Add new SentenceTransformer model

4a82566 verified about 1 year ago

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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:3630
  - loss:CoSENTLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
  - source_sentence: equipment database
    sentences:
      - >-
        What is uncertainty?

        Uncertainty is a measure of confidence in the precision and reliability
        of results obtained from equipment or measurement systems. It quantifies
        the potential error or margin of error in measurements.


        Types of uncertainty:

        There are two main types of uncertainty:

        1. Uncertainty of magnitudes (variables):
            - Refers to the uncertainty of specific variables, such as temperature or pressure.
            - It is calculated after calibrating a device or obtained from the **equipment** manufacturer's manual.
            - This uncertainty serves as a starting point for further calculations related to the equipment.

        2. Uncertainty of the measurement system:
            - Refers to the uncertainty calculated for the overall flow measurement.
            - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.

        Key points:

        - The uncertainties of magnitudes (variables) are the foundation for
        calculating the uncertainty of the measurement system. Think of them as
        the "building blocks."

        - Do not confuse the two types of uncertainty:
            - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).
            - **Uncertainty of the measurement system**: Specific to the overall flow measurement.
      - >-
        What is a flow computer?

        A flow computer is a device used in measurement engineering. It collects
        analog and digital data from flow meters and other sensors.


        Key features of a flow computer:

        - It has a unique name, firmware version, and manufacturer information.

        - It is designed to record and process data such as temperature,
        pressure, and fluid volume (for gases or oils).
      - >-
        What is an Equipment Type?

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


        Key Aspects of Equipment Types:

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

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

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

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

        Understanding equipment types ensures correct data interpretation,
        proper calibration, and accurate measurement within a system.
  - source_sentence: transmitter calibration record
    sentences:
      - >-
        What is a Measurement Type?

        Measurement types define the classification of measurements used within
        a system based on their purpose and regulatory requirements. These types
        include **fiscal**, **appropriation**, **operational**, and **custody**
        measurements.  


        - **Fiscal measurements** are used for tax and regulatory reporting,
        ensuring accurate financial transactions based on measured quantities.  

        - **Appropriation measurements** track resource allocation and ownership
        distribution among stakeholders.  

        - **Operational measurements** support real-time monitoring and process
        optimization within industrial operations.  

        - **Custody measurements** are essential for legal and contractual
        transactions, ensuring precise handover of fluids between parties.  


        These classifications play a crucial role in compliance, financial
        accuracy, and operational efficiency across industries such as oil and
        gas, water management, and energy distribution.  
      - >-
        What is a Fluid?

        A Fluid is the substance measured within a measurement system. It can be
        a gas or liquid, such as hydrocarbons, water, or other industrial
        fluids. Proper classification of fluids is essential for ensuring
        measurement accuracy, regulatory compliance, and operational efficiency.
        By identifying fluids correctly, the system applies the appropriate
        measurement techniques, processing methods, and reporting standards.
      - >-
        What is a measurement system?

        **Measurement systems** are essential components in industrial
        measurement and processing. They are identified by a unique **Tag** and
        are associated with a specific **installation** and **fluid type**.
        These systems utilize different **measurement technologies**, including
        **differential (DIF)** and **linear (LIN)**, depending on the
        application. Measurement systems can be classified based on their
        **application type**, such as **fiscal** or **custody transfer**.  
  - source_sentence: most recent calibration
    sentences:
      - >-
        What is a Fluid?

        A Fluid is the substance measured within a measurement system. It can be
        a gas or liquid, such as hydrocarbons, water, or other industrial
        fluids. Proper classification of fluids is essential for ensuring
        measurement accuracy, regulatory compliance, and operational efficiency.
        By identifying fluids correctly, the system applies the appropriate
        measurement techniques, processing methods, and reporting standards.
      - >-
        What is a Calibration Record?

