d4-embeddings-v2.0 / README.md

Add new SentenceTransformer model

d431cde verified 2 months ago

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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:11165
	- loss:ContrastiveLoss
	base_model: intfloat/multilingual-e5-large-instruct
	widget:
	- source_sentence: PTE CRUZEIRO B
	sentences:
	- 'What is an Installation?

	An Installation is a physical or operational site where measurement systems and
	equipment are deployed. These locations can include processing plants, industrial
	facilities, or other operational sites. Installations serve as key points for
	monitoring and managing measurement processes. Examples include "Cexis" or "Processing
	Plant XYZ."'
	- 'What is a Measurement Unit?

	A Measurement Unit defines the standard for quantifying a physical magnitude (e.g.,
	temperature, pressure, volume). It establishes a consistent reference for interpreting
	values recorded in a measurement system.


	Each measurement unit is associated with a specific magnitude, ensuring that values
	are correctly interpreted within their context. For example:


	- °C (Celsius) → Used for temperature

	- psi (pounds per square inch) → Used for pressure

	- m³ (cubic meters) → Used for volume

	Measurement units are essential for maintaining consistency across recorded data,
	ensuring comparability, and enabling accurate calculations within measurement
	systems.'
	- "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."
	- source_sentence: ECOMP-VP-03116
	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."
	- 'What is a Calibration Record?

	A Calibration Record documents the calibration process of a specific equipment
	tag, ensuring that its measurements remain accurate and reliable. Calibration
	is a critical process in maintaining measurement precision and compliance with
	standards.


	Key Aspects of a Calibration Record:

	- Calibration Date: The exact date when the calibration was performed, crucial
	for tracking maintenance schedules.

	- Certification Number: A unique identifier for the calibration certificate, providing
	traceability and verification of compliance.

	- Range Values: The minimum and maximum measurement values covered during the
	calibration process.

	- Calibration Status: Indicates whether the calibration was approved or saved
	for further review.

	- Associated Units: Specifies the measurement units used in calibration (e.g.,
	°C, psi).

	- Associated Equipment Tag ID: Links the calibration record to a specific equipment
	tag, ensuring traceability of measurement instruments.

	Calibration records play a fundamental role in quality assurance, helping maintain
	measurement integrity and regulatory compliance.'
	- 'What are Flow Computer Types?

	Flow computer types categorize different models of flow computers used in measurement
	systems, such as OMNI, KROHNE, ROC, FC302, S600, FLOWBOSS, F407, F107, and ThermoFisher.
	Each type is defined by its capabilities, functionalities, and applications, determining
	how it processes measurement data, performs calculations, and enables real-time
	monitoring. Understanding these types is essential for selecting the right equipment
	to ensure precise flow measurement, system integration, and operational efficiency.'
	- source_sentence: Resistencia
	sentences:
	- "What is an Uncertainty Curve Point?\nAn Uncertainty Curve Point represents a\
	\ data point used to construct the uncertainty curve of a measurement system.\
	\ These curves help analyze how measurement uncertainty behaves under different\
	\ flow rate conditions, ensuring accuracy and reliability in uncertainty assessments.\n\
	\nKey Aspects of an Uncertainty Curve Point:\n- Uncertainty File ID: Links the\
	\ point to the specific uncertainty dataset, ensuring traceability.\nEquipment\
	\ Tag ID: Identifies the equipment associated with the uncertainty measurement,\
	\ crucial for system validation.\n- Uncertainty Points: Represent a list uncertainty\
	\ values recorded at specific conditions, forming part of the overall uncertainty\
	\ curve. Do not confuse this uncertainty points with the calculated uncertainty.\
	\ \n- Flow Rate Points: Corresponding flow rate values at which the uncertainty\
	\ was measured, essential for evaluating performance under varying operational\
	\ conditions.\nThese points are fundamental for generating uncertainty curves,\
	\ which are used in calibration, validation, and compliance assessments to ensure\
	\ measurement reliability in industrial processes.\"\n\nIMPORTANT: Do not\
	\ confuse the two types of Points:\n - Uncertainty Curve Point: Specific\
	\ to a measurement system uncertainty or uncertainty simulation or uncertainty\
	\ curve.\n - Calibration Point: Specific to the calibration.\n - **Uncertainty\
	\ values**: Do not confuse these uncertainty points with the single calculated\
	\ uncertainty."
	- "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."
	- 'What is an Equipment Tag?

