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measuring-embeddings-v3

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:7552
loss:CoSENTLoss
text-embeddings-inference
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7552
- loss:CoSENTLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
- source_sentence: How are calibration points linked to equipment?
sentences:
- >-
How are flow computers and measurement systems related?
Flow computers can have multiple systems assigned to them. However, a
measurement system can only be assigned to one flow computer.
Database terminology:
In the database, this relationship is referred to as:
- Meter streams
- Meter runs
- Sections
Storage of the relationship:
The relationship between a flow computer and its assigned measurement system
is stored in a special table.
User context:
When a user refers to a "meter stream," they are indicating that they are
searching for a measurement system assigned to a specific flow computer.
- >-
How does a flow computer generate and store reports?
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.
Report structure:
- Each report includes:
- Date and time of the data recording.
- Data recorded from flow computers.
Data storage in tables:
The reports are saved in two tables:
1. Main table (Index):
- Stores the date, time, and flow computer identifier.
2. Detail table:
- Stores the measured values associated with the report.
Connection to the Modbus table:
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.
- >-
What is uncertainty?
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.
Types of uncertainty:
There are two main types of uncertainty:
1. Uncertainty of magnitudes (variables):
- Refers to the uncertainty of specific variables, such as temperature or pressure.
- It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.
- This uncertainty serves as a starting point for further calculations related to the equipment.
2. Uncertainty of the measurement system:
- Refers to the uncertainty calculated for the overall flow measurement.
- It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.
Key points:
- The uncertainties of magnitudes (variables) are the foundation for
calculating the uncertainty of the measurement system. Think of them as the
"building blocks."
- Do not confuse the two types of uncertainty:
- **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).
- **Uncertainty of the measurement system**: Specific to the overall flow measurement.
Database storage for uncertainties:
In the database, uncertainty calculations are stored in two separate tables:
1. Uncertainty of magnitudes (variables):
- Stores the uncertainty values for specific variables (e.g., temperature, pressure).
2. Uncertainty of the measurement system:
- Stores the uncertainty values for the overall flow measurement system.
How to retrieve uncertainty data:
- To find the uncertainty of the measurement system, join the measurement
systems table with the uncertainty of the measurement system table.
- To find the uncertainty of a specific variable (magnitude), join the
measurement systems table with the uncertainty of magnitudes (variables)
table.
Important note:
Do not confuse the two types of uncertainty:
- If the user requests the uncertainty of the measurement system, use the
first join (measurement systems table + uncertainty of the measurement
system table).
- If the user requests the uncertainty of a specific variable (magnitude) in
a report, use the second join (measurement systems table + uncertainty of
magnitudes table).
- source_sentence: What is the primary key of the flow computer table?
sentences:
- >-
What is equipment calibration?
Calibration is a metrological verification process used to ensure the
accuracy of measurement equipment. It is performed periodically, based on
intervals set by the company or a regulatory body.
Purpose of calibration:
The calibration process corrects any deviations in how the equipment
measures physical magnitudes (variables). This ensures the equipment
provides accurate and reliable data.
Calibration cycles:
There are two main calibration cycles:
1. As-found: Represents the equipment's measurement accuracy before any
adjustments are made. This cycle is almost always implemented.
2. As-left: Represents the equipment's measurement accuracy after
adjustments are made. This cycle is used depending on regulatory
requirements.
Calibration uncertainty:
- Uncertainty is included in the results of a calibration.
- Calibration uncertainty refers to the margin of error in the device's
measurements, which also affects the uncertainty of the measured variable or
magnitude.
- >-
What is equipment calibration?
Calibration is a metrological verification process used to ensure the
accuracy of measurement equipment. It is performed periodically, based on
intervals set by the company or a regulatory body.
Purpose of calibration:
The calibration process corrects any deviations in how the equipment
measures physical magnitudes (variables). This ensures the equipment
provides accurate and reliable data.
Calibration cycles:
There are two main calibration cycles:
1. As-found: Represents the equipment's measurement accuracy before any
adjustments are made. This cycle is almost always implemented.
2. As-left: Represents the equipment's measurement accuracy after
adjustments are made. This cycle is used depending on regulatory
requirements.
Calibration uncertainty:
- Uncertainty is included in the results of a calibration.
