metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:3075
  - loss:CoSENTLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
  - source_sentence: last calibrated span
    sentences:
      - >-
        What is a Calibration Point?

        A Calibration Point represents a specific data entry in a calibration
        process, comparing an expected reference value to an actual measured
        value. These points are fundamental in ensuring measurement accuracy and
        identifying deviations.


        Key Aspects of Calibration Points:

        - Calibration Report Association: Each calibration point belongs to a
        specific calibration report, linking it to a broader calibration
        procedure.

        - Reference Values: Theoretical or expected values used as a benchmark
        for measurement validation.

        - Measured Values: The actual recorded values during calibration,
        reflecting the instrument’s response.

        - Errors: The difference between reference and measured values,
        indicating possible measurement inaccuracies.

        Calibration points are essential for evaluating instrument performance,
        ensuring compliance with standards, and maintaining measurement
        reliability.
      - >-
        What is Equipment?

        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.


        Key Aspects of Equipment:

        - Serial Number: A unique identifier assigned to each equipment unit for
        tracking and reference.

        - Current State: Indicates whether the equipment is currently in use
        (ACT) or inactive (INA).

        - Associated Equipment Type: Defines the category of the equipment
        (e.g., transmitter, thermometer), allowing classification and
        management.

        Equipment plays a critical role in measurement systems, ensuring
        accuracy and reliability in data collection and processing.
      - >-
        What is an Equipment Tag?

        An Equipment Tag is a unique 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: transmitter calibration record
    sentences:
      - >-
        What are historical report values?

        These represent the recorded data points within flow computer reports.
        Unlike the report index, which serves as a reference to locate reports,
        these values contain the actual measurements and calculated data stored
        in the historical records.


        Flow computer reports store two types of data values:


        - **Hourly data values**: Contain measured or calculated values (e.g.,
        operational minutes, alarms set, etc.) recorded on an hourly basis.

        - **Daily data values**: Contain measured or calculated values (e.g.,
        operational minutes, alarms set, etc.) recorded on a daily basis.

        Each value is directly linked to its respective report index, ensuring
        traceability to the original flow computer record. These values maintain
        their raw integrity, providing a reliable source for analysis and
        validation.
      - >-
        What is a Flow Computer Firmware?

        A flow computer firmware is a software component that defines the
        functionality and behavior of a flow computer.


        🔹 Key Characteristics:


        Each firmware version (e.g., F407, FB107, EMED-010) is linked to a
        specific flow computer model.

        Firmware versions can have a status indicating whether they are active
        or inactive.

        They determine how the flow computer processes measurements,
        calculations, and system operations.

        📌 Database Tip: When querying firmware information, ensure the firmware
        version is matched with the correct flow computer type for accurate
        results.
      - >-
        What is an Uncertainty Curve Point?

        An 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.


        Key Aspects of an Uncertainty Curve Point:

        - Uncertainty File ID: Links the point to the specific uncertainty
        dataset, ensuring traceability.

        Equipment Tag ID: Identifies the equipment associated with the
        uncertainty measurement, crucial for system validation.

        - Uncertainty Points: Represent uncertainty values recorded at specific
        conditions, forming part of the overall uncertainty curve.

        - Flow Rate Points: Corresponding flow rate values at which the
        uncertainty was measured, essential for evaluating performance under
        varying operational conditions.

        These points are fundamental for generating uncertainty curves, which
        are used in calibration, validation, and compliance assessments to
        ensure measurement reliability in industrial processes.
  - source_sentence: measurement systems
    sentences:
      - >-
        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 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 Measured Magnitude Value?

        A Measured Magnitude Value represents a 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.


        Key Aspects of a Measured Magnitude Value:

        - Measurement Date: The timestamp indicating when the measurement was
        recorded.

        - Measured Value: The actual numeric result of the recorded physical
        magnitude.

        - Measurement System Association: Links the measured value to a specific
        measurement system responsible for capturing the data.

        - Variable Association: Identifies the specific variable (e.g.,
        temperature, pressure, flow rate) corresponding to the recorded value.

        Measured magnitude values are crucial for real-time monitoring,
        historical analysis, and calibration processes within measurement
        systems.
  - source_sentence: measurement system tag
    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 an Equipment Tag?

        An Equipment Tag is a unique 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.
      - >-
        What is a measurement system?

        **Measurement systems** are essential components in industrial
        measurement and processing. They are identified by a unique **Tag** and
        are associated with a specific **installation** and **fluid type**.
        These systems utilize different **measurement technologies**, including
        **differential (DIF)** and **linear (LIN)**, depending on the
        application. Measurement systems can be classified based on their
        **application type**, such as **fiscal** or **custody transfer**.  
  - source_sentence: uncertainty points
    sentences:
      - >-
        What is a Calibration Point?

        A Calibration Point represents a specific data entry in a calibration
        process, comparing an expected reference value to an actual measured
        value. These points are fundamental in ensuring measurement accuracy and
        identifying deviations.


        Key Aspects of Calibration Points:

        - Calibration Report Association: Each calibration point belongs to a
        specific calibration report, linking it to a broader calibration
        procedure.

