SentenceTransformer

This is a sentence-transformers model trained on the measuring-embeddings-v4 dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 1024 dimensions
  • Similarity Function: Cosine Similarity
  • Training Dataset:

Model Sources

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-v4.3")
# Run inference
sentences = [
    'uncertainty points',
    'What is a Fluid?\nA Fluid is the substance measured within a measurement system. It can be a gas or liquid, such as hydrocarbons, water, or other industrial fluids. Proper classification of fluids is essential for ensuring measurement accuracy, regulatory compliance, and operational efficiency. By identifying fluids correctly, the system applies the appropriate measurement techniques, processing methods, and reporting standards.',
    'What is a Calibration Point?\nA Calibration Point represents a specific data entry in a calibration process, comparing an expected reference value to an actual measured value. These points are fundamental in ensuring measurement accuracy and identifying deviations.\n\nKey Aspects of Calibration Points:\n- Calibration Report Association: Each calibration point belongs to a specific calibration report, linking it to a broader calibration procedure.\n- Reference Values: Theoretical or expected values used as a benchmark for measurement validation.\n- Measured Values: The actual recorded values during calibration, reflecting the instrument’s response.\n- Errors: The difference between reference and measured values, indicating possible measurement inaccuracies.\nCalibration points are essential for evaluating instrument performance, ensuring compliance with standards, and maintaining measurement reliability.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Training Details

Training Dataset

measuring-embeddings-v4

  • Dataset: measuring-embeddings-v4 at 1e3ca2c
  • Size: 3,075 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: 7.55 tokens
    • max: 17 tokens
    • min: 80 tokens
    • mean: 180.22 tokens
    • max: 406 tokens
    • min: 0.07
    • mean: 0.21
    • max: 0.95
  • Samples:
    sentence1 sentence2 score
    last calibrated span What are historical report values?
    These represent the recorded data points within flow computer reports. Unlike the report index, which serves as a reference to locate reports, these values contain the actual measurements and calculated data stored in the historical records.

    Flow computer reports store two types of data values:

    - Hourly data values: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on an hourly basis.
    - Daily data values: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on a daily basis.
    Each value is directly linked to its respective report index, ensuring traceability to the original flow computer record. These values maintain their raw integrity, providing a reliable source for analysis and validation.
    0.1
    flow computer configuration 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.
    0.1
    uncertainty certificate number 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.
    0.1
  • Loss: CoSENTLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "pairwise_cos_sim"
    }
    

Evaluation Dataset

measuring-embeddings-v4

  • Dataset: measuring-embeddings-v4 at 1e3ca2c
  • Size: 659 evaluation samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 659 samples:
    sentence1 sentence2 score
    type string string float
    details
    • min: 3 tokens
    • mean: 7.63 tokens
    • max: 17 tokens
    • min: 80 tokens
    • mean: 186.36 tokens
    • max: 406 tokens
    • min: 0.07
    • mean: 0.2
    • max: 0.9
  • Samples:
    sentence1 sentence2 score
    measurement system details 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.
    0.15
    measurement system tag EMED-3102-02-010 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.
    0.24
    static pressure What is a Meter Stream?
    A Meter Stream represents a measurement system configured within a flow computer. It serves as the interface between the physical measurement system and the computational processes that record and analyze flow data.

    Key Aspects of a Meter Stream:
    - Status: Indicates whether the meter stream is active or inactive.
    - Measurement System Association: Links the meter stream to a specific measurement system, ensuring that the data collected corresponds to a defined physical setup.
    - Flow Computer Association: Identifies the flow computer responsible for managing and recording the measurement system's data.
    Why is a Meter Stream Important?
    A meter stream is a critical component in flow measurement, as it ensures that the measurement system is correctly integrated into the flow computer for accurate monitoring and reporting. Since each flow computer can handle multiple meter streams, proper configuration is essential for maintaining data integrity and traceability.
    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: 4
  • per_device_eval_batch_size: 4
  • gradient_accumulation_steps: 4
  • learning_rate: 2e-05
  • num_train_epochs: 10
  • warmup_ratio: 0.1