- Calibration uncertainty refers to the margin of error in the device's
measurements, which also affects the uncertainty of the measured variable or
magnitude.
- >-
How does a flow computer generate and store reports?
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.
Report structure:
- Each report includes:
- Date and time of the data recording.
- Data recorded from flow computers.
Data storage in tables:
The reports are saved in two tables:
1. Main table (Index):
- Stores the date, time, and flow computer identifier.
2. Detail table:
- Stores the measured values associated with the report.
Connection to the Modbus table:
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.
- source_sentence: >-
Can you provide a sample query to test the retrieval of the uncertainty
result for the specified tag and date?
sentences:
- >-
What is equipment calibration?
Calibration is a metrological verification process used to ensure the
accuracy of measurement equipment. It is performed periodically, based on
intervals set by the company or a regulatory body.
Purpose of calibration:
The calibration process corrects any deviations in how the equipment
measures physical magnitudes (variables). This ensures the equipment
provides accurate and reliable data.
Calibration cycles:
There are two main calibration cycles:
1. As-found: Represents the equipment's measurement accuracy before any
adjustments are made. This cycle is almost always implemented.
2. As-left: Represents the equipment's measurement accuracy after
adjustments are made. This cycle is used depending on regulatory
requirements.
Calibration uncertainty:
- Uncertainty is included in the results of a calibration.
- Calibration uncertainty refers to the margin of error in the device's
measurements, which also affects the uncertainty of the measured variable or
magnitude.
- >-
What kind of data store an equipment?
Equipments can capture meteorological data, such as pressure, temperature,
and volume (magnitudes). This data is essential for users to perform various
calculations.
Data storage:
- The measured values are stored in a special table in the database for
magnitudes. This table contains the values of the variables captured by the
equipments.
- These values are **direct measurements** from the fluid (e.g., raw
pressure, temperature, or volume readings). **They are not calculated
values**, such as uncertainty.
- The values stored in the variable values table are **different** from
variable uncertainty values, which are calculated separately and represent
the margin of error.
Accessing the data:
- Users typically access the data by referring to the readings from the
measurement system, not directly from the individual equipments.
- The readings are stored in a "variable values" table within the database.
Linking variable names:
If the user needs to know the name of a variable, they must link the data to
another table that stores information about the types of variables.
- >-
What is uncertainty?
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.
Types of uncertainty:
There are two main types of uncertainty:
1. Uncertainty of magnitudes (variables):
- Refers to the uncertainty of specific variables, such as temperature or pressure.
- It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.
- This uncertainty serves as a starting point for further calculations related to the equipment.
2. Uncertainty of the measurement system:
- Refers to the uncertainty calculated for the overall flow measurement.
- It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.
Key points:
- The uncertainties of magnitudes (variables) are the foundation for
calculating the uncertainty of the measurement system. Think of them as the
"building blocks."
- Do not confuse the two types of uncertainty:
- **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).
- **Uncertainty of the measurement system**: Specific to the overall flow measurement.
Database storage for uncertainties:
In the database, uncertainty calculations are stored in two separate tables:
1. Uncertainty of magnitudes (variables):
- Stores the uncertainty values for specific variables (e.g., temperature, pressure).
2. Uncertainty of the measurement system:
- Stores the uncertainty values for the overall flow measurement system.
How to retrieve uncertainty data:
- To find the uncertainty of the measurement system, join the measurement
systems table with the uncertainty of the measurement system table.
- To find the uncertainty of a specific variable (magnitude), join the
measurement systems table with the uncertainty of magnitudes (variables)
table.
Important note:
Do not confuse the two types of uncertainty:
- If the user requests the uncertainty of the measurement system, use the
first join (measurement systems table + uncertainty of the measurement
system table).
- If the user requests the uncertainty of a specific variable (magnitude) in
a report, use the second join (measurement systems table + uncertainty of
magnitudes table).
- source_sentence: How are the secondary equipment and measurement system related?
sentences:
- >-
What kind of data store an equipment?
Equipments can capture meteorological data, such as pressure, temperature,
and volume (magnitudes). This data is essential for users to perform various
calculations.
Data storage:
- The measured values are stored in a special table in the database for
magnitudes. This table contains the values of the variables captured by the
equipments.