	An Equipment Tag is a unique label string identifier assigned to equipment that
	is actively installed and in use within a measurement system. It differentiates
	between equipment in general (which may be in storage or inactive) and equipment
	that is currently operational in a system.


	Key Aspects of Equipment Tags:

	- Equipment-Tag: A distinct label or identifier that uniquely marks the equipment
	in operation.

	- Equipment ID: Links the tag to the corresponding equipment unit.

	- Belonging Measurement System: Specifies which measurement system the tagged
	equipment is part of.

	- Equipment Type Name: Classifies the equipment (e.g., transmitter, thermometer),
	aiding in organization and system integration.

	The Equipment Tag is essential for tracking and managing operational equipment
	within a measurement system, ensuring proper identification, monitoring, and maintenance.'
	- source_sentence: nitrogen composition
	sentences:
	- 'What is a Meter Stream?

	A Meter Stream represents a measurement system configured within a flow computer.
	It serves as the interface between the physical measurement system and the computational
	processes that record and analyze flow data.


	Key Aspects of a Meter Stream:

	- Status: Indicates whether the meter stream is active or inactive.

	- Measurement System Association: Links the meter stream to a specific measurement
	system, ensuring that the data collected corresponds to a defined physical setup.

	- Flow Computer Association: Identifies the flow computer responsible for managing
	and recording the measurement system''s data.

	Why is a Meter Stream Important?

	A meter stream is a critical component in flow measurement, as it ensures
	that the measurement system is correctly integrated into the flow computer for
	accurate monitoring and reporting. Since each flow computer can handle multiple
	meter streams, proper configuration is essential for maintaining data integrity
	and traceability.'
	- "What is a Measurement Type?\nMeasurement types define the classification of measurements\
	\ used within a system based on their purpose and regulatory requirements. These\
	\ types include fiscal, appropriation, operational, and custody\
	\ measurements. \n\n- Fiscal measurements are used for tax and regulatory\
	\ reporting, ensuring accurate financial transactions based on measured quantities.\
	\ \n- Appropriation measurements track resource allocation and ownership\
	\ distribution among stakeholders. \n- Operational measurements support real-time\
	\ monitoring and process optimization within industrial operations. \n- **Custody\
	\ measurements** are essential for legal and contractual transactions, ensuring\
	\ precise handover of fluids between parties. \n\nThese classifications play\
	\ a crucial role in compliance, financial accuracy, and operational efficiency\
	\ across industries such as oil and gas, water management, and energy distribution.\
	\ "
	- 'What is a Meter Stream?

	A Meter Stream represents a measurement system configured within a flow computer.
	It serves as the interface between the physical measurement system and the computational
	processes that record and analyze flow data.


	Key Aspects of a Meter Stream:

	- Status: Indicates whether the meter stream is active or inactive.

	- Measurement System Association: Links the meter stream to a specific measurement
	system, ensuring that the data collected corresponds to a defined physical setup.

	- Flow Computer Association: Identifies the flow computer responsible for managing
	and recording the measurement system''s data.

	Why is a Meter Stream Important?

	A meter stream is a critical component in flow measurement, as it ensures
	that the measurement system is correctly integrated into the flow computer for
	accurate monitoring and reporting. Since each flow computer can handle multiple
	meter streams, proper configuration is essential for maintaining data integrity
	and traceability.'
	- source_sentence: PTE SUZANO
	sentences:
	- 'What are Flow Computer Types?

	Flow computer types categorize different models of flow computers used in measurement
	systems, such as OMNI, KROHNE, ROC, FC302, S600, FLOWBOSS, F407, F107, and ThermoFisher.
	Each type is defined by its capabilities, functionalities, and applications, determining
	how it processes measurement data, performs calculations, and enables real-time
	monitoring. Understanding these types is essential for selecting the right equipment
	to ensure precise flow measurement, system integration, and operational efficiency.'
	- '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).'
	- 'What is a Calibration Record?

	A Calibration Record documents the calibration process of a specific equipment
	tag, ensuring that its measurements remain accurate and reliable. Calibration
	is a critical process in maintaining measurement precision and compliance with
	standards.


	Key Aspects of a Calibration Record:

	- Calibration Date: The exact date when the calibration was performed, crucial
	for tracking maintenance schedules.

	- Certification Number: A unique identifier for the calibration certificate, providing
	traceability and verification of compliance.

