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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3075
- loss:CoSENTLoss
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
This is a [sentence-transformers](https://www.SBERT.net) model trained on the [measuring-embeddings-v4](https://huggingface.co/datasets/Lauther/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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [measuring-embeddings-v4](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/measuring-embeddings-v4.3")
# 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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### measuring-embeddings-v4
* Dataset: [measuring-embeddings-v4](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4) at [1e3ca2c](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4/tree/1e3ca2c224ad58d1cc57b797997231e22154e471)
* Size: 3,075 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 7.55 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 180.22 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 0.07</li><li>mean: 0.21</li><li>max: 0.95</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>last calibrated span</code> | <code>What are historical report values?<br>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.<br><br>Flow computer reports store two types of data values:<br><br>- **Hourly data values**: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on an hourly basis.<br>- **Daily data values**: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on a daily basis.<br>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.</code> | <code>0.1</code> |
| <code>flow computer configuration</code> | <code>What is a Measurement Type?<br>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. <br><br>- **Fiscal measurements** are used for tax and regulatory reporting, ensuring accurate financial transactions based on measured quantities. <br>- **Appropriation measurements** track resource allocation and ownership distribution among stakeholders. <br>- **Operational measurements** support real-time monitoring and process optimization within industrial operations. <br>- **Custody measurements** are essential for legal and contractual transactions, ensuring precise handover of fluids between parties. <br><br>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. </code> | <code>0.1</code> |
| <code>uncertainty certificate number</code> | <code>What is an Uncertainty Composition?<br>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.<br><br>Key Aspects of an Uncertainty Component:<br>- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.<br>- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.<br>- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.<br>Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.</code> | <code>0.1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### measuring-embeddings-v4
* Dataset: [measuring-embeddings-v4](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4) at [1e3ca2c](https://huggingface.co/datasets/Lauther/measuring-embeddings-v4/tree/1e3ca2c224ad58d1cc57b797997231e22154e471)
* Size: 659 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 659 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 7.63 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 186.36 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 0.07</li><li>mean: 0.2</li><li>max: 0.9</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>measurement system details</code> | <code>What is an Uncertainty Composition?<br>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.<br><br>Key Aspects of an Uncertainty Component:<br>- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.<br>- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.<br>- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.<br>Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.</code> | <code>0.15</code> |