- These values are **direct measurements** from the fluid (e.g., raw
pressure, temperature, or volume readings). **They are not calculated
values**, such as uncertainty.
- The values stored in the variable values table are **different** from
variable uncertainty values, which are calculated separately and represent
the margin of error.
Accessing the data:
- Users typically access the data by referring to the readings from the
measurement system, not directly from the individual equipments.
- The readings are stored in a "variable values" table within the database.
Linking variable names:
If the user needs to know the name of a variable, they must link the data to
another table that stores information about the types of variables.
- >-
What do measurement equipment measure?
Each equipment measures a physical magnitude, also known as a variable.
Based on the type of variable they measure, devices are classified into
different categories.
Equipment classification:
- Primary meter: Assigned by default to equipments like orifice plates.
- Secondary meter: Assigned by default to equipments like transmitters.
- Tertiary meter: Used for other types of equipments.
Equipment types in the database:
The database includes a table listing all equipment types. Examples of
equipment types are:
- Differential pressure transmitters
- RTDs (Resistance Temperature Detectors)
- Orifice plates
- Multivariable transmitters
- Ultrasonic meters
Meteorological checks for equipments:
Each equipment type is assigned a meteorological check, which can be either:
- Calibration: To ensure measurement accuracy.
- Inspection: To verify proper functioning.
Data storage in tables:
The database also includes a separate table for equipment classifications,
which are:
- Primary meter
- Secondary meter
- Tertiary meter
So, an equipment has equipment types and this types has classifications.
- >-
What kind of data store an equipment?
Equipments can capture meteorological data, such as pressure, temperature,
and volume (magnitudes). This data is essential for users to perform various
calculations.
Data storage:
- The measured values are stored in a special table in the database for
magnitudes. This table contains the values of the variables captured by the
equipments.
- These values are **direct measurements** from the fluid (e.g., raw
pressure, temperature, or volume readings). **They are not calculated
values**, such as uncertainty.
- The values stored in the variable values table are **different** from
variable uncertainty values, which are calculated separately and represent
the margin of error.
Accessing the data:
- Users typically access the data by referring to the readings from the
measurement system, not directly from the individual equipments.
- The readings are stored in a "variable values" table within the database.
Linking variable names:
If the user needs to know the name of a variable, they must link the data to
another table that stores information about the types of variables.
- source_sentence: What is the table structure for secondary equipment?
sentences:
- >-
What kind of data store an equipment?
Equipments can capture meteorological data, such as pressure, temperature,
and volume (magnitudes). This data is essential for users to perform various
calculations.
Data storage:
- The measured values are stored in a special table in the database for
magnitudes. This table contains the values of the variables captured by the
equipments.
- These values are **direct measurements** from the fluid (e.g., raw
pressure, temperature, or volume readings). **They are not calculated
values**, such as uncertainty.
- The values stored in the variable values table are **different** from
variable uncertainty values, which are calculated separately and represent
the margin of error.
Accessing the data:
- Users typically access the data by referring to the readings from the
measurement system, not directly from the individual equipments.
- The readings are stored in a "variable values" table within the database.
Linking variable names:
If the user needs to know the name of a variable, they must link the data to
another table that stores information about the types of variables.
- >-
How are flow computers and measurement systems related?
Flow computers can have multiple systems assigned to them. However, a
measurement system can only be assigned to one flow computer.
Database terminology:
In the database, this relationship is referred to as:
- Meter streams
- Meter runs
- Sections
Storage of the relationship:
The relationship between a flow computer and its assigned measurement system
is stored in a special table.
User context:
When a user refers to a "meter stream," they are indicating that they are
searching for a measurement system assigned to a specific flow computer.
- >-
How are flow computers and measurement systems related?
Flow computers can have multiple systems assigned to them. However, a
measurement system can only be assigned to one flow computer.
Database terminology:
In the database, this relationship is referred to as:
- Meter streams
- Meter runs
- Sections
Storage of the relationship:
The relationship between a flow computer and its assigned measurement system
is stored in a special table.