	- Range Values: The minimum and maximum measurement values covered during the
	calibration process.

	- Calibration Status: Indicates whether the calibration was approved or saved
	for further review.

	- Associated Units: Specifies the measurement units used in calibration (e.g.,
	°C, psi).

	- Associated Equipment Tag ID: Links the calibration record to a specific equipment
	tag, ensuring traceability of measurement instruments.

	Calibration records play a fundamental role in quality assurance, helping maintain
	measurement integrity and regulatory compliance.'
	datasets:
	- Lauther/d4-embeddings
	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 [d4-embeddings](https://huggingface.co/datasets/Lauther/d4-embeddings) 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 274baa43b0e13e37fafa6428dbc7938e62e5c439 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [d4-embeddings](https://huggingface.co/datasets/Lauther/d4-embeddings)
	<!-- - 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/d4-embeddings-v2.0")
	# Run inference
	sentences = [
	'PTE SUZANO',
	'What is a Calibration Record?\nA Calibration Record documents the calibration process of a specific equipment tag, ensuring that its measurements remain accurate and reliable. Calibration is a critical process in maintaining measurement precision and compliance with standards.\n\nKey Aspects of a Calibration Record:\n- Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules.\n- Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance.\n- Range Values: The minimum and maximum measurement values covered during the calibration process.\n- Calibration Status: Indicates whether the calibration was approved or saved for further review.\n- Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi).\n- Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments.\nCalibration records play a fundamental role in quality assurance, helping maintain measurement integrity and regulatory compliance.',
	'What is a flow computer?\nA flow computer is a device used in measurement engineering. It collects analog and digital data from flow meters and other sensors.\n\nKey features of a flow computer:\n- It has a unique name, firmware version, and manufacturer information.\n- It is designed to record and process data such as temperature, pressure, and fluid volume (for gases or oils).',
	]
	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

