| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:7552 |
| | - loss:CoSENTLoss |
| | base_model: intfloat/multilingual-e5-large-instruct |
| | widget: |
| | - source_sentence: How are calibration points linked to equipment? |
| | sentences: |
| | - >- |
| | How are flow computers and measurement systems related? |
| | |
| | Flow computers can have multiple systems assigned to them. However, a |
| | measurement system can only be assigned to one flow computer. |
| |
|
| |
|
| | Database terminology: |
| |
|
| | In the database, this relationship is referred to as: |
| |
|
| | - Meter streams |
| |
|
| | - Meter runs |
| |
|
| | - Sections |
| |
|
| |
|
| | Storage of the relationship: |
| |
|
| | The relationship between a flow computer and its assigned measurement system |
| | is stored in a special table. |
| |
|
| |
|
| | User context: |
| |
|
| | When a user refers to a "meter stream," they are indicating that they are |
| | searching for a measurement system assigned to a specific flow computer. |
| | - >- |
| | How does a flow computer generate and store reports? |
| | |
| | A flow computer generates daily or hourly reports to provide users with |
| | operational data. These reports are stored in the flow computer's memory in |
| | an organized format. |
| |
|
| |
|
| | Report structure: |
| |
|
| | - Each report includes: |
| |
|
| | - Date and time of the data recording. |
| |
|
| | - Data recorded from flow computers. |
| |
|
| |
|
| | Data storage in tables: |
| |
|
| | The reports are saved in two tables: |
| |
|
| | 1. Main table (Index): |
| | - Stores the date, time, and flow computer identifier. |
| | 2. Detail table: |
| | - Stores the measured values associated with the report. |
| |
|
| | Connection to the Modbus table: |
| |
|
| | The flow computer's reports are linked to a Modbus table. This table |
| | contains the names corresponding to each value in the reports, making it |
| | easier to interpret the data. |
| | - >- |
| | 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. |
| |
|
| | How to retrieve uncertainty data: |
| |
|
| | - To find the uncertainty of the measurement system, join the measurement |
| | systems table with the uncertainty of the measurement system table. |
| |
|
| | - To find the uncertainty of a specific variable (magnitude), join the |
| | measurement systems table with the uncertainty of magnitudes (variables) |
| | table. |
| |
|
| |
|
| | Important note: |
| |
|
| | Do not confuse the two types of uncertainty: |
| |
|
| | - If the user requests the uncertainty of the measurement system, use the |
| | first join (measurement systems table + uncertainty of the measurement |
| | system table). |
| |
|
| | - If the user requests the uncertainty of a specific variable (magnitude) in |
| | a report, use the second join (measurement systems table + uncertainty of |
| | magnitudes table). |
| | - source_sentence: What is the primary key of the flow computer table? |
| | sentences: |
| | - >- |
| | What is equipment calibration? |
| | |
| | Calibration is a metrological verification process used to ensure the |
| | accuracy of measurement equipment. It is performed periodically, based on |
| | intervals set by the company or a regulatory body. |
| |
|
| |
|
| | Purpose of calibration: |
| |
|
| | The calibration process corrects any deviations in how the equipment |
| | measures physical magnitudes (variables). This ensures the equipment |
| | provides accurate and reliable data. |
| |
|
| |
|
| | Calibration cycles: |
| |
|
| | There are two main calibration cycles: |
| |
|
| | 1. As-found: Represents the equipment's measurement accuracy before any |
| | adjustments are made. This cycle is almost always implemented. |
| |
|
| | 2. As-left: Represents the equipment's measurement accuracy after |
| | adjustments are made. This cycle is used depending on regulatory |
| | requirements. |
| |
|
| |
|
| | Calibration uncertainty: |
| |
|
| | - Uncertainty is included in the results of a calibration. |
| |
|
| | - Calibration uncertainty refers to the margin of error in the device's |
| | measurements, which also affects the uncertainty of the measured variable or |
| | magnitude. |
| | - >- |
| | What is equipment calibration? |
| | |
| | Calibration is a metrological verification process used to ensure the |
| | accuracy of measurement equipment. It is performed periodically, based on |
| | intervals set by the company or a regulatory body. |
| |
|
| |
|