        A Calibration Record documents the calibration process of a specific
        equipment tag, ensuring that its measurements remain accurate and
        reliable. Calibration is a critical process in maintaining measurement
        precision and compliance with standards.


        Key Aspects of a Calibration Record:

        - Calibration Date: The exact date when the calibration was performed,
        crucial for tracking maintenance schedules.

        - Certification Number: A unique identifier for the calibration
        certificate, providing traceability and verification of compliance.

        - Range Values: The minimum and maximum measurement values covered
        during the calibration process.

        - Calibration Status: Indicates whether the calibration was approved or
        saved for further review.

        - Associated Units: Specifies the measurement units used in calibration
        (e.g., °C, psi).

        - Associated Equipment Tag ID: Links the calibration record to a
        specific equipment tag, ensuring traceability of measurement
        instruments.

        Calibration records play a fundamental role in quality assurance,
        helping maintain measurement integrity and regulatory compliance.
      - >-
        What is a report index or historic index?

        Indexes represent the recorded reports generated by flow computers,
        classified into two types: 

        - **Hourly reports Index**: Store data for hourly events.

        - **Daily reports Index**: Strore data for daily events.


        These reports, also referred to as historical data or flow computer
        historical records, contain raw, first-hand measurements directly
        collected from the flow computer. The data has not been processed or
        used in any calculations, preserving its original state for analysis or
        validation.


        The index is essential for locating specific values within the report.
  - source_sentence: measurement system tag EMED-3102-02-010
    sentences:
      - >-
        What is a report index or historic index?

        Indexes represent the recorded reports generated by flow computers,
        classified into two types: 

        - **Hourly reports Index**: Store data for hourly events.

        - **Daily reports Index**: Strore data for daily events.


        These reports, also referred to as historical data or flow computer
        historical records, contain raw, first-hand measurements directly
        collected from the flow computer. The data has not been processed or
        used in any calculations, preserving its original state for analysis or
        validation.


        The index is essential for locating specific values within the report.
      - >-
        What is uncertainty?

        Uncertainty is a measure of confidence in the precision and reliability
        of results obtained from equipment or measurement systems. It quantifies
        the potential error or margin of error in measurements.


        Types of uncertainty:

        There are two main types of uncertainty:

        1. Uncertainty of magnitudes (variables):
            - Refers to the uncertainty of specific variables, such as temperature or pressure.
            - It is calculated after calibrating a device or obtained from the **equipment** manufacturer's manual.
            - This uncertainty serves as a starting point for further calculations related to the equipment.

        2. Uncertainty of the measurement system:
            - Refers to the uncertainty calculated for the overall flow measurement.
            - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.

        Key points:

        - The uncertainties of magnitudes (variables) are the foundation for
        calculating the uncertainty of the measurement system. Think of them as
        the "building blocks."

        - Do not confuse the two types of uncertainty:
            - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).
            - **Uncertainty of the measurement system**: Specific to the overall flow measurement.
      - >-
        What is a Magnitude?

        A magnitude/variable represents a physical magnitude measured by the
        system, such as temperature, pressure, or volume. It plays a crucial
        role in monitoring and analyzing system performance. Each variable has a
        status that indicates whether it is active (ACT) or inactive (INA),
        ensuring proper identification and usage within measurement processes.
  - source_sentence: list of measurement systems
    sentences:
      - >-
        What is a Calibration Record?

        A Calibration Record documents the calibration process of a specific
        equipment tag, ensuring that its measurements remain accurate and
        reliable. Calibration is a critical process in maintaining measurement
        precision and compliance with standards.


        Key Aspects of a Calibration Record:

        - Calibration Date: The exact date when the calibration was performed,
        crucial for tracking maintenance schedules.

        - Certification Number: A unique identifier for the calibration
        certificate, providing traceability and verification of compliance.

        - Range Values: The minimum and maximum measurement values covered
        during the calibration process.

        - Calibration Status: Indicates whether the calibration was approved or
        saved for further review.

