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",
+}
+```
+
+<!--
+## 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.*
+-->

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config.json

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+  "_name_or_path": "NovaSearch/stella_en_1.5B_v5",
+  "architectures": [
+    "Qwen2Model"
+  ],
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+    "AutoModel": "NovaSearch/stella_en_1.5B_v5--modeling_qwen.Qwen2Model",
+    "AutoModelForCausalLM": "NovaSearch/stella_en_1.5B_v5--modeling_qwen.Qwen2ForCausalLM",
+    "AutoModelForSequenceClassification": "NovaSearch/stella_en_1.5B_v5--modeling_qwen.Qwen2ForSequenceClassification"
+  },
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+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
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+  "max_position_embeddings": 131072,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
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+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "use_cache": true,
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+  "vocab_size": 151646
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {
+    "s2p_query": "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: ",
+    "s2s_query": "Instruct: Retrieve semantically similar text.\nQuery: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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