| | Purpose of calibration: |
| |
|
| | The calibration process corrects any deviations in how the equipment |
| | measures physical magnitudes (variables). This ensures the equipment |
| | provides accurate and reliable data. |
| |
|
| |
|
| | Calibration cycles: |
| |
|
| | There are two main calibration cycles: |
| |
|
| | 1. As-found: Represents the equipment's measurement accuracy before any |
| | adjustments are made. This cycle is almost always implemented. |
| |
|
| | 2. As-left: Represents the equipment's measurement accuracy after |
| | adjustments are made. This cycle is used depending on regulatory |
| | requirements. |
| |
|
| |
|
| | Calibration uncertainty: |
| |
|
| | - Uncertainty is included in the results of a calibration. |
| |
|
| | - Calibration uncertainty refers to the margin of error in the device's |
| | measurements, which also affects the uncertainty of the measured variable or |
| | magnitude. |
| | - >- |
| | How does a flow computer generate and store reports? |
| | |
| | A flow computer generates daily or hourly reports to provide users with |
| | operational data. These reports are stored in the flow computer's memory in |
| | an organized format. |
| |
|
| |
|
| | Report structure: |
| |
|
| | - Each report includes: |
| |
|
| | - Date and time of the data recording. |
| |
|
| | - Data recorded from flow computers. |
| |
|
| |
|
| | Data storage in tables: |
| |
|
| | The reports are saved in two tables: |
| |
|
| | 1. Main table (Index): |
| | - Stores the date, time, and flow computer identifier. |
| | 2. Detail table: |
| | - Stores the measured values associated with the report. |
| |
|
| | Connection to the Modbus table: |
| |
|
| | The flow computer's reports are linked to a Modbus table. This table |
| | contains the names corresponding to each value in the reports, making it |
| | easier to interpret the data. |
| | - source_sentence: >- |
| | Can you provide a sample query to test the retrieval of the uncertainty |
| | result for the specified tag and date? |
| | sentences: |
| | - >- |
| | What is equipment calibration? |
| | |
| | Calibration is a metrological verification process used to ensure the |
| | accuracy of measurement equipment. It is performed periodically, based on |
| | intervals set by the company or a regulatory body. |
| |
|
| |
|
| | Purpose of calibration: |
| |
|
| | The calibration process corrects any deviations in how the equipment |
| | measures physical magnitudes (variables). This ensures the equipment |
| | provides accurate and reliable data. |
| |
|
| |
|
| | Calibration cycles: |
| |
|
| | There are two main calibration cycles: |
| |
|
| | 1. As-found: Represents the equipment's measurement accuracy before any |
| | adjustments are made. This cycle is almost always implemented. |
| |
|
| | 2. As-left: Represents the equipment's measurement accuracy after |
| | adjustments are made. This cycle is used depending on regulatory |
| | requirements. |
| |
|
| |
|
| | Calibration uncertainty: |
| |
|
| | - Uncertainty is included in the results of a calibration. |
| |
|
| | - Calibration uncertainty refers to the margin of error in the device's |
| | measurements, which also affects the uncertainty of the measured variable or |
| | magnitude. |
| | - >- |
| | What kind of data store an equipment? |
| | |
| | Equipments can capture meteorological data, such as pressure, temperature, |
| | and volume (magnitudes). This data is essential for users to perform various |
| | calculations. |
| |
|
| |
|
| | Data storage: |
| |
|
| | - The measured values are stored in a special table in the database for |
| | magnitudes. This table contains the values of the variables captured by the |
| | equipments. |
| |
|
| | - These values are **direct measurements** from the fluid (e.g., raw |
| | pressure, temperature, or volume readings). **They are not calculated |
| | values**, such as uncertainty. |
| |
|
| | - The values stored in the variable values table are **different** from |
| | variable uncertainty values, which are calculated separately and represent |
| | the margin of error. |
| |
|
| |
|
| | Accessing the data: |
| |
|
| | - Users typically access the data by referring to the readings from the |
| | measurement system, not directly from the individual equipments. |
| |
|
| | - The readings are stored in a "variable values" table within the database. |
| |
|
| |
|
| | Linking variable names: |
| |
|