        - Associated Units: Specifies the measurement units used in calibration
        (e.g., °C, psi).

        - Associated Equipment Tag ID: Links the calibration record to a
        specific equipment tag, ensuring traceability of measurement
        instruments.

        Calibration records play a fundamental role in quality assurance,
        helping maintain measurement integrity and regulatory compliance.
      - >-
        What is a measurement system?

        **Measurement systems** are essential components in industrial
        measurement and processing. They are identified by a unique **Tag** and
        are associated with a specific **installation** and **fluid type**.
        These systems utilize different **measurement technologies**, including
        **differential (DIF)** and **linear (LIN)**, depending on the
        application. Measurement systems can be classified based on their
        **application type**, such as **fiscal** or **custody transfer**.  
      - >-
        What is a Calibration Point?

        A Calibration Point represents a specific data entry in a calibration
        process, comparing an expected reference value to an actual measured
        value. These points are fundamental in ensuring measurement accuracy and
        identifying deviations.


        Key Aspects of Calibration Points:

        - Calibration Report Association: Each calibration point belongs to a
        specific calibration report, linking it to a broader calibration
        procedure.

        - Reference Values: Theoretical or expected values used as a benchmark
        for measurement validation.

        - Measured Values: The actual recorded values during calibration,
        reflecting the instrument’s response.

        - Errors: The difference between reference and measured values,
        indicating possible measurement inaccuracies.

        Calibration points are essential for evaluating instrument performance,
        ensuring compliance with standards, and maintaining measurement
        reliability.
datasets:
  - Lauther/measuring-embeddings-v5
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on intfloat/multilingual-e5-large-instruct

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the measuring-embeddings-v5 dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: intfloat/multilingual-e5-large-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- measuring-embeddings-v5

Model Sources

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

Full Model Architecture

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

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/measuring-embeddings-v5.1")
# Run inference
sentences = [
    'list of measurement systems',
    'What is a Calibration Point?\nA Calibration Point represents a specific data entry in a calibration process, comparing an expected reference value to an actual measured value. These points are fundamental in ensuring measurement accuracy and identifying deviations.\n\nKey Aspects of Calibration Points:\n- Calibration Report Association: Each calibration point belongs to a specific calibration report, linking it to a broader calibration procedure.\n- Reference Values: Theoretical or expected values used as a benchmark for measurement validation.\n- Measured Values: The actual recorded values during calibration, reflecting the instrument’s response.\n- Errors: The difference between reference and measured values, indicating possible measurement inaccuracies.\nCalibration points are essential for evaluating instrument performance, ensuring compliance with standards, and maintaining measurement reliability.',
    'What is a Calibration Record?\nA Calibration Record documents the calibration process of a specific equipment tag, ensuring that its measurements remain accurate and reliable. Calibration is a critical process in maintaining measurement precision and compliance with standards.\n\nKey Aspects of a Calibration Record:\n- Calibration Date: The exact date when the calibration was performed, crucial for tracking maintenance schedules.\n- Certification Number: A unique identifier for the calibration certificate, providing traceability and verification of compliance.\n- Range Values: The minimum and maximum measurement values covered during the calibration process.\n- Calibration Status: Indicates whether the calibration was approved or saved for further review.\n- Associated Units: Specifies the measurement units used in calibration (e.g., °C, psi).\n- Associated Equipment Tag ID: Links the calibration record to a specific equipment tag, ensuring traceability of measurement instruments.\nCalibration records play a fundamental role in quality assurance, helping maintain measurement integrity and regulatory compliance.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

measuring-embeddings-v5

Dataset: measuring-embeddings-v5 at 90b5410
Size: 3,630 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 7.46 tokens
max: 17 tokens

min: 80 tokens
mean: 181.99 tokens
max: 406 tokens

min: 0.0
mean: 0.23
max: 0.95

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 7.46 tokens max: 17 tokens	min: 80 tokens mean: 181.99 tokens max: 406 tokens	min: 0.0 mean: 0.23 max: 0.95

