|
--- |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:5220 |
|
- loss:CosineSimilarityLoss |
|
base_model: intfloat/multilingual-e5-large-instruct |
|
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 intfloat/multilingual-e5-large-instruct |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision c9e87c786ffac96aeaeb42863276930883923ecb --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 1024 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("Lauther/emb-multilingual-e5-large-instruct-3e") |
|
# Run inference |
|
sentences = [ |
|
'What columns store the uncertainty values?', |
|
'How are flow computers and measurement systems related?\nFlow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.\n\nDatabase terminology:\nIn the database, this relationship is referred to as:\n- Meter streams\n- Meter runs\n- Sections\n\nStorage of the relationship:\nThe relationship between a flow computer and its assigned measurement system is stored in a special table.\n\nUser context:\nWhen a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.', |
|
'What is uncertainty?\nUncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.\n\nTypes of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of magnitudes (variables):\n - Refers to the uncertainty of specific variables, such as temperature or pressure.\n - It is calculated after calibrating a device or obtained from the equipment manufacturer\'s manual.\n - This uncertainty serves as a starting point for further calculations related to the equipment.\n\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated for the overall flow measurement.\n - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of the measurement system. Think of them as the "building blocks."\n- Do not confuse the two types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).\n - **Uncertainty of the measurement system**: Specific to the overall flow measurement.\n\nDatabase storage for uncertainties:\nIn the database, uncertainty calculations are stored in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores the uncertainty values for specific variables (e.g., temperature, pressure).\n\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n- To find the uncertainty of the measurement system, join the measurement systems table with the uncertainty of the measurement system table.\n- To find the uncertainty of a specific variable (magnitude), join the measurement systems table with the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not confuse the two types of uncertainty:\n- If the user requests the uncertainty of the measurement system, use the first join (measurement systems table + uncertainty of the measurement system table).\n- If the user requests the uncertainty of a specific variable (magnitude) in a report, use the second join (measurement systems table + uncertainty of magnitudes table).', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 1024] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### embeddings-train-semantic |
|
|
|
* Dataset: [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) at [ce90f53](https://huggingface.co/datasets/Lauther/embeddings-train-semantic/tree/ce90f531bc39037053d223b27868ad178852f330) |
|
* Size: 5,220 training samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | score | |
|
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 18.3 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 257.3 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 1.0</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | score | |
|
|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| |
|
| <code>What is the data type of differential pressure in the measurement system?</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>0.15000000000000002</code> | |
|
| <code>What is the structure of the &&&equipment_data&&& table?</code> | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.</code> | <code>0.35000000000000003</code> | |
|