	#### d4-embeddings

	* Dataset: [d4-embeddings](https://huggingface.co/datasets/Lauther/d4-embeddings) at [09fb8a5](https://huggingface.co/datasets/Lauther/d4-embeddings/tree/09fb8a5fa7b222693e3a536b5ee892a1948b740b)
	* Size: 11,165 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:---------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|:------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 8.23 tokens</li><li>max: 19 tokens</li></ul> \| <ul><li>min: 27 tokens</li><li>mean: 187.19 tokens</li><li>max: 406 tokens</li></ul> \| <ul><li>0: ~66.20%</li><li>1: ~33.80%</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:----------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>Ramal ESVOL - TEVOL (GASVOL 14")</code> \| <code>What is Equipment?<br>An Equipment represents a physical device that may be used within a measurement system. Equipment can be active or inactive and is classified by type, such as transmitters, thermometers, or other measurement-related devices.<br><br>Key Aspects of Equipment:<br>- Serial Number: A unique identifier assigned to each equipment unit for tracking and reference.<br>- Current State: Indicates whether the equipment is currently in use (ACT) or inactive (INA).<br>- Associated Equipment Type: Defines the category of the equipment (e.g., transmitter, thermometer), allowing classification and management.<br>Equipment plays a critical role in measurement systems, ensuring accuracy and reliability in data collection and processing.</code> \| <code>0</code> \|
	\| <code>Mol (%) CO</code> \| <code>What is an Equipment Tag?<br>An Equipment Tag is a unique label string identifier assigned to equipment that is actively installed and in use within a measurement system. It differentiates between equipment in general (which may be in storage or inactive) and equipment that is currently operational in a system.<br><br>Key Aspects of Equipment Tags:<br>- Equipment-Tag: A distinct label or identifier that uniquely marks the equipment in operation.<br>- Equipment ID: Links the tag to the corresponding equipment unit.<br>- Belonging Measurement System: Specifies which measurement system the tagged equipment is part of.<br>- Equipment Type Name: Classifies the equipment (e.g., transmitter, thermometer), aiding in organization and system integration.<br>The Equipment Tag is essential for tracking and managing operational equipment within a measurement system, ensuring proper identification, monitoring, and maintenance.</code> \| <code>0</code> \|
	\| <code>FQI-4715-1411</code> \| <code>What is a flow computer?<br>A flow computer is a device used in measurement engineering. It collects analog and digital data from flow meters and other sensors.<br><br>Key features of a flow computer:<br>- It has a unique name, firmware version, and manufacturer information.<br>- It is designed to record and process data such as temperature, pressure, and fluid volume (for gases or oils).</code> \| <code>0</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.5,
	"size_average": true
	}
	```

	### Evaluation Dataset

	#### d4-embeddings

	* Dataset: [d4-embeddings](https://huggingface.co/datasets/Lauther/d4-embeddings) at [09fb8a5](https://huggingface.co/datasets/Lauther/d4-embeddings/tree/09fb8a5fa7b222693e3a536b5ee892a1948b740b)
	* Size: 2,392 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:---------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|:------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 8.22 tokens</li><li>max: 19 tokens</li></ul> \| <ul><li>min: 27 tokens</li><li>mean: 183.06 tokens</li><li>max: 406 tokens</li></ul> \| <ul><li>0: ~66.30%</li><li>1: ~33.70%</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>PTE UTE JUIZ DE FORA (IGREJINHA) B</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>1</code> \|
	\| <code>measure type</code> \| <code>What is a Calibration Record?<br>A Calibration Record documents the calibration process of a specific equipment tag, ensuring that its measurements remain accurate and reliable. Calibration is a critical process in maintaining measurement precision and compliance with standards.<br><br>Key Aspects of a Calibration Record:<br>- Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules.<br>- Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance.<br>- Range Values: The minimum and maximum measurement values covered during the calibration process.<br>- Calibration Status: Indicates whether the calibration was approved or saved for further review.<br>- Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi).<br>- Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments.<br>Calibration r...</code> \| <code>0</code> \|
	\| <code>daily flow rate</code> \| <code>What is a Measured Magnitude Value?<br>A Measured Magnitude Value represents a DAILY recorded physical measurement of a variable within a monitored fluid. These values are essential for tracking system performance, analyzing trends, and ensuring accurate monitoring of fluid properties.<br><br>Key Aspects of a Measured Magnitude Value:<br>- Measurement Date: The timestamp indicating when the measurement was recorded.<br>- Measured Value: The daily numeric result of the recorded physical magnitude.<br>- Measurement System Association: Links the measured value to a specific measurement system responsible for capturing the data.<br>- Variable Association: Identifies the specific variable (e.g., temperature, pressure, flow rate) corresponding to the recorded value.<br>Measured magnitude values are crucial for real-time monitoring, historical analysis, and calibration processes within measurement systems.<br><br>Database advices:<br>This values also are in historics of a flow computer report. Although, to go directl...</code> \| <code>1</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.5,
	"size_average": true
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 12
	- `per_device_eval_batch_size`: 12
	- `gradient_accumulation_steps`: 8
	- `weight_decay`: 0.01
	- `max_grad_norm`: 0.5
	- `num_train_epochs`: 5
	- `lr_scheduler_type`: cosine
	- `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`: 12
	- `per_device_eval_batch_size`: 12
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 8
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 5e-05
	- `weight_decay`: 0.01
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 0.5
	- `num_train_epochs`: 5
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: None
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.4296 \| 50 \| 0.1345 \| - \|
	\| 0.8593 \| 100 \| 0.0512 \| - \|
	\| 1.2836 \| 150 \| 0.041 \| 0.0051 \|
	\| 1.7132 \| 200 \| 0.0344 \| - \|
	\| 2.1375 \| 250 \| 0.0324 \| - \|
	\| 2.5671 \| 300 \| 0.0284 \| 0.0038 \|
	\| 2.9968 \| 350 \| 0.0296 \| - \|
	\| 3.4211 \| 400 \| 0.0261 \| - \|
	\| 3.8507 \| 450 \| 0.0268 \| 0.0035 \|
	\| 4.2750 \| 500 \| 0.0244 \| - \|
	\| 4.7046 \| 550 \| 0.0249 \| - \|


	### Framework Versions
	- Python: 3.11.0
	- Sentence Transformers: 3.4.1
	- Transformers: 4.49.0
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.4.0
	- Datasets: 3.3.2
	- 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",
	}
	```

	#### ContrastiveLoss
	```bibtex
	@inproceedings{hadsell2006dimensionality,
	author={Hadsell, R. and Chopra, S. and LeCun, Y.},
	booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
	title={Dimensionality Reduction by Learning an Invariant Mapping},
	year={2006},
	volume={2},
	number={},
	pages={1735-1742},
	doi={10.1109/CVPR.2006.100}
	}
	```

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