Samples:

sentence1	sentence2	score
`measurement technology name`	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...	`0.001`
`transmitter calibration record`	What is an Uncertainty Curve Point? An Uncertainty Curve Point represents a data point used to construct the uncertainty curve of a measurement system. These curves help analyze how measurement uncertainty behaves under different flow rate conditions, ensuring accuracy and reliability in uncertainty assessments. Key Aspects of an Uncertainty Curve Point: - Uncertainty File ID: Links the point to the specific uncertainty dataset, ensuring traceability. Equipment Tag ID: Identifies the equipment associated with the uncertainty measurement, crucial for system validation. - Uncertainty Points: Represent uncertainty values recorded at specific conditions, forming part of the overall uncertainty curve. - Flow Rate Points: Corresponding flow rate values at which the uncertainty was measured, essential for evaluating performance under varying operational conditions. These points are fundamental for generating uncertainty curves, which are used in calibration, validation, and compliance assess...	`0.001`
`measurement magnitude uncertainty`	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...	`0.95`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

measuring-embeddings-v5

Dataset: measuring-embeddings-v5 at 90b5410
Size: 778 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 778 samples:
sentence1 sentence2 score
type string string float
details
min: 3 tokens
mean: 7.44 tokens
max: 17 tokens

min: 80 tokens
mean: 184.77 tokens
max: 406 tokens

min: 0.0
mean: 0.23
max: 0.95

	sentence1	sentence2	score
type	string	string	float
details	min: 3 tokens mean: 7.44 tokens max: 17 tokens	min: 80 tokens mean: 184.77 tokens max: 406 tokens	min: 0.0 mean: 0.23 max: 0.95

Samples:

sentence1	sentence2	score
`measurement type`	What is an Equipment Class? An Equipment Class categorizes different types of equipment based on their function or role within a measurement system. This classification helps in organizing and distinguishing equipment types for operational, maintenance, and analytical purposes. Each Equipment Class groups related equipment under a common category. Examples include: Primary → Main measurement device in a system. Secondary → Supporting measurement device, often used for verification. Tertiary → Additional measurement equipment. Valves → Flow control devices used in the system. By defining Equipment Classes, the system ensures proper identification, tracking, and management of measurement-related assets.	`0.001`
`latest uncertainty result`	What is an Uncertainty Composition? An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty. Key Aspects of an Uncertainty Component: - Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system. - Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact. - Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation. Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.	`0.75`
`uncertainty calculation ID 593`	What is an Uncertainty Composition? An Uncertainty Composition represents a specific factor that contributes to the overall uncertainty of a measurement system. These components are essential for evaluating the accuracy and reliability of measurements by identifying and quantifying the sources of uncertainty. Key Aspects of an Uncertainty Component: - Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system. - Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact. - Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation. Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.	`0.95`

Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
gradient_accumulation_steps: 8
learning_rate: 5e-06
weight_decay: 0.01
max_grad_norm: 0.5
num_train_epochs: 20
lr_scheduler_type: cosine
warmup_ratio: 0.1

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-06
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 0.5
num_train_epochs: 20
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss
6.2467	350	5.4993	-
7.1233	400	5.1991	-
8.0	450	4.7573	0.6509
8.8811	500	4.8783	-
9.7577	550	4.4897	-
10.6344	600	3.9524	0.6758
11.5110	650	3.679	-
12.3877	700	3.4076	-
13.2643	750	3.8909	0.6588
14.1410	800	3.1191	-
15.0176	850	3.5478	-
15.8987	900	3.2201	0.6553
16.7753	950	3.3027	-
17.6520	1000	2.4874	-
18.5286	1050	2.9745	0.6615
19.4053	1100	2.8649	-

Framework Versions

Python: 3.11.0
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.6.0+cu124
Accelerate: 1.4.0
Datasets: 3.3.2
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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},
}