| | If the user needs to know the name of a variable, they must link the data to |
| | another table that stores information about the types of variables. |
| | - >- |
| | 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. |
| |
|
| | How to retrieve uncertainty data: |
| |
|
| | - To find the uncertainty of the measurement system, join the measurement |
| | systems table with the uncertainty of the measurement system table. |
| |
|
| | - To find the uncertainty of a specific variable (magnitude), join the |
| | measurement systems table with the uncertainty of magnitudes (variables) |
| | table. |
| |
|
| |
|
| | Important note: |
| |
|
| | Do not confuse the two types of uncertainty: |
| |
|
| | - If the user requests the uncertainty of the measurement system, use the |
| | first join (measurement systems table + uncertainty of the measurement |
| | system table). |
| |
|
| | - If the user requests the uncertainty of a specific variable (magnitude) in |
| | a report, use the second join (measurement systems table + uncertainty of |
| | magnitudes table). |
| | - source_sentence: How are the secondary equipment and measurement system related? |
| | sentences: |
| | - >- |
| | What kind of data store an equipment? |
| | |
| | Equipments can capture meteorological data, such as pressure, temperature, |
| | and volume (magnitudes). This data is essential for users to perform various |
| | calculations. |
| |
|
| |
|
| | Data storage: |
| |
|
| | - The measured values are stored in a special table in the database for |
| | magnitudes. This table contains the values of the variables captured by the |
| | equipments. |
| |
|
| | - These values are **direct measurements** from the fluid (e.g., raw |
| | pressure, temperature, or volume readings). **They are not calculated |
| | values**, such as uncertainty. |
| |
|
| | - The values stored in the variable values table are **different** from |
| | variable uncertainty values, which are calculated separately and represent |
| | the margin of error. |
| |
|
| |
|
| | Accessing the data: |
| |
|
| | - Users typically access the data by referring to the readings from the |
| | measurement system, not directly from the individual equipments. |
| |
|
| | - The readings are stored in a "variable values" table within the database. |
| |
|
| |
|
| | Linking variable names: |
| |
|
| | If the user needs to know the name of a variable, they must link the data to |
| | another table that stores information about the types of variables. |
| | - >- |
| | What do measurement equipment measure? |
| | |
| | Each equipment measures a physical magnitude, also known as a variable. |
| | Based on the type of variable they measure, devices are classified into |
| | different categories. |
| |
|
| |
|
| | Equipment classification: |
| |
|
| | - Primary meter: Assigned by default to equipments like orifice plates. |
| |
|
| | - Secondary meter: Assigned by default to equipments like transmitters. |
| |
|
| | - Tertiary meter: Used for other types of equipments. |
| |
|
| |
|
| | Equipment types in the database: |
| |
|
| | The database includes a table listing all equipment types. Examples of |
| | equipment types are: |
| |
|
| | - Differential pressure transmitters |
| |
|
| | - RTDs (Resistance Temperature Detectors) |
| |
|
| | - Orifice plates |
| |
|
| | - Multivariable transmitters |
| |
|
| | - Ultrasonic meters |
| |
|
| |
|
| | Meteorological checks for equipments: |
| |
|
| | Each equipment type is assigned a meteorological check, which can be either: |
| |
|
| | - Calibration: To ensure measurement accuracy. |
| |
|
| | - Inspection: To verify proper functioning. |
| |
|
| |
|
| | Data storage in tables: |
| |
|
| | The database also includes a separate table for equipment classifications, |
| | which are: |
| |
|
| | - Primary meter |
| |
|
| | - Secondary meter |
| |
|
| | - Tertiary meter |
| |
|
| | So, an equipment has equipment types and this types has classifications. |
| | - >- |
| | What kind of data store an equipment? |
| | |
| | Equipments can capture meteorological data, such as pressure, temperature, |
| | and volume (magnitudes). This data is essential for users to perform various |
| | calculations. |
| |
|
| |
|
| | Data storage: |
| |
|
| | - The measured values are stored in a special table in the database for |
| | magnitudes. This table contains the values of the variables captured by the |
| | equipments. |
| |
|
| | - These values are **direct measurements** from the fluid (e.g., raw |
