metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:4151
  - loss:TripletLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
  - source_sentence: flow computer tags
    sentences:
      - >-
        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 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. 

        - 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."


        **IMPORTANT**: Do not confuse the two types of **Points**:
            - **Uncertainty Curve Point**: Specific to a measurement system uncertainty or uncertainty simulation or uncertainty curve.
            - **Calibration Point**: Specific to the calibration.
            - **Uncertainty values**: Do not confuse these uncertainty points with the single calculated uncertainty.
      - >-
        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 **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.


        Key Aspects of a Measured Magnitude Value:

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

        - Measured Value: The daily 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.


        Database advices:

        This values also are in **historics of a flow computer report**.
        Although, to go directly instead querying the flow computer report you
        can do it by going to the table of variables data in the database.
  - source_sentence: PTE CAPUAVA B
    sentences:
      - >-
        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.
      - >-
        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 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.  
  - source_sentence: PTE PARACAMBI A
    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 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.
      - >-
        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: PTE UTE BAIXADA FLUMINENSE A
    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 an Equipment Type?

        An Equipment Type defines a category of measurement or monitoring
        devices used in a system. Each type of equipment is classified based on
        its function, the physical magnitude it measures, and its associated
        measurement unit.


        Key Aspects of Equipment Types:

        - Categorization: Equipment types include devices like transmitters,
        thermometers, and other measurement instruments.

        - Classification: Equipment can be primary (directly involved in
        measurement) or secondary (supporting measurement processes).

        - Measurement Unit: Each equipment type is linked to a unit of measure
        (e.g., °C for temperature, psi for pressure).

        - Measured Magnitude: Defines what the equipment measures (e.g.,
        temperature, pressure, volume).

        Understanding equipment types ensures correct data interpretation,
        proper calibration, and accurate measurement within a system.
      - >-
        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: PTE BRAGANÇA PAULISTA C
    sentences:
      - >-
        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.
      - >-
        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 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.
datasets:
  - Lauther/d4-embeddings-TripletLoss
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 d4-embeddings-triplet_loss 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:
- d4-embeddings-triplet_loss

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/d4-embeddings-v3.0-tl")
# Run inference
sentences = [
    'PTE BRAGANÇA PAULISTA C',
    'What is an Uncertainty Composition?\nAn 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.\n\nKey Aspects of an Uncertainty Component:\n- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.\n- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.\n- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.\nUnderstanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.',
    'What is a Measurement Unit?\nA 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.\n\nEach measurement unit is associated with a specific magnitude, ensuring that values are correctly interpreted within their context. For example:\n\n- °C (Celsius) → Used for temperature\n- psi (pounds per square inch) → Used for pressure\n- m³ (cubic meters) → Used for volume\nMeasurement units are essential for maintaining consistency across recorded data, ensuring comparability, and enabling accurate calculations within measurement systems.',
]
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

d4-embeddings-triplet_loss

Dataset: d4-embeddings-triplet_loss at 4d11c52
Size: 4,151 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 3 tokens mean: 9.03 tokens max: 19 tokens	min: 80 tokens mean: 213.47 tokens max: 406 tokens	min: 27 tokens mean: 168.24 tokens max: 406 tokens

Samples:

anchor	positive	negative
`Orifice Diameter (mm)`	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 ...	What is an Equipment Class? An Equipment Class categorizes different types of equipment based on their function or role within a measurement system. This classification helps in organizing and distinguishing equipment types for operational, maintenance, and analytical purposes. Each Equipment Class groups related equipment under a common category. Examples include: Primary → Main measurement device in a system. Secondary → Supporting measurement device, often used for verification. Tertiary → Additional measurement equipment. Valves → Flow control devices used in the system. By defining Equipment Classes, the system ensures proper identification, tracking, and management of measurement-related assets.
`prueba_gonzalo`	`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.`	What is a Measured Magnitude Value? 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. Key Aspects of a Measured Magnitude Value: - Measurement Date: The timestamp indicating when the measurement was recorded. - Measured Value: The daily 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. Database advices: This values also are in historics of a flow computer report. Although, to go directl...
`Vazao Instantanea`	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 ...	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.

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 0.5
}

Evaluation Dataset

d4-embeddings-triplet_loss

Dataset: d4-embeddings-triplet_loss at 4d11c52
Size: 1,038 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 3 tokens mean: 9.02 tokens max: 19 tokens	min: 27 tokens mean: 209.21 tokens max: 406 tokens	min: 27 tokens mean: 172.36 tokens max: 406 tokens

Samples:

anchor	positive	negative
`FQI-4301.4522B`	`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.`	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...
`PTE GUARATINGUETA B`	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 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.
`PTE BRAGANÇA PAULISTA B`	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.	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...

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 0.5
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 80
per_device_eval_batch_size: 80
weight_decay: 0.01
max_grad_norm: 0.5
num_train_epochs: 10
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
dataloader_num_workers: 4

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 80
per_device_eval_batch_size: 80
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
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: 10
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: True
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: 4
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

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.2885	15	0.447	-
0.5769	30	0.1328	-
0.8654	45	0.0629	-
1.1538	60	0.027	-
1.4423	75	0.0258	-
1.7308	90	0.0296	-
2.0192	105	0.0168	-
2.3077	120	0.0155	-
2.5962	135	0.0183	-
2.8846	150	0.0106	0.0153
3.1731	165	0.0258	-
3.4615	180	0.0128	-
3.75	195	0.007	-
4.0385	210	0.0089	-
4.3269	225	0.0079	-
4.6154	240	0.0094	-
4.9038	255	0.0052	-
5.1923	270	0.0084	-
5.4808	285	0.0071	-
5.7692	300	0.0075	0.0098
6.0577	315	0.0057	-
6.3462	330	0.0048	-
6.6346	345	0.0037	-
6.9231	360	0.0053	-
7.2115	375	0.0039	-
7.5	390	0.0027	-
7.7885	405	0.0069	-
8.0769	420	0.0033	-
8.3654	435	0.0021	-
8.6538	450	0.0038	0.0086
8.9423	465	0.0034	-
9.2308	480	0.0034	-
9.5192	495	0.003	-
9.8077	510	0.0028	-

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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}