User context:
When a user refers to a "meter stream," they are indicating that they are
searching for a measurement system assigned to a specific flow computer.
datasets:
- Lauther/measuring-embeddings-v3
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision c9e87c786ffac96aeaeb42863276930883923ecb -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/measuring-embeddings-v3-multilingual-e5-large-instruct-20e")
# Run inference
sentences = [
'What is the table structure for secondary equipment?',
'How are flow computers and measurement systems related?\nFlow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.\n\nDatabase terminology:\nIn the database, this relationship is referred to as:\n- Meter streams\n- Meter runs\n- Sections\n\nStorage of the relationship:\nThe relationship between a flow computer and its assigned measurement system is stored in a special table.\n\nUser context:\nWhen a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.',
'What kind of data store an equipment?\nEquipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.\n\nData storage:\n- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.\n- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.\n- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.\n\nAccessing the data:\n- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.\n- The readings are stored in a "variable values" table within the database.\n\nLinking variable names:\nIf the user needs to know the name of a variable, they must link the data to another table that stores information about the types of variables.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### measuring-embeddings-v3
* Dataset: [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) at [1b3cbbe](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3/tree/1b3cbbeb70b63338110491cd3de2950fb40b4f87)
* Size: 7,552 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 9 tokens</li><li>mean: 15.96 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 255.56 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.22</li><li>max: 0.95</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>How can I combine the sub-query with the main query to fetch the last uncertainty report?</code> | <code>What do measurement equipment measure?<br>Each equipment measures a physical magnitude, also known as a variable. Based on the type of variable they measure, devices are classified into different categories.<br><br>Equipment classification:<br>- Primary meter: Assigned by default to equipments like orifice plates.<br>- Secondary meter: Assigned by default to equipments like transmitters.<br>- Tertiary meter: Used for other types of equipments.<br><br>Equipment types in the database:<br>The database includes a table listing all equipment types. Examples of equipment types are:<br>- Differential pressure transmitters<br>- RTDs (Resistance Temperature Detectors)<br>- Orifice plates<br>- Multivariable transmitters<br>- Ultrasonic meters<br><br>Meteorological checks for equipments:<br>Each equipment type is assigned a meteorological check, which can be either:<br>- Calibration: To ensure measurement accuracy.<br>- Inspection: To verify proper functioning.<br><br>Data storage in tables:<br>The database also includes a separate table for equipment classific...</code> | <code>0.1</code> |