| | pressure, temperature, or volume readings). **They are not calculated |
| | values**, such as uncertainty. |
| |
|
| | - The values stored in the variable values table are **different** from |
| | variable uncertainty values, which are calculated separately and represent |
| | the margin of error. |
| |
|
| |
|
| | Accessing the data: |
| |
|
| | - Users typically access the data by referring to the readings from the |
| | measurement system, not directly from the individual equipments. |
| |
|
| | - The readings are stored in a "variable values" table within the database. |
| |
|
| |
|
| | Linking variable names: |
| |
|
| | If the user needs to know the name of a variable, they must link the data to |
| | another table that stores information about the types of variables. |
| | - source_sentence: What is the table structure for secondary equipment? |
| | sentences: |
| | - >- |
| | What kind of data store an equipment? |
| | |
| | Equipments can capture meteorological data, such as pressure, temperature, |
| | and volume (magnitudes). This data is essential for users to perform various |
| | calculations. |
| |
|
| |
|
| | Data storage: |
| |
|
| | - The measured values are stored in a special table in the database for |
| | magnitudes. This table contains the values of the variables captured by the |
| | equipments. |
| |
|
| | - These values are **direct measurements** from the fluid (e.g., raw |
| | pressure, temperature, or volume readings). **They are not calculated |
| | values**, such as uncertainty. |
| |
|
| | - The values stored in the variable values table are **different** from |
| | variable uncertainty values, which are calculated separately and represent |
| | the margin of error. |
| |
|
| |
|
| | Accessing the data: |
| |
|
| | - Users typically access the data by referring to the readings from the |
| | measurement system, not directly from the individual equipments. |
| |
|
| | - The readings are stored in a "variable values" table within the database. |
| |
|
| |
|
| | Linking variable names: |
| |
|
| | If the user needs to know the name of a variable, they must link the data to |
| | another table that stores information about the types of variables. |
| | - >- |
| | How are flow computers and measurement systems related? |
| | |
| | Flow computers can have multiple systems assigned to them. However, a |
| | measurement system can only be assigned to one flow computer. |
| |
|
| |
|
| | Database terminology: |
| |
|
| | In the database, this relationship is referred to as: |
| |
|
| | - Meter streams |
| |
|
| | - Meter runs |
| |
|
| | - Sections |
| |
|
| |
|
| | Storage of the relationship: |
| |
|
| | The relationship between a flow computer and its assigned measurement system |
| | is stored in a special table. |
| |
|
| |
|
| | User context: |
| |
|
| | When a user refers to a "meter stream," they are indicating that they are |
| | searching for a measurement system assigned to a specific flow computer. |
| | - >- |
| | How are flow computers and measurement systems related? |
| | |
| | Flow computers can have multiple systems assigned to them. However, a |
| | measurement system can only be assigned to one flow computer. |
| |
|
| |
|
| | Database terminology: |
| |
|
| | In the database, this relationship is referred to as: |
| |
|
| | - Meter streams |
| |
|
| | - Meter runs |
| |
|
| | - Sections |
| |
|
| |
|
| | Storage of the relationship: |
| |
|
| | The relationship between a flow computer and its assigned measurement system |
| | is stored in a special table. |
| |
|
| |
|
| | User context: |
| |
|
| | When a user refers to a "meter stream," they are indicating that they are |
| | searching for a measurement system assigned to a specific flow computer. |
| | datasets: |
| | - Lauther/measuring-embeddings-v3 |
| | 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-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) 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 c9e87c786ffac96aeaeb42863276930883923ecb --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 1024 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) |
| | <!-- - **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-v3-multilingual-e5-large-instruct-20e") |
| | # Run inference |
| | sentences = [ |
| | 'What is the table structure for secondary equipment?', |