| <code>Find the columns in the flow computer table that identify the flow computer.</code> | <code>What kind of data store an equipment?<br>Equipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.<br><br>Data storage:<br>- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.<br>- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.<br>- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.<br><br>Accessing the data:<br>- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.<br>- The readings are stored in a "variable values" table within the database.<br><br>Linking variable names:<br>If the user needs to kno...</code> | <code>0.1</code> | |
|
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
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#### embeddings-train-semantic |
|
|
|
* Dataset: [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) at [ce90f53](https://huggingface.co/datasets/Lauther/embeddings-train-semantic/tree/ce90f531bc39037053d223b27868ad178852f330) |
|
* Size: 652 evaluation samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
|
* Approximate statistics based on the first 652 samples: |
|
| | sentence1 | sentence2 | score | |
|
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 17.8 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 253.84 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.24</li><li>max: 0.9</li></ul> | |
|
* Samples: |
|
| sentence1 | sentence2 | score | |
|
|:-------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| |
|
| <code>How can I filter uncertainty reports by equipment tag?</code> | <code>How does a flow computer generate and store reports?<br>A flow computer generates daily or hourly reports to provide users with operational data. These reports are stored in the flow computer's memory in an organized format.<br><br>Report structure:<br>- Each report includes:<br>- Date and time of the data recording.<br>- Data recorded from flow computers.<br><br>Data storage in tables:<br>The reports are saved in two tables:<br>1. Main table (Index):<br> - Stores the date, time, and flow computer identifier.<br>2. Detail table:<br> - Stores the measured values associated with the report.<br><br>Connection to the Modbus table:<br>The flow computer's reports are linked to a Modbus table. This table contains the names corresponding to each value in the reports, making it easier to interpret the data.</code> | <code>0.09999999999999999</code> | |
|
| <code>What is the purpose of the flow_data table?</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>0.15000000000000002</code> | |
|
| <code>What is the column name for the report date in the Reports table?</code> | <code>What is equipment calibration?<br>Calibration is a metrological verification process used to ensure the accuracy of measurement equipment. It is performed periodically, based on intervals set by the company or a regulatory body.<br><br>Purpose of calibration:<br>The calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data.<br><br>Calibration cycles:<br>There are two main calibration cycles:<br>1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented.<br>2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements.<br><br>Calibration uncertainty:<br>- Uncertainty is included in the results of a calibration.<br>- Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or ...</code> | <code>0.1</code> | |
|
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
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### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 4 |
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- `per_device_eval_batch_size`: 4 |
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- `gradient_accumulation_steps`: 4 |
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- `learning_rate`: 2e-05 |
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- `warmup_ratio`: 0.1 |
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|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 4 |
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- `per_device_eval_batch_size`: 4 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 4 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 3 |