| <code>What is the column name for the calibration date in the calibration table?</code> | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.</code> | <code>0.1</code> |
| <code>What is the name of the table that contains the flow computer tags?</code> | <code>What is equipment calibration?<br>Calibration is a metrological verification process used to ensure the accuracy of measurement equipment. It is performed periodically, based on intervals set by the company or a regulatory body.<br><br>Purpose of calibration:<br>The calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data.<br><br>Calibration cycles:<br>There are two main calibration cycles:<br>1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented.<br>2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements.<br><br>Calibration uncertainty:<br>- Uncertainty is included in the results of a calibration.<br>- Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or ...</code> | <code>0.05</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### measuring-embeddings-v3
* Dataset: [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) at [1b3cbbe](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3/tree/1b3cbbeb70b63338110491cd3de2950fb40b4f87)
* Size: 1,618 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 9 tokens</li><li>mean: 15.83 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 250.41 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 0.95</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>Identify any additional tables or columns that might be needed for the query.</code> | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.</code> | <code>0.2</code> |
| <code>What columns in these tables contain the measurement system tag and the flow computer tag?</code> | <code>How does a flow computer generate and store reports?<br>A flow computer generates daily or hourly reports to provide users with operational data. These reports are stored in the flow computer's memory in an organized format.<br><br>Report structure:<br>- Each report includes:<br>- Date and time of the data recording.<br>- Data recorded from flow computers.<br><br>Data storage in tables:<br>The reports are saved in two tables:<br>1. Main table (Index):<br> - Stores the date, time, and flow computer identifier.<br>2. Detail table:<br> - Stores the measured values associated with the report.<br><br>Connection to the Modbus table:<br>The flow computer's reports are linked to a Modbus table. This table contains the names corresponding to each value in the reports, making it easier to interpret the data.</code> | <code>0.1</code> |
| <code>Identify the column that stores the calibration number.</code> | <code>What kind of data store an equipment?<br>Equipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.<br><br>Data storage:<br>- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.<br>- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.<br>- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.<br><br>Accessing the data:<br>- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.<br>- The readings are stored in a "variable values" table within the database.<br><br>Linking variable names:<br>If the user needs to kno...</code> | <code>0.1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 7