| | 'How are flow computers and measurement systems related?\nFlow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.\n\nDatabase terminology:\nIn the database, this relationship is referred to as:\n- Meter streams\n- Meter runs\n- Sections\n\nStorage of the relationship:\nThe relationship between a flow computer and its assigned measurement system is stored in a special table.\n\nUser context:\nWhen a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.', |
| | 'What kind of data store an equipment?\nEquipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.\n\nData storage:\n- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.\n- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.\n- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.\n\nAccessing the data:\n- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.\n- The readings are stored in a "variable values" table within the database.\n\nLinking variable names:\nIf the user needs to know the name of a variable, they must link the data to another table that stores information about the types of variables.', |
| | ] |
| | 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-v3 |
| |
|
| | * Dataset: [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) at [1b3cbbe](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3/tree/1b3cbbeb70b63338110491cd3de2950fb40b4f87) |
| | * Size: 7,552 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: 9 tokens</li><li>mean: 15.96 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 255.56 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.22</li><li>max: 0.95</li></ul> | |
| | * Samples: |
| | | sentence1 | sentence2 | score | |
| | |:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------| |
| | | <code>How can I combine the sub-query with the main query to fetch the last uncertainty report?</code> | <code>What do measurement equipment measure?<br>Each equipment measures a physical magnitude, also known as a variable. Based on the type of variable they measure, devices are classified into different categories.<br><br>Equipment classification:<br>- Primary meter: Assigned by default to equipments like orifice plates.<br>- Secondary meter: Assigned by default to equipments like transmitters.<br>- Tertiary meter: Used for other types of equipments.<br><br>Equipment types in the database:<br>The database includes a table listing all equipment types. Examples of equipment types are:<br>- Differential pressure transmitters<br>- RTDs (Resistance Temperature Detectors)<br>- Orifice plates<br>- Multivariable transmitters<br>- Ultrasonic meters<br><br>Meteorological checks for equipments:<br>Each equipment type is assigned a meteorological check, which can be either:<br>- Calibration: To ensure measurement accuracy.<br>- Inspection: To verify proper functioning.<br><br>Data storage in tables:<br>The database also includes a separate table for equipment classific...</code> | <code>0.1</code> | |
| | | <code>What is the column name for the calibration date in the calibration table?</code> | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.</code> | <code>0.1</code> | |
| | | <code>What is the name of the table that contains the flow computer tags?</code> | <code>What is equipment calibration?<br>Calibration is a metrological verification process used to ensure the accuracy of measurement equipment. It is performed periodically, based on intervals set by the company or a regulatory body.<br><br>Purpose of calibration:<br>The calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data.<br><br>Calibration cycles:<br>There are two main calibration cycles:<br>1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented.<br>2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements.<br><br>Calibration uncertainty:<br>- Uncertainty is included in the results of a calibration.<br>- Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or ...</code> | <code>0.05</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-v3 |
| |
|
| | * Dataset: [measuring-embeddings-v3](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3) at [1b3cbbe](https://huggingface.co/datasets/Lauther/measuring-embeddings-v3/tree/1b3cbbeb70b63338110491cd3de2950fb40b4f87) |
| | * Size: 1,618 evaluation 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: 9 tokens</li><li>mean: 15.83 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 250.41 tokens</li><li>max: 512 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>Identify any additional tables or columns that might be needed for the query.</code> | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>When a user refers to a "meter stream," they are indicating that they are searching for a measurement system assigned to a specific flow computer.</code> | <code>0.2</code> | |