        - Reference Values: Theoretical or expected values used as a benchmark
        for measurement validation.

        - Measured Values: The actual recorded values during calibration,
        reflecting the instrument’s response.

        - Errors: The difference between reference and measured values,
        indicating possible measurement inaccuracies.

        Calibration points are essential for evaluating instrument performance,
        ensuring compliance with standards, and maintaining measurement
        reliability.
      - >-
        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 Fluid?

        A Fluid is the substance measured within a measurement system. It can be
        a gas or liquid, such as hydrocarbons, water, or other industrial
        fluids. Proper classification of fluids is essential for ensuring
        measurement accuracy, regulatory compliance, and operational efficiency.
        By identifying fluids correctly, the system applies the appropriate
        measurement techniques, processing methods, and reporting standards.
datasets:
  - Lauther/measuring-embeddings-v4
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on intfloat/multilingual-e5-large-instruct

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the measuring-embeddings-v4 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- measuring-embeddings-v4

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/measuring-embeddings-v4.1")
# Run inference
sentences = [
    'uncertainty points',
    'What is a Fluid?\nA Fluid is the substance measured within a measurement system. It can be a gas or liquid, such as hydrocarbons, water, or other industrial fluids. Proper classification of fluids is essential for ensuring measurement accuracy, regulatory compliance, and operational efficiency. By identifying fluids correctly, the system applies the appropriate measurement techniques, processing methods, and reporting standards.',
    'What is a Calibration Point?\nA Calibration Point represents a specific data entry in a calibration process, comparing an expected reference value to an actual measured value. These points are fundamental in ensuring measurement accuracy and identifying deviations.\n\nKey Aspects of Calibration Points:\n- Calibration Report Association: Each calibration point belongs to a specific calibration report, linking it to a broader calibration procedure.\n- Reference Values: Theoretical or expected values used as a benchmark for measurement validation.\n- Measured Values: The actual recorded values during calibration, reflecting the instrument’s response.\n- Errors: The difference between reference and measured values, indicating possible measurement inaccuracies.\nCalibration points are essential for evaluating instrument performance, ensuring compliance with standards, and maintaining measurement reliability.',
]
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]

Training Details

Training Dataset

measuring-embeddings-v4

Dataset: measuring-embeddings-v4 at 1e3ca2c
Size: 3,075 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 7.55 tokens
max: 17 tokens

min: 80 tokens
mean: 180.22 tokens
max: 406 tokens

min: 0.07
mean: 0.21
max: 0.95

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 7.55 tokens max: 17 tokens	min: 80 tokens mean: 180.22 tokens max: 406 tokens	min: 0.07 mean: 0.21 max: 0.95

Samples:

sentence1	sentence2	score
`last calibrated span`	What are historical report values? These represent the recorded data points within flow computer reports. Unlike the report index, which serves as a reference to locate reports, these values contain the actual measurements and calculated data stored in the historical records. Flow computer reports store two types of data values: - Hourly data values: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on an hourly basis. - Daily data values: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on a daily basis. Each value is directly linked to its respective report index, ensuring traceability to the original flow computer record. These values maintain their raw integrity, providing a reliable source for analysis and validation.	`0.1`
`flow computer configuration`	What is a Measurement Type? Measurement 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. - Fiscal measurements are used for tax and regulatory reporting, ensuring accurate financial transactions based on measured quantities. - Appropriation measurements track resource allocation and ownership distribution among stakeholders. - Operational measurements support real-time monitoring and process optimization within industrial operations. - Custody measurements are essential for legal and contractual transactions, ensuring precise handover of fluids between parties. These classifications play a crucial role in compliance, financial accuracy, and operational efficiency across industries such as oil and gas, water management, and energy distribution.	`0.1`
`uncertainty certificate number`	What is an Uncertainty Composition? An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty. Key Aspects of an Uncertainty Component: - Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system. - Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact. - Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation. Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.	`0.1`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Dataset

measuring-embeddings-v4

Dataset: measuring-embeddings-v4 at 1e3ca2c
Size: 659 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 659 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 7.63 tokens
max: 17 tokens

min: 80 tokens
mean: 186.36 tokens
max: 406 tokens

min: 0.07
mean: 0.2
max: 0.9

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 7.63 tokens max: 17 tokens	min: 80 tokens mean: 186.36 tokens max: 406 tokens	min: 0.07 mean: 0.2 max: 0.9

Samples:

sentence1	sentence2	score
`measurement system details`	What is an Uncertainty Composition? An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty. Key Aspects of an Uncertainty Component: - Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system. - Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact. - Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation. Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.	`0.15`
`measurement system tag EMED-3102-02-010`	What is a report index or historic index? Indexes represent the recorded reports generated by flow computers, classified into two types: - Hourly reports Index: Store data for hourly events. - Daily reports Index: Strore data for daily events. These reports, also referred to as historical data or flow computer historical records, contain raw, first-hand measurements directly collected from the flow computer. The data has not been processed or used in any calculations, preserving its original state for analysis or validation. The index is essential for locating specific values within the report.	`0.24`
`static pressure`	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.	`0.1`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 2e-05
num_train_epochs: 5
warmup_ratio: 0.1

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
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

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

@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

@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},
}