- `per_device_eval_batch_size`: 7
- `gradient_accumulation_steps`: 4
- `learning_rate`: 3e-05
- `num_train_epochs`: 20
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 7
- `per_device_eval_batch_size`: 7
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 20
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 9.5153 | 2560 | 6.782 | - |
| 9.5524 | 2570 | 7.3027 | - |
| 9.5894 | 2580 | 7.3348 | - |
| 9.6265 | 2590 | 7.7864 | - |
| 9.6636 | 2600 | 6.3552 | - |
| 9.7006 | 2610 | 7.151 | - |
| 9.7377 | 2620 | 6.1664 | - |
| 9.7748 | 2630 | 6.0398 | - |
| 9.8119 | 2640 | 7.0452 | - |
| 9.8489 | 2650 | 7.2457 | - |
| 9.8860 | 2660 | 6.7531 | - |
| 9.9231 | 2670 | 6.7149 | - |
| 9.9601 | 2680 | 6.4635 | - |
| 9.9972 | 2690 | 6.2237 | - |
| 10.0371 | 2700 | 6.1798 | 2.9939 |
| 10.0741 | 2710 | 7.2224 | - |
| 10.1112 | 2720 | 6.5327 | - |
| 10.1483 | 2730 | 7.4686 | - |
| 10.1854 | 2740 | 6.1404 | - |
| 10.2224 | 2750 | 7.0005 | - |
| 10.2595 | 2760 | 5.7726 | - |
| 10.2966 | 2770 | 6.5327 | - |
| 10.3336 | 2780 | 7.5015 | - |
| 10.3707 | 2790 | 6.5526 | - |
| 10.4078 | 2800 | 6.2078 | - |
| 10.4449 | 2810 | 6.1 | - |
| 10.4819 | 2820 | 7.1027 | - |
| 10.5190 | 2830 | 8.639 | - |
| 10.5561 | 2840 | 6.9937 | - |
| 10.5931 | 2850 | 7.2734 | 2.8532 |
| 10.6302 | 2860 | 7.6321 | - |
| 10.6673 | 2870 | 7.5788 | - |
| 10.7044 | 2880 | 6.7864 | - |
| 10.7414 | 2890 | 7.4237 | - |
| 10.7785 | 2900 | 6.9813 | - |
| 10.8156 | 2910 | 6.6884 | - |
| 10.8526 | 2920 | 6.7464 | - |
| 10.8897 | 2930 | 7.7989 | - |
| 10.9268 | 2940 | 7.3568 | - |
| 10.9639 | 2950 | 8.6706 | - |
| 11.0 | 2960 | 6.5687 | - |
| 11.0371 | 2970 | 5.8992 | - |
| 11.0741 | 2980 | 6.4543 | - |
| 11.1112 | 2990 | 6.1386 | - |
| 11.1483 | 3000 | 6.9047 | 2.9147 |
| 11.1854 | 3010 | 7.405 | - |
| 11.2224 | 3020 | 7.5441 | - |
| 11.2595 | 3030 | 6.7524 | - |
| 11.2966 | 3040 | 7.698 | - |
| 11.3336 | 3050 | 7.6167 | - |
| 11.3707 | 3060 | 7.1516 | - |
| 11.4078 | 3070 | 6.7458 | - |
| 11.4449 | 3080 | 6.7608 | - |
| 11.4819 | 3090 | 7.1508 | - |
| 11.5190 | 3100 | 6.9155 | - |
| 11.5561 | 3110 | 6.6664 | - |
| 11.5931 | 3120 | 8.3841 | - |
| 11.6302 | 3130 | 7.1934 | - |
| 11.6673 | 3140 | 6.9681 | - |
| 11.7044 | 3150 | 7.2187 | 2.7509 |
| 11.7414 | 3160 | 7.3155 | - |
| 11.7785 | 3170 | 7.3103 | - |
| 11.8156 | 3180 | 7.1959 | - |
| 11.8526 | 3190 | 6.8164 | - |
| 11.8897 | 3200 | 7.5836 | - |
| 11.9268 | 3210 | 5.2671 | - |
| 11.9639 | 3220 | 6.4929 | - |
| 12.0 | 3230 | 7.0892 | - |
| 12.0371 | 3240 | 7.0877 | - |
| 12.0741 | 3250 | 5.8302 | - |
| 12.1112 | 3260 | 5.6145 | - |
| 12.1483 | 3270 | 6.5808 | - |
| 12.1854 | 3280 | 6.6826 | - |
| 12.2224 | 3290 | 5.9819 | - |
| 12.2595 | 3300 | 6.68 | 3.0175 |
| 12.2966 | 3310 | 6.1685 | - |
| 12.3336 | 3320 | 6.4473 | - |
| 12.3707 | 3330 | 6.3965 | - |
| 12.4078 | 3340 | 6.6278 | - |
| 12.4449 | 3350 | 5.4575 | - |
| 12.4819 | 3360 | 7.3019 | - |
| 12.5190 | 3370 | 7.4843 | - |
| 12.5561 | 3380 | 6.709 | - |
| 12.5931 | 3390 | 6.7168 | - |
| 12.6302 | 3400 | 7.0223 | - |
| 12.6673 | 3410 | 6.5089 | - |