| | | <code>What columns in these tables contain the measurement system tag and the flow computer tag?</code> | <code>How does a flow computer generate and store reports?<br>A flow computer generates daily or hourly reports to provide users with operational data. These reports are stored in the flow computer's memory in an organized format.<br><br>Report structure:<br>- Each report includes:<br>- Date and time of the data recording.<br>- Data recorded from flow computers.<br><br>Data storage in tables:<br>The reports are saved in two tables:<br>1. Main table (Index):<br> - Stores the date, time, and flow computer identifier.<br>2. Detail table:<br> - Stores the measured values associated with the report.<br><br>Connection to the Modbus table:<br>The flow computer's reports are linked to a Modbus table. This table contains the names corresponding to each value in the reports, making it easier to interpret the data.</code> | <code>0.1</code> | |
| | | <code>Identify the column that stores the calibration number.</code> | <code>What kind of data store an equipment?<br>Equipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.<br><br>Data storage:<br>- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.<br>- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.<br>- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.<br><br>Accessing the data:<br>- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.<br>- The readings are stored in a "variable values" table within the database.<br><br>Linking variable names:<br>If the user needs to kno...</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`: 7 |
| | - `per_device_eval_batch_size`: 7 |
| | - `gradient_accumulation_steps`: 4 |
| | - `learning_rate`: 3e-05 |
| | - `num_train_epochs`: 20 |
| | - `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`: 7 |
| | - `per_device_eval_batch_size`: 7 |
| | - `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`: 3e-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`: 20 |
| | - `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 | |
| | |:-------:|:----:|:-------------:|:---------------:| |
| | | 9.5153 | 2560 | 6.782 | - | |
| | | 9.5524 | 2570 | 7.3027 | - | |
| | | 9.5894 | 2580 | 7.3348 | - | |
| | | 9.6265 | 2590 | 7.7864 | - | |
| | | 9.6636 | 2600 | 6.3552 | - | |
| | | 9.7006 | 2610 | 7.151 | - | |
| | | 9.7377 | 2620 | 6.1664 | - | |
| | | 9.7748 | 2630 | 6.0398 | - | |
| | | 9.8119 | 2640 | 7.0452 | - | |
| | | 9.8489 | 2650 | 7.2457 | - | |
| | | 9.8860 | 2660 | 6.7531 | - | |
| | | 9.9231 | 2670 | 6.7149 | - | |
| | | 9.9601 | 2680 | 6.4635 | - | |
| | | 9.9972 | 2690 | 6.2237 | - | |
| | | 10.0371 | 2700 | 6.1798 | 2.9939 | |
| | | 10.0741 | 2710 | 7.2224 | - | |
| | | 10.1112 | 2720 | 6.5327 | - | |
| | | 10.1483 | 2730 | 7.4686 | - | |
| | | 10.1854 | 2740 | 6.1404 | - | |
| | | 10.2224 | 2750 | 7.0005 | - | |
| | | 10.2595 | 2760 | 5.7726 | - | |
| | | 10.2966 | 2770 | 6.5327 | - | |
| | | 10.3336 | 2780 | 7.5015 | - | |
| | | 10.3707 | 2790 | 6.5526 | - | |
| | | 10.4078 | 2800 | 6.2078 | - | |
| | | 10.4449 | 2810 | 6.1 | - | |
| | | 10.4819 | 2820 | 7.1027 | - | |
| | | 10.5190 | 2830 | 8.639 | - | |
| | | 10.5561 | 2840 | 6.9937 | - | |
| | | 10.5931 | 2850 | 7.2734 | 2.8532 | |
| | | 10.6302 | 2860 | 7.6321 | - | |
| | | 10.6673 | 2870 | 7.5788 | - | |
| | | 10.7044 | 2880 | 6.7864 | - | |
| | | 10.7414 | 2890 | 7.4237 | - | |
| | | 10.7785 | 2900 | 6.9813 | - | |
| | | 10.8156 | 2910 | 6.6884 | - | |
| | | 10.8526 | 2920 | 6.7464 | - | |
| | | 10.8897 | 2930 | 7.7989 | - | |
| | | 10.9268 | 2940 | 7.3568 | - | |
| | | 10.9639 | 2950 | 8.6706 | - | |
| | | 11.0 | 2960 | 6.5687 | - | |
| | | 11.0371 | 2970 | 5.8992 | - | |
| | | 11.0741 | 2980 | 6.4543 | - | |
| | | 11.1112 | 2990 | 6.1386 | - | |
| | | 11.1483 | 3000 | 6.9047 | 2.9147 | |
| | | 11.1854 | 3010 | 7.405 | - | |
| | | 11.2224 | 3020 | 7.5441 | - | |
| | | 11.2595 | 3030 | 6.7524 | - | |
| | | 11.2966 | 3040 | 7.698 | - | |
| | | 11.3336 | 3050 | 7.6167 | - | |
| | | 11.3707 | 3060 | 7.1516 | - | |
| | | 11.4078 | 3070 | 6.7458 | - | |
| | | 11.4449 | 3080 | 6.7608 | - | |
| | | 11.4819 | 3090 | 7.1508 | - | |
| | | 11.5190 | 3100 | 6.9155 | - | |
| | | 11.5561 | 3110 | 6.6664 | - | |
| | | 11.5931 | 3120 | 8.3841 | - | |