| <code>measurement system tag EMED-3102-02-010</code> | <code>What is a report index or historic index?<br>Indexes represent the recorded reports generated by flow computers, classified into two types: <br>- **Hourly reports Index**: Store data for hourly events.<br>- **Daily reports Index**: Strore data for daily events.<br><br>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.<br><br>The index is essential for locating specific values within the report.</code> | <code>0.24</code> |
| <code>static pressure</code> | <code>What is a Meter Stream?<br>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.<br><br>Key Aspects of a Meter Stream:<br>- Status: Indicates whether the meter stream is active or inactive.<br>- Measurement System Association: Links the meter stream to a specific measurement system, ensuring that the data collected corresponds to a defined physical setup.<br>- Flow Computer Association: Identifies the flow computer responsible for managing and recording the measurement system's data.<br>Why is a Meter Stream Important?<br>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.</code> | <code>0.1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 4
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `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`: 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`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.8322 | 160 | 3.0564 | - |
| 0.8843 | 170 | 2.2963 | - |
| 0.9363 | 180 | 1.8767 | - |
| 0.9883 | 190 | 2.8634 | - |
| 1.0416 | 200 | 2.5195 | - |
| 1.0936 | 210 | 2.4094 | - |
| 1.1456 | 220 | 1.5141 | - |
| 1.1977 | 230 | 2.1366 | - |
| 1.2497 | 240 | 1.5389 | - |
| 1.3017 | 250 | 3.8265 | - |
| 1.3537 | 260 | 1.9989 | - |
| 1.4057 | 270 | 2.6037 | - |
| 1.4577 | 280 | 3.898 | - |
| 1.5098 | 290 | 2.9363 | - |
| 1.5618 | 300 | 3.3853 | 0.5155 |
| 1.6138 | 310 | 2.2995 | - |
| 1.6658 | 320 | 1.3945 | - |
| 1.7178 | 330 | 3.8312 | - |
| 1.7698 | 340 | 2.626 | - |
| 1.8218 | 350 | 1.5451 | - |
| 1.8739 | 360 | 1.1062 | - |
| 1.9259 | 370 | 2.6593 | - |
| 1.9779 | 380 | 1.773 | - |
| 2.0260 | 390 | 1.3937 | - |
| 2.0780 | 400 | 2.2228 | - |
| 2.1300 | 410 | 0.7027 | - |
| 2.1821 | 420 | 1.5933 | - |
| 2.2341 | 430 | 2.295 | - |
| 2.2861 | 440 | 1.042 | - |
| 2.3381 | 450 | 2.8671 | 0.3661 |
| 2.3901 | 460 | 1.879 | - |
| 2.4421 | 470 | 4.0556 | - |
| 2.4941 | 480 | 2.9677 | - |
| 2.5462 | 490 | 1.4443 | - |
| 2.5982 | 500 | 3.2575 | - |
| 2.6502 | 510 | 1.6124 | - |
| 2.7022 | 520 | 1.3976 | - |
| 2.7542 | 530 | 1.3161 | - |
| 2.8062 | 540 | 2.5047 | - |
| 2.8583 | 550 | 0.9757 | - |
| 2.9103 | 560 | 2.1051 | - |
| 2.9623 | 570 | 2.4919 | - |
| 3.0104 | 580 | 1.4737 | - |
| 3.0624 | 590 | 1.3318 | - |
| 3.1144 | 600 | 1.4474 | 0.4409 |
| 3.1664 | 610 | 2.3727 | - |
| 3.2185 | 620 | 0.6234 | - |
| 3.2705 | 630 | 1.9529 | - |
| 3.3225 | 640 | 1.5384 | - |
| 3.3745 | 650 | 1.5913 | - |
| 3.4265 | 660 | 0.6265 | - |
| 3.4785 | 670 | 2.1122 | - |
| 3.5306 | 680 | 1.8046 | - |
| 3.5826 | 690 | 0.8298 | - |
| 3.6346 | 700 | 1.4242 | - |
| 3.6866 | 710 | 1.5808 | - |
| 3.7386 | 720 | 1.1792 | - |
| 3.7906 | 730 | 2.7767 | - |
| 3.8427 | 740 | 1.7814 | - |
| 3.8947 | 750 | 0.5374 | 0.3227 |
| 3.9467 | 760 | 1.493 | - |
| 3.9987 | 770 | 1.8282 | - |
| 4.0468 | 780 | 1.6991 | - |
| 4.0988 | 790 | 0.7883 | - |
| 4.1508 | 800 | 0.841 | - |