| 12.7044 | 3420 | 6.5094 | - |
| 12.7414 | 3430 | 7.2317 | - |
| 12.7785 | 3440 | 6.6885 | - |
| 12.8156 | 3450 | 6.9693 | 2.8462 |
| 12.8526 | 3460 | 6.8242 | - |
| 12.8897 | 3470 | 6.6899 | - |
| 12.9268 | 3480 | 6.9113 | - |
| 12.9639 | 3490 | 7.1903 | - |
| 13.0 | 3500 | 7.3286 | - |
| 13.0371 | 3510 | 6.5465 | - |
| 13.0741 | 3520 | 5.6804 | - |
| 13.1112 | 3530 | 5.6412 | - |
| 13.1483 | 3540 | 6.6161 | - |
| 13.1854 | 3550 | 5.761 | - |
| 13.2224 | 3560 | 5.5669 | - |
| 13.2595 | 3570 | 5.6184 | - |
| 13.2966 | 3580 | 6.2996 | - |
| 13.3336 | 3590 | 4.99 | - |
| 13.3707 | 3600 | 5.9974 | 3.2358 |
| 13.4078 | 3610 | 5.6962 | - |
| 13.4449 | 3620 | 6.3662 | - |
| 13.4819 | 3630 | 7.0398 | - |
| 13.5190 | 3640 | 7.7358 | - |
| 13.5561 | 3650 | 7.9063 | - |
| 13.5931 | 3660 | 5.7823 | - |
| 13.6302 | 3670 | 6.9861 | - |
| 13.6673 | 3680 | 7.2855 | - |
| 13.7044 | 3690 | 5.6785 | - |
| 13.7414 | 3700 | 6.4071 | - |
| 13.7785 | 3710 | 6.4294 | - |
| 13.8156 | 3720 | 6.0842 | - |
| 13.8526 | 3730 | 5.9422 | - |
| 13.8897 | 3740 | 7.0778 | - |
| 13.9268 | 3750 | 8.1597 | 3.0093 |
| 13.9639 | 3760 | 6.3154 | - |
| 14.0 | 3770 | 6.2416 | - |
| 14.0371 | 3780 | 5.9958 | - |
| 14.0741 | 3790 | 5.7032 | - |
| 14.1112 | 3800 | 4.9524 | - |
| 14.1483 | 3810 | 5.386 | - |
| 14.1854 | 3820 | 5.6353 | - |
| 14.2224 | 3830 | 5.0873 | - |
| 14.2595 | 3840 | 4.9255 | - |
| 14.2966 | 3850 | 5.1423 | - |
| 14.3336 | 3860 | 6.0775 | - |
| 14.3707 | 3870 | 4.5073 | - |
| 14.4078 | 3880 | 6.8347 | - |
| 14.4449 | 3890 | 6.5397 | - |
| 14.4819 | 3900 | 7.2143 | 3.3080 |
| 14.5190 | 3910 | 6.1123 | - |
| 14.5561 | 3920 | 6.6048 | - |
| 14.5931 | 3930 | 6.3464 | - |
| 14.6302 | 3940 | 6.3618 | - |
| 14.6673 | 3950 | 6.5718 | - |
| 14.7044 | 3960 | 5.9785 | - |
| 14.7414 | 3970 | 6.5758 | - |
| 14.7785 | 3980 | 6.4308 | - |
| 14.8156 | 3990 | 6.0208 | - |
| 14.8526 | 4000 | 6.0303 | - |
| 14.8897 | 4010 | 6.6396 | - |
| 14.9268 | 4020 | 6.0184 | - |
| 14.9639 | 4030 | 6.6248 | - |
| 15.0 | 4040 | 6.4538 | - |
| 15.0371 | 4050 | 6.4742 | 3.1761 |
| 15.0741 | 4060 | 5.5295 | - |
| 15.1112 | 4070 | 6.8753 | - |
| 15.1483 | 4080 | 5.639 | - |
| 15.1854 | 4090 | 5.6232 | - |
| 15.2224 | 4100 | 6.3026 | - |
| 15.2595 | 4110 | 6.1182 | - |
| 15.2966 | 4120 | 5.4736 | - |
| 15.3336 | 4130 | 6.2961 | - |
| 15.3707 | 4140 | 5.4742 | - |
| 15.4078 | 4150 | 5.4707 | - |
| 15.4449 | 4160 | 4.7272 | - |
| 15.4819 | 4170 | 6.1026 | - |
| 15.5190 | 4180 | 5.0468 | - |
| 15.5561 | 4190 | 5.5796 | - |
| 15.5931 | 4200 | 6.9046 | 3.1433 |
| 15.6302 | 4210 | 5.6123 | - |
| 15.6673 | 4220 | 6.7246 | - |
| 15.7044 | 4230 | 5.7076 | - |
| 15.7414 | 4240 | 6.6772 | - |
| 15.7785 | 4250 | 5.6038 | - |
| 15.8156 | 4260 | 4.9544 | - |
| 15.8526 | 4270 | 5.0661 | - |
| 15.8897 | 4280 | 5.291 | - |
| 15.9268 | 4290 | 6.6652 | - |
| 15.9639 | 4300 | 5.6797 | - |
| 16.0 | 4310 | 5.1129 | - |
| 16.0371 | 4320 | 5.4445 | - |
| 16.0741 | 4330 | 4.8946 | - |
| 16.1112 | 4340 | 6.3929 | - |
| 16.1483 | 4350 | 6.0633 | 3.1426 |
| 16.1854 | 4360 | 5.522 | - |
| 16.2224 | 4370 | 4.7067 | - |
| 16.2595 | 4380 | 5.4688 | - |
| 16.2966 | 4390 | 5.6009 | - |
| 16.3336 | 4400 | 5.1376 | - |
| 16.3707 | 4410 | 4.5196 | - |
| 16.4078 | 4420 | 5.5109 | - |