| | | 11.6302 | 3130 | 7.1934 | - | |
| | | 11.6673 | 3140 | 6.9681 | - | |
| | | 11.7044 | 3150 | 7.2187 | 2.7509 | |
| | | 11.7414 | 3160 | 7.3155 | - | |
| | | 11.7785 | 3170 | 7.3103 | - | |
| | | 11.8156 | 3180 | 7.1959 | - | |
| | | 11.8526 | 3190 | 6.8164 | - | |
| | | 11.8897 | 3200 | 7.5836 | - | |
| | | 11.9268 | 3210 | 5.2671 | - | |
| | | 11.9639 | 3220 | 6.4929 | - | |
| | | 12.0 | 3230 | 7.0892 | - | |
| | | 12.0371 | 3240 | 7.0877 | - | |
| | | 12.0741 | 3250 | 5.8302 | - | |
| | | 12.1112 | 3260 | 5.6145 | - | |
| | | 12.1483 | 3270 | 6.5808 | - | |
| | | 12.1854 | 3280 | 6.6826 | - | |
| | | 12.2224 | 3290 | 5.9819 | - | |
| | | 12.2595 | 3300 | 6.68 | 3.0175 | |
| | | 12.2966 | 3310 | 6.1685 | - | |
| | | 12.3336 | 3320 | 6.4473 | - | |
| | | 12.3707 | 3330 | 6.3965 | - | |
| | | 12.4078 | 3340 | 6.6278 | - | |
| | | 12.4449 | 3350 | 5.4575 | - | |
| | | 12.4819 | 3360 | 7.3019 | - | |
| | | 12.5190 | 3370 | 7.4843 | - | |
| | | 12.5561 | 3380 | 6.709 | - | |
| | | 12.5931 | 3390 | 6.7168 | - | |
| | | 12.6302 | 3400 | 7.0223 | - | |
| | | 12.6673 | 3410 | 6.5089 | - | |
| | | 12.7044 | 3420 | 6.5094 | - | |
| | | 12.7414 | 3430 | 7.2317 | - | |
| | | 12.7785 | 3440 | 6.6885 | - | |
| | | 12.8156 | 3450 | 6.9693 | 2.8462 | |
| | | 12.8526 | 3460 | 6.8242 | - | |
| | | 12.8897 | 3470 | 6.6899 | - | |
| | | 12.9268 | 3480 | 6.9113 | - | |
| | | 12.9639 | 3490 | 7.1903 | - | |
| | | 13.0 | 3500 | 7.3286 | - | |
| | | 13.0371 | 3510 | 6.5465 | - | |
| | | 13.0741 | 3520 | 5.6804 | - | |
| | | 13.1112 | 3530 | 5.6412 | - | |
| | | 13.1483 | 3540 | 6.6161 | - | |
| | | 13.1854 | 3550 | 5.761 | - | |
| | | 13.2224 | 3560 | 5.5669 | - | |
| | | 13.2595 | 3570 | 5.6184 | - | |
| | | 13.2966 | 3580 | 6.2996 | - | |
| | | 13.3336 | 3590 | 4.99 | - | |
| | | 13.3707 | 3600 | 5.9974 | 3.2358 | |
| | | 13.4078 | 3610 | 5.6962 | - | |
| | | 13.4449 | 3620 | 6.3662 | - | |
| | | 13.4819 | 3630 | 7.0398 | - | |
| | | 13.5190 | 3640 | 7.7358 | - | |
| | | 13.5561 | 3650 | 7.9063 | - | |
| | | 13.5931 | 3660 | 5.7823 | - | |
| | | 13.6302 | 3670 | 6.9861 | - | |
| | | 13.6673 | 3680 | 7.2855 | - | |
| | | 13.7044 | 3690 | 5.6785 | - | |
| | | 13.7414 | 3700 | 6.4071 | - | |
| | | 13.7785 | 3710 | 6.4294 | - | |
| | | 13.8156 | 3720 | 6.0842 | - | |
| | | 13.8526 | 3730 | 5.9422 | - | |
| | | 13.8897 | 3740 | 7.0778 | - | |
| | | 13.9268 | 3750 | 8.1597 | 3.0093 | |
| | | 13.9639 | 3760 | 6.3154 | - | |
| | | 14.0 | 3770 | 6.2416 | - | |
| | | 14.0371 | 3780 | 5.9958 | - | |
| | | 14.0741 | 3790 | 5.7032 | - | |
| | | 14.1112 | 3800 | 4.9524 | - | |
| | | 14.1483 | 3810 | 5.386 | - | |
| | | 14.1854 | 3820 | 5.6353 | - | |
| | | 14.2224 | 3830 | 5.0873 | - | |
| | | 14.2595 | 3840 | 4.9255 | - | |
| | | 14.2966 | 3850 | 5.1423 | - | |
| | | 14.3336 | 3860 | 6.0775 | - | |
| | | 14.3707 | 3870 | 4.5073 | - | |
| | | 14.4078 | 3880 | 6.8347 | - | |
| | | 14.4449 | 3890 | 6.5397 | - | |
| | | 14.4819 | 3900 | 7.2143 | 3.3080 | |
| | | 14.5190 | 3910 | 6.1123 | - | |
| | | 14.5561 | 3920 | 6.6048 | - | |
| | | 14.5931 | 3930 | 6.3464 | - | |
| | | 14.6302 | 3940 | 6.3618 | - | |
| | | 14.6673 | 3950 | 6.5718 | - | |
| | | 14.7044 | 3960 | 5.9785 | - | |
| | | 14.7414 | 3970 | 6.5758 | - | |
| | | 14.7785 | 3980 | 6.4308 | - | |
| | | 14.8156 | 3990 | 6.0208 | - | |
| | | 14.8526 | 4000 | 6.0303 | - | |
| | | 14.8897 | 4010 | 6.6396 | - | |
| | | 14.9268 | 4020 | 6.0184 | - | |
| | | 14.9639 | 4030 | 6.6248 | - | |
| | | 15.0 | 4040 | 6.4538 | - | |
| | | 15.0371 | 4050 | 6.4742 | 3.1761 | |
| | | 15.0741 | 4060 | 5.5295 | - | |
| | | 15.1112 | 4070 | 6.8753 | - | |
| | | 15.1483 | 4080 | 5.639 | - | |
| | | 15.1854 | 4090 | 5.6232 | - | |
| | | 15.2224 | 4100 | 6.3026 | - | |
| | | 15.2595 | 4110 | 6.1182 | - | |
| | | 15.2966 | 4120 | 5.4736 | - | |
| | | 15.3336 | 4130 | 6.2961 | - | |
| | | 15.3707 | 4140 | 5.4742 | - | |
| | | 15.4078 | 4150 | 5.4707 | - | |
| | | 15.4449 | 4160 | 4.7272 | - | |
| | | 15.4819 | 4170 | 6.1026 | - | |
| | | 15.5190 | 4180 | 5.0468 | - | |
| | | 15.5561 | 4190 | 5.5796 | - | |
| | | 15.5931 | 4200 | 6.9046 | 3.1433 | |
| | | 15.6302 | 4210 | 5.6123 | - | |
| | | 15.6673 | 4220 | 6.7246 | - | |
| | | 15.7044 | 4230 | 5.7076 | - | |
| | | 15.7414 | 4240 | 6.6772 | - | |
| | | 15.7785 | 4250 | 5.6038 | - | |
| | | 15.8156 | 4260 | 4.9544 | - | |
| | | 15.8526 | 4270 | 5.0661 | - | |
| | | 15.8897 | 4280 | 5.291 | - | |
| | | 15.9268 | 4290 | 6.6652 | - | |
