checkpoints/checkpoint-2100/README.md

metadata

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 model finetuned from intfloat/multilingual-e5-large-instruct on the 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • measuring-embeddings-v3

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# 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]

Training Details

Training Dataset

measuring-embeddings-v3

• Dataset: measuring-embeddings-v3 at 1b3cbbe
• Size: 7,552 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 9 tokens mean: 15.96 tokens max: 40 tokens	min: 120 tokens mean: 255.56 tokens max: 512 tokens	min: 0.0 mean: 0.22 max: 0.95

• Samples:

  sentence1	sentence2	score
  How can I combine the sub-query with the main query to fetch the last uncertainty report?	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 classific...	0.1
  What is the column name for the calibration date in the calibration table?	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.	0.1
  What is the name of the table that contains the flow computer tags?	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 ...	0.05

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

measuring-embeddings-v3

• Dataset: measuring-embeddings-v3 at 1b3cbbe
• Size: 1,618 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 9 tokens mean: 15.83 tokens max: 40 tokens	min: 120 tokens mean: 250.41 tokens max: 512 tokens	min: 0.0 mean: 0.23 max: 0.95

• Samples:

  sentence1	sentence2	score
  Identify any additional tables or columns that might be needed for the query.	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.	0.2
  What columns in these tables contain the measurement system tag and the flow computer tag?	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.	0.1
  Identify the column that stores the calibration number.	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 kno...	0.1

• Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 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

Click to expand

• 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

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
3.9379	1060	8.5934	-
3.9750	1070	8.006	-
4.0148	1080	9.0081	-
4.0519	1090	8.6706	-
4.0890	1100	9.6146	-
4.1260	1110	9.225	-
4.1631	1120	8.7522	-
4.2002	1130	9.0221	-
4.2373	1140	9.6458	-
4.2743	1150	8.7692	-
4.3114	1160	9.2874	-
4.3485	1170	8.9276	-
4.3855	1180	8.7444	-
4.4226	1190	8.7265	-
4.4597	1200	8.7642	2.6471
4.4968	1210	8.8917	-
4.5338	1220	9.2155	-
4.5709	1230	8.6101	-
4.6080	1240	8.9904	-
4.6450	1250	9.3272	-
4.6821	1260	7.9367	-
4.7192	1270	8.5891	-
4.7563	1280	8.6286	-
4.7933	1290	7.9982	-
4.8304	1300	7.5587	-
4.8675	1310	7.9405	-
4.9045	1320	9.7092	-
4.9416	1330	8.1475	-
4.9787	1340	9.3603	-
5.0148	1350	7.6621	2.8309
5.0519	1360	9.2301	-
5.0890	1370	9.7789	-
5.1260	1380	9.5359	-
5.1631	1390	10.8065	-
5.2002	1400	10.0149	-
5.2373	1410	10.2582	-
5.2743	1420	10.16	-
5.3114	1430	10.0763	-
5.3485	1440	9.5737	-
5.3855	1450	10.4816	-
5.4226	1460	8.6687	-
5.4597	1470	8.4066	-
5.4968	1480	9.386	-
5.5338	1490	8.3911	-
5.5709	1500	8.8025	2.5408
5.6080	1510	8.7939	-
5.6450	1520	9.0903	-
5.6821	1530	8.9878	-
5.7192	1540	8.8642	-
5.7563	1550	8.8625	-
5.7933	1560	8.4105	-
5.8304	1570	9.0163	-
5.8675	1580	8.8947	-
5.9045	1590	8.5647	-
5.9416	1600	7.7047	-
5.9787	1610	8.1484	-
6.0148	1620	8.4079	-
6.0519	1630	8.5027	-
6.0890	1640	8.1805	-
6.1260	1650	8.4519	2.5901
6.1631	1660	9.062	-
6.2002	1670	8.8499	-
6.2373	1680	8.6576	-
6.2743	1690	8.4652	-
6.3114	1700	9.0782	-
6.3485	1710	8.1532	-
6.3855	1720	8.5185	-
6.4226	1730	9.5908	-
6.4597	1740	8.4188	-
6.4968	1750	8.1885	-
6.5338	1760	8.7666	-
6.5709	1770	8.6105	-
6.6080	1780	8.664	-
6.6450	1790	8.5294	-
6.6821	1800	9.1857	2.4974
6.7192	1810	8.7053	-
6.7563	1820	8.1428	-
6.7933	1830	8.4988	-
6.8304	1840	8.4147	-
6.8675	1850	9.069	-
6.9045	1860	8.4405	-
6.9416	1870	9.2157	-
6.9787	1880	9.5492	-
7.0148	1890	8.1325	-
7.0519	1900	8.324	-
7.0890	1910	7.7097	-
7.1260	1920	8.0982	-
7.1631	1930	7.7669	-
7.2002	1940	7.809	-
7.2373	1950	7.9729	2.6108
7.2743	1960	8.2125	-
7.3114	1970	7.7403	-
7.3485	1980	7.5494	-
7.3855	1990	8.2821	-
7.4226	2000	8.1644	-
7.4597	2010	8.1664	-
7.4968	2020	8.5876	-
7.5338	2030	8.2753	-
7.5709	2040	9.2057	-
7.6080	2050	8.0052	-
7.6450	2060	8.4954	-
7.6821	2070	8.0325	-
7.7192	2080	8.2934	-
7.7563	2090	9.4019	-
7.7933	2100	8.874	2.4529

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

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}