| 16.4449 | 4430 | 5.1888 | - |
| 16.4819 | 4440 | 6.0305 | - |
| 16.5190 | 4450 | 5.2791 | - |
| 16.5561 | 4460 | 5.4005 | - |
| 16.5931 | 4470 | 5.255 | - |
| 16.6302 | 4480 | 6.2026 | - |
| 16.6673 | 4490 | 6.6388 | - |
| 16.7044 | 4500 | 5.6138 | 3.2812 |
| 16.7414 | 4510 | 4.7913 | - |
| 16.7785 | 4520 | 5.6675 | - |
| 16.8156 | 4530 | 5.8975 | - |
| 16.8526 | 4540 | 5.4597 | - |
| 16.8897 | 4550 | 5.137 | - |
| 16.9268 | 4560 | 4.5395 | - |
| 16.9639 | 4570 | 4.6304 | - |
| 17.0 | 4580 | 5.8098 | - |
| 17.0371 | 4590 | 4.0267 | - |
| 17.0741 | 4600 | 4.9194 | - |
| 17.1112 | 4610 | 4.1852 | - |
| 17.1483 | 4620 | 5.129 | - |
| 17.1854 | 4630 | 4.469 | - |
| 17.2224 | 4640 | 5.4298 | - |
| 17.2595 | 4650 | 4.5234 | 3.3447 |
| 17.2966 | 4660 | 4.6856 | - |
| 17.3336 | 4670 | 6.3431 | - |
| 17.3707 | 4680 | 5.347 | - |
| 17.4078 | 4690 | 4.9223 | - |
| 17.4449 | 4700 | 5.4404 | - |
| 17.4819 | 4710 | 4.916 | - |
| 17.5190 | 4720 | 6.1744 | - |
| 17.5561 | 4730 | 4.8039 | - |
| 17.5931 | 4740 | 5.2276 | - |
| 17.6302 | 4750 | 4.4189 | - |
| 17.6673 | 4760 | 4.1434 | - |
| 17.7044 | 4770 | 4.9443 | - |
| 17.7414 | 4780 | 5.6975 | - |
| 17.7785 | 4790 | 4.6667 | - |
| 17.8156 | 4800 | 4.9876 | 3.2924 |
| 17.8526 | 4810 | 4.4342 | - |
| 17.8897 | 4820 | 5.2595 | - |
| 17.9268 | 4830 | 5.6566 | - |
| 17.9639 | 4840 | 5.5452 | - |
| 18.0 | 4850 | 4.4986 | - |
| 18.0371 | 4860 | 4.8155 | - |
| 18.0741 | 4870 | 4.2278 | - |
| 18.1112 | 4880 | 5.4733 | - |
| 18.1483 | 4890 | 4.2394 | - |
| 18.1854 | 4900 | 5.1253 | - |
| 18.2224 | 4910 | 4.7498 | - |
| 18.2595 | 4920 | 4.9775 | - |
| 18.2966 | 4930 | 4.797 | - |
| 18.3336 | 4940 | 4.5694 | - |
| 18.3707 | 4950 | 4.6192 | 3.6615 |
| 18.4078 | 4960 | 5.8114 | - |
| 18.4449 | 4970 | 4.8035 | - |
| 18.4819 | 4980 | 4.6944 | - |
| 18.5190 | 4990 | 4.8664 | - |
| 18.5561 | 5000 | 4.6916 | - |
| 18.5931 | 5010 | 4.3352 | - |
| 18.6302 | 5020 | 5.9779 | - |
| 18.6673 | 5030 | 4.7813 | - |
| 18.7044 | 5040 | 4.632 | - |
| 18.7414 | 5050 | 4.7411 | - |
| 18.7785 | 5060 | 3.6489 | - |
| 18.8156 | 5070 | 4.5373 | - |
| 18.8526 | 5080 | 5.6129 | - |
| 18.8897 | 5090 | 4.8933 | - |
| 18.9268 | 5100 | 4.27 | 3.6957 |
| 18.9639 | 5110 | 4.5338 | - |
| 19.0 | 5120 | 5.5175 | - |
| 19.0371 | 5130 | 5.0835 | - |
| 19.0741 | 5140 | 4.6826 | - |
| 19.1112 | 5150 | 4.5391 | - |
| 19.1483 | 5160 | 5.3723 | - |
| 19.1854 | 5170 | 4.8095 | - |
| 19.2224 | 5180 | 4.7402 | - |
| 19.2595 | 5190 | 4.0488 | - |
| 19.2966 | 5200 | 3.6424 | - |
| 19.3336 | 5210 | 4.2256 | - |
| 19.3707 | 5220 | 4.4607 | - |
| 19.4078 | 5230 | 3.5702 | - |
| 19.4449 | 5240 | 4.3062 | - |
| 19.4819 | 5250 | 4.2919 | 3.6594 |
| 19.5190 | 5260 | 4.6985 | - |
| 19.5561 | 5270 | 4.6907 | - |
| 19.5931 | 5280 | 4.3865 | - |
| 19.6302 | 5290 | 3.9818 | - |
| 19.6673 | 5300 | 4.3166 | - |
| 19.7044 | 5310 | 4.9131 | - |
| 19.7414 | 5320 | 4.7641 | - |
| 19.7785 | 5330 | 5.419 | - |
| 19.8156 | 5340 | 4.068 | - |
| 19.8526 | 5350 | 4.1094 | - |
| 19.8897 | 5360 | 5.2279 | - |
| 19.9268 | 5370 | 4.4818 | - |
| 19.9639 | 5380 | 4.3103 | - |
</details>
### Framework Versions
- Python: 3.11.0
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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