| | | 15.9639 | 4300 | 5.6797 | - | |
| | | 16.0 | 4310 | 5.1129 | - | |
| | | 16.0371 | 4320 | 5.4445 | - | |
| | | 16.0741 | 4330 | 4.8946 | - | |
| | | 16.1112 | 4340 | 6.3929 | - | |
| | | 16.1483 | 4350 | 6.0633 | 3.1426 | |
| | | 16.1854 | 4360 | 5.522 | - | |
| | | 16.2224 | 4370 | 4.7067 | - | |
| | | 16.2595 | 4380 | 5.4688 | - | |
| | | 16.2966 | 4390 | 5.6009 | - | |
| | | 16.3336 | 4400 | 5.1376 | - | |
| | | 16.3707 | 4410 | 4.5196 | - | |
| | | 16.4078 | 4420 | 5.5109 | - | |
| | | 16.4449 | 4430 | 5.1888 | - | |
| | | 16.4819 | 4440 | 6.0305 | - | |
| | | 16.5190 | 4450 | 5.2791 | - | |
| | | 16.5561 | 4460 | 5.4005 | - | |
| | | 16.5931 | 4470 | 5.255 | - | |
| | | 16.6302 | 4480 | 6.2026 | - | |
| | | 16.6673 | 4490 | 6.6388 | - | |
| | | 16.7044 | 4500 | 5.6138 | 3.2812 | |
| | | 16.7414 | 4510 | 4.7913 | - | |
| | | 16.7785 | 4520 | 5.6675 | - | |
| | | 16.8156 | 4530 | 5.8975 | - | |
| | | 16.8526 | 4540 | 5.4597 | - | |
| | | 16.8897 | 4550 | 5.137 | - | |
| | | 16.9268 | 4560 | 4.5395 | - | |
| | | 16.9639 | 4570 | 4.6304 | - | |
| | | 17.0 | 4580 | 5.8098 | - | |
| | | 17.0371 | 4590 | 4.0267 | - | |
| | | 17.0741 | 4600 | 4.9194 | - | |
| | | 17.1112 | 4610 | 4.1852 | - | |
| | | 17.1483 | 4620 | 5.129 | - | |
| | | 17.1854 | 4630 | 4.469 | - | |
| | | 17.2224 | 4640 | 5.4298 | - | |
| | | 17.2595 | 4650 | 4.5234 | 3.3447 | |
| | | 17.2966 | 4660 | 4.6856 | - | |
| | | 17.3336 | 4670 | 6.3431 | - | |
| | | 17.3707 | 4680 | 5.347 | - | |
| | | 17.4078 | 4690 | 4.9223 | - | |
| | | 17.4449 | 4700 | 5.4404 | - | |
| | | 17.4819 | 4710 | 4.916 | - | |
| | | 17.5190 | 4720 | 6.1744 | - | |
| | | 17.5561 | 4730 | 4.8039 | - | |
| | | 17.5931 | 4740 | 5.2276 | - | |
| | | 17.6302 | 4750 | 4.4189 | - | |
| | | 17.6673 | 4760 | 4.1434 | - | |
| | | 17.7044 | 4770 | 4.9443 | - | |
| | | 17.7414 | 4780 | 5.6975 | - | |
| | | 17.7785 | 4790 | 4.6667 | - | |
| | | 17.8156 | 4800 | 4.9876 | 3.2924 | |
| | | 17.8526 | 4810 | 4.4342 | - | |
| | | 17.8897 | 4820 | 5.2595 | - | |
| | | 17.9268 | 4830 | 5.6566 | - | |
| | | 17.9639 | 4840 | 5.5452 | - | |
| | | 18.0 | 4850 | 4.4986 | - | |
| | | 18.0371 | 4860 | 4.8155 | - | |
| | | 18.0741 | 4870 | 4.2278 | - | |
| | | 18.1112 | 4880 | 5.4733 | - | |
| | | 18.1483 | 4890 | 4.2394 | - | |
| | | 18.1854 | 4900 | 5.1253 | - | |
| | | 18.2224 | 4910 | 4.7498 | - | |
| | | 18.2595 | 4920 | 4.9775 | - | |
| | | 18.2966 | 4930 | 4.797 | - | |
| | | 18.3336 | 4940 | 4.5694 | - | |
| | | 18.3707 | 4950 | 4.6192 | 3.6615 | |
| | | 18.4078 | 4960 | 5.8114 | - | |
| | | 18.4449 | 4970 | 4.8035 | - | |
| | | 18.4819 | 4980 | 4.6944 | - | |
| | | 18.5190 | 4990 | 4.8664 | - | |
| | | 18.5561 | 5000 | 4.6916 | - | |
| | | 18.5931 | 5010 | 4.3352 | - | |
| | | 18.6302 | 5020 | 5.9779 | - | |
| | | 18.6673 | 5030 | 4.7813 | - | |
| | | 18.7044 | 5040 | 4.632 | - | |
| | | 18.7414 | 5050 | 4.7411 | - | |
| | | 18.7785 | 5060 | 3.6489 | - | |
| | | 18.8156 | 5070 | 4.5373 | - | |
| | | 18.8526 | 5080 | 5.6129 | - | |
| | | 18.8897 | 5090 | 4.8933 | - | |
| | | 18.9268 | 5100 | 4.27 | 3.6957 | |
| | | 18.9639 | 5110 | 4.5338 | - | |
| | | 19.0 | 5120 | 5.5175 | - | |
| | | 19.0371 | 5130 | 5.0835 | - | |
| | | 19.0741 | 5140 | 4.6826 | - | |
| | | 19.1112 | 5150 | 4.5391 | - | |
| | | 19.1483 | 5160 | 5.3723 | - | |
| | | 19.1854 | 5170 | 4.8095 | - | |
| | | 19.2224 | 5180 | 4.7402 | - | |
| | | 19.2595 | 5190 | 4.0488 | - | |
| | | 19.2966 | 5200 | 3.6424 | - | |
| | | 19.3336 | 5210 | 4.2256 | - | |
| | | 19.3707 | 5220 | 4.4607 | - | |
| | | 19.4078 | 5230 | 3.5702 | - | |
| | | 19.4449 | 5240 | 4.3062 | - | |
| | | 19.4819 | 5250 | 4.2919 | 3.6594 | |
| | | 19.5190 | 5260 | 4.6985 | - | |
| | | 19.5561 | 5270 | 4.6907 | - | |
| | | 19.5931 | 5280 | 4.3865 | - | |
| | | 19.6302 | 5290 | 3.9818 | - | |
| | | 19.6673 | 5300 | 4.3166 | - | |
| | | 19.7044 | 5310 | 4.9131 | - | |
| | | 19.7414 | 5320 | 4.7641 | - | |
| | | 19.7785 | 5330 | 5.419 | - | |
| | | 19.8156 | 5340 | 4.068 | - | |
| | | 19.8526 | 5350 | 4.1094 | - | |
| | | 19.8897 | 5360 | 5.2279 | - | |
| | | 19.9268 | 5370 | 4.4818 | - | |
| | | 19.9639 | 5380 | 4.3103 | - | |
| |
|
| | </details> |
| |
|
| | ### Framework Versions |
| | - Python: 3.11.0 |
| | - Sentence Transformers: 3.4.0 |
| | - Transformers: 4.48.1 |
| | - PyTorch: 2.5.1+cu124 |
| | - Accelerate: 1.3.0 |
| | - Datasets: 3.2.0 |
| | - 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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