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- `max_steps`: -1 |
|
- `lr_scheduler_type`: linear |
|
- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
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- `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 |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: None |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|:------:|:----:|:-------------:|:---------------:| |
|
| 0.0307 | 10 | 1.5374 | - | |
|
| 0.0613 | 20 | 1.0251 | - | |
|
| 0.0920 | 30 | 0.361 | - | |
|
| 0.1226 | 40 | 0.1819 | - | |
|
| 0.1533 | 50 | 0.186 | - | |
|
| 0.1839 | 60 | 0.1697 | - | |
|
| 0.2146 | 70 | 0.1437 | - | |
|
| 0.2452 | 80 | 0.172 | - | |
|
| 0.2759 | 90 | 0.1199 | - | |
|
| 0.3065 | 100 | 0.1278 | - | |
|
| 0.3372 | 110 | 0.1037 | - | |
|
| 0.3678 | 120 | 0.1156 | - | |
|
| 0.3985 | 130 | 0.0971 | - | |
|
| 0.4291 | 140 | 0.0911 | - | |
|
| 0.4598 | 150 | 0.1158 | 0.0249 | |
|
| 0.4904 | 160 | 0.0906 | - | |
|
| 0.5211 | 170 | 0.106 | - | |
|
| 0.5517 | 180 | 0.0921 | - | |
|
| 0.5824 | 190 | 0.0748 | - | |
|
| 0.6130 | 200 | 0.0741 | - | |
|
| 0.6437 | 210 | 0.0894 | - | |
|
| 0.6743 | 220 | 0.0815 | - | |
|
| 0.7050 | 230 | 0.0771 | - | |
|
| 0.7356 | 240 | 0.1156 | - | |
|
| 0.7663 | 250 | 0.0857 | - | |
|
| 0.7969 | 260 | 0.0566 | - | |
|
| 0.8276 | 270 | 0.0716 | - | |
|
| 0.8582 | 280 | 0.0662 | - | |
|
| 0.8889 | 290 | 0.0963 | - | |
|
| 0.9195 | 300 | 0.0678 | 0.0212 | |
|
| 0.9502 | 310 | 0.077 | - | |
|
| 0.9808 | 320 | 0.0642 | - | |
|
| 1.0092 | 330 | 0.0725 | - | |
|
| 1.0398 | 340 | 0.0701 | - | |
|
| 1.0705 | 350 | 0.0549 | - | |
|
| 1.1011 | 360 | 0.0699 | - | |
|
| 1.1318 | 370 | 0.0714 | - | |
|
| 1.1625 | 380 | 0.0745 | - | |
|
| 1.1931 | 390 | 0.0754 | - | |
|
| 1.2238 | 400 | 0.0486 | - | |
|
| 1.2544 | 410 | 0.047 | - | |
|
| 1.2851 | 420 | 0.076 | - | |
|
| 1.3157 | 430 | 0.0689 | - | |
|
| 1.3464 | 440 | 0.0629 | - | |
|
| 1.3770 | 450 | 0.0657 | 0.0178 | |
|
| 1.4077 | 460 | 0.0622 | - | |
|
| 1.4383 | 470 | 0.0657 | - | |
|
| 1.4690 | 480 | 0.0498 | - | |
|
| 1.4996 | 490 | 0.0653 | - | |
|
| 1.5303 | 500 | 0.0715 | - | |
|
| 1.5609 | 510 | 0.0615 | - | |
|
| 1.5916 | 520 | 0.0441 | - | |
|
| 1.6222 | 530 | 0.0566 | - | |
|
| 1.6529 | 540 | 0.0524 | - | |
|
| 1.6835 | 550 | 0.0423 | - | |
|
| 1.7142 | 560 | 0.0441 | - | |
|
| 1.7448 | 570 | 0.0553 | - | |
|
| 1.7755 | 580 | 0.0572 | - | |
|
| 1.8061 | 590 | 0.0686 | - | |
|
| 1.8368 | 600 | 0.06 | 0.0146 | |
|
| 1.8674 | 610 | 0.0562 | - | |
|
| 1.8981 | 620 | 0.0517 | - | |
|
| 1.9287 | 630 | 0.0498 | - | |
|
| 1.9594 | 640 | 0.0424 | - | |
|
| 1.9900 | 650 | 0.0729 | - | |
|
| 2.0184 | 660 | 0.0347 | - | |
|
| 2.0490 | 670 | 0.06 | - | |
|
| 2.0797 | 680 | 0.0441 | - | |
|
| 2.1103 | 690 | 0.0409 | - | |
|
| 2.1410 | 700 | 0.0416 | - | |
|
| 2.1716 | 710 | 0.0345 | - | |
|
| 2.2023 | 720 | 0.024 | - | |
|
| 2.2330 | 730 | 0.0458 | - | |
|
| 2.2636 | 740 | 0.0465 | - | |
|
| 2.2943 | 750 | 0.0494 | 0.0132 | |
|
| 2.3249 | 760 | 0.0388 | - | |
|
| 2.3556 | 770 | 0.0363 | - | |
|
| 2.3862 | 780 | 0.0441 | - | |
|
| 2.4169 | 790 | 0.0378 | - | |
|
| 2.4475 | 800 | 0.0484 | - | |
|
| 2.4782 | 810 | 0.051 | - | |
|
| 2.5088 | 820 | 0.0464 | - | |
|
| 2.5395 | 830 | 0.036 | - | |
|
| 2.5701 | 840 | 0.0423 | - | |
|
| 2.6008 | 850 | 0.0278 | - | |
|
| 2.6314 | 860 | 0.0474 | - | |
|
| 2.6621 | 870 | 0.0357 | - | |
|
| 2.6927 | 880 | 0.0386 | - | |
|
| 2.7234 | 890 | 0.0334 | - | |
|
| 2.7540 | 900 | 0.0199 | 0.0127 | |
|
| 2.7847 | 910 | 0.0381 | - | |
|
| 2.8153 | 920 | 0.0415 | - | |
|
| 2.8460 | 930 | 0.0274 | - | |
|
| 2.8766 | 940 | 0.0353 | - | |
|
| 2.9073 | 950 | 0.0423 | - | |
|
| 2.9379 | 960 | 0.0267 | - | |
|
| 2.9686 | 970 | 0.042 | - | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.11.0 |
|
- Sentence Transformers: 3.4.0 |
|
- Transformers: 4.48.1 |
|
- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.3.0 |
|
- Datasets: 3.2.0 |
|
- Tokenizers: 0.21.0 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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