Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,607 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:3630
+- loss:CoSENTLoss
+base_model: intfloat/multilingual-e5-large-instruct
+widget:
+- source_sentence: equipment database
+  sentences:
+  - "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
+    \ and reliability of results obtained from equipment or measurement systems. It\
+    \ quantifies the potential error or margin of error in measurements.\n\nTypes\
+    \ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
+    \ magnitudes (variables):\n    - Refers to the uncertainty of specific variables,\
+    \ such as temperature or pressure.\n    - It is calculated after calibrating a\
+    \ device or obtained from the **equipment** manufacturer's manual.\n    - This\
+    \ uncertainty serves as a starting point for further calculations related to the\
+    \ equipment.\n\n2. Uncertainty of the measurement system:\n    - Refers to the\
+    \ uncertainty calculated for the overall flow measurement.\n    - It depends on\
+    \ the uncertainties of the individual variables (magnitudes) and represents the\
+    \ combined margin of error for the entire system.\n\nKey points:\n- The uncertainties\
+    \ of magnitudes (variables) are the foundation for calculating the uncertainty\
+    \ of the measurement system. Think of them as the \"building blocks.\"\n- Do not\
+    \ confuse the two types of uncertainty:\n    - **Uncertainty of magnitudes/variables**:\
+    \ Specific to individual variables (e.g., temperature, pressure).\n    - **Uncertainty\
+    \ of the measurement system**: Specific to the overall flow measurement."
+  - 'What is a 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 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.'
+- source_sentence: transmitter calibration record
+  sentences:
+  - "What is a Measurement Type?\nMeasurement types define the classification of measurements\
+    \ used within a system based on their purpose and regulatory requirements. These\
+    \ types include **fiscal**, **appropriation**, **operational**, and **custody**\
+    \ measurements.  \n\n- **Fiscal measurements** are used for tax and regulatory\
+    \ reporting, ensuring accurate financial transactions based on measured quantities.\
+    \  \n- **Appropriation measurements** track resource allocation and ownership\
+    \ distribution among stakeholders.  \n- **Operational measurements** support real-time\
+    \ monitoring and process optimization within industrial operations.  \n- **Custody\
+    \ measurements** are essential for legal and contractual transactions, ensuring\
+    \ precise handover of fluids between parties.  \n\nThese classifications play\
+    \ a crucial role in compliance, financial accuracy, and operational efficiency\
+    \ across industries such as oil and gas, water management, and energy distribution.\
+    \  "
+  - 'What is a 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.'
+  - '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: most recent calibration
+  sentences:
+  - '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.'
+  - '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 report index or historic index?\nIndexes represent the recorded reports\
+    \ generated by flow computers, classified into two types: \n- **Hourly reports\
+    \ Index**: Store data for hourly events.\n- **Daily reports Index**: Strore data\
+    \ for daily events.\n\nThese 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.\n\nThe index is essential\
+    \ for locating specific values within the report."
+- source_sentence: measurement system tag EMED-3102-02-010
+  sentences:
+  - "What is a report index or historic index?\nIndexes represent the recorded reports\
+    \ generated by flow computers, classified into two types: \n- **Hourly reports\
+    \ Index**: Store data for hourly events.\n- **Daily reports Index**: Strore data\
+    \ for daily events.\n\nThese 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.\n\nThe index is essential\
+    \ for locating specific values within the report."
+  - "What is uncertainty?\nUncertainty is a measure of confidence in the precision\
+    \ and reliability of results obtained from equipment or measurement systems. It\
+    \ quantifies the potential error or margin of error in measurements.\n\nTypes\
+    \ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
+    \ magnitudes (variables):\n    - Refers to the uncertainty of specific variables,\
+    \ such as temperature or pressure.\n    - It is calculated after calibrating a\
+    \ device or obtained from the **equipment** manufacturer's manual.\n    - This\
+    \ uncertainty serves as a starting point for further calculations related to the\
+    \ equipment.\n\n2. Uncertainty of the measurement system:\n    - Refers to the\
+    \ uncertainty calculated for the overall flow measurement.\n    - It depends on\
+    \ the uncertainties of the individual variables (magnitudes) and represents the\
+    \ combined margin of error for the entire system.\n\nKey points:\n- The uncertainties\
+    \ of magnitudes (variables) are the foundation for calculating the uncertainty\
+    \ of the measurement system. Think of them as the \"building blocks.\"\n- Do not\
+    \ confuse the two types of uncertainty:\n    - **Uncertainty of magnitudes/variables**:\
+    \ Specific to individual variables (e.g., temperature, pressure).\n    - **Uncertainty\
+    \ of the measurement system**: Specific to the overall flow measurement."
+  - 'What is a Magnitude?
+
+    A magnitude/variable represents a physical magnitude measured by the system, such
+    as temperature, pressure, or volume. It plays a crucial role in monitoring and
+    analyzing system performance. Each variable has a status that indicates whether
+    it is active (ACT) or inactive (INA), ensuring proper identification and usage
+    within measurement processes.'
+- source_sentence: list of 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 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 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.'
+datasets:
+- Lauther/measuring-embeddings-v5
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [measuring-embeddings-v5](https://huggingface.co/datasets/Lauther/measuring-embeddings-v5) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision 84344a23ee1820ac951bc365f1e91d094a911763 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [measuring-embeddings-v5](https://huggingface.co/datasets/Lauther/measuring-embeddings-v5)
+<!-- - **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-v5.1")
+# Run inference
+sentences = [
+    'list of measurement systems',
+    '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.',
+    'What is a Calibration Record?\nA Calibration Record documents the calibration process of a specific equipment tag, ensuring that its measurements remain accurate and reliable. Calibration is a critical process in maintaining measurement precision and compliance with standards.\n\nKey Aspects of a Calibration Record:\n- Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules.\n- Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance.\n- Range Values: The minimum and maximum measurement values covered during the calibration process.\n- Calibration Status: Indicates whether the calibration was approved or saved for further review.\n- Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi).\n- Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments.\nCalibration records play a fundamental role in quality assurance, helping maintain measurement integrity and regulatory compliance.',