| 4.2029 | 810 | 0.923 | - |
| 4.2549 | 820 | 0.3459 | - |
| 4.3069 | 830 | 2.3643 | - |
| 4.3589 | 840 | 0.9606 | - |
| 4.4109 | 850 | 0.7961 | - |
| 4.4629 | 860 | 1.749 | - |
| 4.5150 | 870 | 0.6536 | - |
| 4.5670 | 880 | 1.668 | - |
| 4.6190 | 890 | 0.5919 | - |
| 4.6710 | 900 | 1.2476 | 0.3258 |
| 4.7230 | 910 | 1.422 | - |
| 4.7750 | 920 | 0.8616 | - |
| 4.8270 | 930 | 0.2323 | - |
| 4.8791 | 940 | 2.7915 | - |
| 4.9311 | 950 | 0.6705 | - |
| 4.9831 | 960 | 1.7353 | - |
| 5.0312 | 970 | 1.7646 | - |
| 5.0832 | 980 | 1.4311 | - |
| 5.1352 | 990 | 0.7089 | - |
| 5.1873 | 1000 | 1.631 | - |
| 5.2393 | 1010 | 1.8051 | - |
| 5.2913 | 1020 | 0.5302 | - |
| 5.3433 | 1030 | 0.7428 | - |
| 5.3953 | 1040 | 0.5852 | - |
| 5.4473 | 1050 | 0.737 | 0.3283 |
| 5.4993 | 1060 | 1.492 | - |
| 5.5514 | 1070 | 0.9142 | - |
| 5.6034 | 1080 | 1.8887 | - |
| 5.6554 | 1090 | 1.1079 | - |
| 5.7074 | 1100 | 0.6984 | - |
| 5.7594 | 1110 | 1.7174 | - |
| 5.8114 | 1120 | 0.9411 | - |
| 5.8635 | 1130 | 1.286 | - |
| 5.9155 | 1140 | 2.1944 | - |
| 5.9675 | 1150 | 1.2478 | - |
| 6.0156 | 1160 | 0.7935 | - |
| 6.0676 | 1170 | 1.4886 | - |
| 6.1196 | 1180 | 1.3375 | - |
| 6.1717 | 1190 | 2.9167 | - |
| 6.2237 | 1200 | 0.3903 | 0.2734 |
| 6.2757 | 1210 | 1.326 | - |
| 6.3277 | 1220 | 0.3135 | - |
| 6.3797 | 1230 | 1.0881 | - |
| 6.4317 | 1240 | 1.5096 | - |
| 6.4837 | 1250 | 0.5525 | - |
| 6.5358 | 1260 | 0.3606 | - |
| 6.5878 | 1270 | 0.9334 | - |
| 6.6398 | 1280 | 0.5658 | - |
| 6.6918 | 1290 | 1.5978 | - |
| 6.7438 | 1300 | 0.4212 | - |
| 6.7958 | 1310 | 1.7793 | - |
| 6.8479 | 1320 | 1.5593 | - |
| 6.8999 | 1330 | 1.6738 | - |
| 6.9519 | 1340 | 0.3041 | - |
| 7.0 | 1350 | 0.5286 | 0.2737 |
| 7.0520 | 1360 | 1.7618 | - |
| 7.1040 | 1370 | 0.4629 | - |
| 7.1560 | 1380 | 0.4087 | - |
| 7.2081 | 1390 | 0.3099 | - |
| 7.2601 | 1400 | 0.6679 | - |
| 7.3121 | 1410 | 0.7688 | - |
| 7.3641 | 1420 | 1.223 | - |
| 7.4161 | 1430 | 0.8108 | - |
| 7.4681 | 1440 | 0.24 | - |
| 7.5202 | 1450 | 0.6616 | - |
| 7.5722 | 1460 | 1.5255 | - |
| 7.6242 | 1470 | 1.3865 | - |
| 7.6762 | 1480 | 0.2771 | - |
| 7.7282 | 1490 | 0.7809 | - |
| 7.7802 | 1500 | 0.2114 | 0.2259 |
| 7.8322 | 1510 | 1.6341 | - |
| 7.8843 | 1520 | 0.7665 | - |
| 7.9363 | 1530 | 0.7204 | - |
| 7.9883 | 1540 | 0.6557 | - |
| 8.0364 | 1550 | 2.0155 | - |
| 8.0884 | 1560 | 0.4718 | - |
| 8.1404 | 1570 | 0.1254 | - |
| 8.1925 | 1580 | 0.8067 | - |
| 8.2445 | 1590 | 0.3196 | - |
| 8.2965 | 1600 | 0.7162 | - |
| 8.3485 | 1610 | 0.1727 | - |
| 8.4005 | 1620 | 0.7634 | - |
| 8.4525 | 1630 | 0.2472 | - |
| 8.5046 | 1640 | 0.264 | - |
| 8.5566 | 1650 | 0.5994 | 0.1935 |
| 8.6086 | 1660 | 0.4445 | - |
| 8.6606 | 1670 | 0.9039 | - |
| 8.7126 | 1680 | 0.7927 | - |
| 8.7646 | 1690 | 0.4908 | - |
| 8.8166 | 1700 | 0.7486 | - |
| 8.8687 | 1710 | 1.377 | - |
| 8.9207 | 1720 | 1.025 | - |
| 8.9727 | 1730 | 1.1134 | - |
| 9.0208 | 1740 | 0.271 | - |
| 9.0728 | 1750 | 1.0931 | - |
| 9.1248 | 1760 | 0.7956 | - |
| 9.1769 | 1770 | 1.2794 | - |
| 9.2289 | 1780 | 0.3901 | - |
| 9.2809 | 1790 | 0.9033 | - |
| 9.3329 | 1800 | 0.4934 | 0.1680 |
| 9.3849 | 1810 | 0.5104 | - |
| 9.4369 | 1820 | 0.2879 | - |
| 9.4889 | 1830 | 0.6565 | - |
| 9.5410 | 1840 | 0.4523 | - |
| 9.5930 | 1850 | 0.7147 | - |
| 9.6450 | 1860 | 0.354 | - |
| 9.6970 | 1870 | 0.277 | - |
| 9.7490 | 1880 | 0.2066 | - |
| 9.8010 | 1890 | 0.6588 | - |
| 9.8531 | 1900 | 0.3789 | - |
| 9.9051 | 1910 | 0.8525 | - |
| 9.9571 | 1920 | 0.366 | - |
</details>
### 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",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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