+]
+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-v5
+
+* Dataset: [measuring-embeddings-v5](https://huggingface.co/datasets/Lauther/measuring-embeddings-v5) at [90b5410](https://huggingface.co/datasets/Lauther/measuring-embeddings-v5/tree/90b5410f6050bbea9cde1fa30323b08505996cb7)
+* Size: 3,630 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: 7.46 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 181.99 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 0.95</li></ul> |
+* Samples:
+  | sentence1                                      | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | score              |
+  |:-----------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------|
+  | <code>measurement technology name</code>       | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br>    - Refers to the uncertainty of specific variables, such as temperature or pressure.<br>    - It is calculated after calibrating a device or obtained from the **equipment** manufacturer's manual.<br>    - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br>    - Refers to the uncertainty calculated for the overall flow measurement.<br>    - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...</code> | <code>0.001</code> |
+  | <code>transmitter calibration record</code>    | <code>What is an Uncertainty Curve Point?<br>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.<br><br>Key Aspects of an Uncertainty Curve Point:<br>- Uncertainty File ID: Links the point to the specific uncertainty dataset, ensuring traceability.<br>Equipment Tag ID: Identifies the equipment associated with the uncertainty measurement, crucial for system validation.<br>- Uncertainty Points: Represent uncertainty values recorded at specific conditions, forming part of the overall uncertainty curve.<br>- Flow Rate Points: Corresponding flow rate values at which the uncertainty was measured, essential for evaluating performance under varying operational conditions.<br>These points are fundamental for generating uncertainty curves, which are used in calibration, validation, and compliance assess...</code>                      | <code>0.001</code> |
+  | <code>measurement magnitude uncertainty</code> | <code>What is uncertainty?<br>Uncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br>    - Refers to the uncertainty of specific variables, such as temperature or pressure.<br>    - It is calculated after calibrating a device or obtained from the **equipment** manufacturer's manual.<br>    - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br>    - Refers to the uncertainty calculated for the overall flow measurement.<br>    - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty...</code> | <code>0.95</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-v5
+
+* Dataset: [measuring-embeddings-v5](https://huggingface.co/datasets/Lauther/measuring-embeddings-v5) at [90b5410](https://huggingface.co/datasets/Lauther/measuring-embeddings-v5/tree/90b5410f6050bbea9cde1fa30323b08505996cb7)
+* Size: 778 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 778 samples:
+  |         | sentence1                                                                        | sentence2                                                                            | score                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                               | float                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 7.44 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 184.77 tokens</li><li>max: 406 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 0.95</li></ul> |
+* Samples:
+  | sentence1                                   | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | score              |
+  |:--------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------|
+  | <code>measurement type</code>               | <code>What is an Equipment Class?<br>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.<br><br>Each Equipment Class groups related equipment under a common category. Examples include:<br><br>Primary → Main measurement device in a system.<br>Secondary → Supporting measurement device, often used for verification.<br>Tertiary → Additional measurement equipment.<br>Valves → Flow control devices used in the system.<br>By defining Equipment Classes, the system ensures proper identification, tracking, and management of measurement-related assets.</code>                                                                                                                                                                                 | <code>0.001</code> |
+  | <code>latest uncertainty result</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>0.75</code>  |
+  | <code>uncertainty calculation ID 593</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>0.95</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
+- `gradient_accumulation_steps`: 8
+- `learning_rate`: 5e-06
+- `weight_decay`: 0.01
+- `max_grad_norm`: 0.5
+- `num_train_epochs`: 20
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 8
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-06
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 0.5
+- `num_train_epochs`: 20
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 6.2467  | 350  | 5.4993        | -               |
+| 7.1233  | 400  | 5.1991        | -               |
+| 8.0     | 450  | 4.7573        | 0.6509          |
+| 8.8811  | 500  | 4.8783        | -               |
+| 9.7577  | 550  | 4.4897        | -               |
+| 10.6344 | 600  | 3.9524        | 0.6758          |
+| 11.5110 | 650  | 3.679         | -               |
+| 12.3877 | 700  | 3.4076        | -               |
+| 13.2643 | 750  | 3.8909        | 0.6588          |
+| 14.1410 | 800  | 3.1191        | -               |
+| 15.0176 | 850  | 3.5478        | -               |
+| 15.8987 | 900  | 3.2201        | 0.6553          |
+| 16.7753 | 950  | 3.3027        | -               |
+| 17.6520 | 1000 | 2.4874        | -               |
+| 18.5286 | 1050 | 2.9745        | 0.6615          |
+| 19.4053 | 1100 | 2.8649        | -               |
+
+
+### 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},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+  "architectures": [
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
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+  "position_embedding_type": "absolute",
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+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
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+    "transformers": "4.49.0",
+    "pytorch": "2.6.0+cu124"
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+  "similarity_fn_name": "cosine"
+}

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modules.json

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+  }
+]

sentence_bert_config.json

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+  "do_lower_case": false
+}

special_tokens_map.json

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