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 4
  • per_device_eval_batch_size: 4
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 4
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 10
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • dispatch_batches: None
  • split_batches: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss Validation Loss
0.8322 160 3.0564 -
0.8843 170 2.2963 -
0.9363 180 1.8767 -
0.9883 190 2.8634 -
1.0416 200 2.5195 -
1.0936 210 2.4094 -
1.1456 220 1.5141 -
1.1977 230 2.1366 -
1.2497 240 1.5389 -
1.3017 250 3.8265 -
1.3537 260 1.9989 -
1.4057 270 2.6037 -
1.4577 280 3.898 -
1.5098 290 2.9363 -
1.5618 300 3.3853 0.5155
1.6138 310 2.2995 -
1.6658 320 1.3945 -
1.7178 330 3.8312 -
1.7698 340 2.626 -
1.8218 350 1.5451 -
1.8739 360 1.1062 -
1.9259 370 2.6593 -
1.9779 380 1.773 -
2.0260 390 1.3937 -
2.0780 400 2.2228 -
2.1300 410 0.7027 -
2.1821 420 1.5933 -
2.2341 430 2.295 -
2.2861 440 1.042 -
2.3381 450 2.8671 0.3661
2.3901 460 1.879 -
2.4421 470 4.0556 -
2.4941 480 2.9677 -
2.5462 490 1.4443 -
2.5982 500 3.2575 -
2.6502 510 1.6124 -
2.7022 520 1.3976 -
2.7542 530 1.3161 -
2.8062 540 2.5047 -
2.8583 550 0.9757 -
2.9103 560 2.1051 -
2.9623 570 2.4919 -
3.0104 580 1.4737 -
3.0624 590 1.3318 -
3.1144 600 1.4474 0.4409
3.1664 610 2.3727 -
3.2185 620 0.6234 -
3.2705 630 1.9529 -
3.3225 640 1.5384 -
3.3745 650 1.5913 -
3.4265 660 0.6265 -
3.4785 670 2.1122 -
3.5306 680 1.8046 -
3.5826 690 0.8298 -
3.6346 700 1.4242 -
3.6866 710 1.5808 -
3.7386 720 1.1792 -
3.7906 730 2.7767 -
3.8427 740 1.7814 -
3.8947 750 0.5374 0.3227
3.9467 760 1.493 -
3.9987 770 1.8282 -
4.0468 780 1.6991 -
4.0988 790 0.7883 -
4.1508 800 0.841 -
4.2029 810 0.923 -
4.2549 820 0.3459 -
4.3069 830 2.3643 -
4.3589 840 0.9606 -
4.4109 850 0.7961 -
4.4629 860 1.749 -
4.5150 870 0.6536 -
4.5670 880 1.668 -
4.6190 890 0.5919 -
4.6710 900 1.2476 0.3258
4.7230 910 1.422 -
4.7750 920 0.8616 -
4.8270 930 0.2323 -
4.8791 940 2.7915 -
4.9311 950 0.6705 -
4.9831 960 1.7353 -
5.0312 970 1.7646 -
5.0832 980 1.4311 -
5.1352 990 0.7089 -
5.1873 1000 1.631 -
5.2393 1010 1.8051 -
5.2913 1020 0.5302 -
5.3433 1030 0.7428 -
5.3953 1040 0.5852 -
5.4473 1050 0.737 0.3283
5.4993 1060 1.492 -
5.5514 1070 0.9142 -
5.6034 1080 1.8887 -
5.6554 1090 1.1079 -
5.7074 1100 0.6984 -
5.7594 1110 1.7174 -
5.8114 1120 0.9411 -
5.8635 1130 1.286 -
5.9155 1140 2.1944 -
5.9675 1150 1.2478 -
6.0156 1160 0.7935 -
6.0676 1170 1.4886 -
6.1196 1180 1.3375 -
6.1717 1190 2.9167 -
6.2237 1200 0.3903 0.2734
6.2757 1210 1.326 -
6.3277 1220 0.3135 -
6.3797 1230 1.0881 -
6.4317 1240 1.5096 -
6.4837 1250 0.5525 -
6.5358 1260 0.3606 -
6.5878 1270 0.9334 -
6.6398 1280 0.5658 -
6.6918 1290 1.5978 -
6.7438 1300 0.4212 -
6.7958 1310 1.7793 -
6.8479 1320 1.5593 -
6.8999 1330 1.6738 -
6.9519 1340 0.3041 -
7.0 1350 0.5286 0.2737
7.0520 1360 1.7618 -
7.1040 1370 0.4629 -
7.1560 1380 0.4087 -
7.2081 1390 0.3099 -
7.2601 1400 0.6679 -
7.3121 1410 0.7688 -
7.3641 1420 1.223 -
7.4161 1430 0.8108 -
7.4681 1440 0.24 -
7.5202 1450 0.6616 -
7.5722 1460 1.5255 -
7.6242 1470 1.3865 -
7.6762 1480 0.2771 -
7.7282 1490 0.7809 -
7.7802 1500 0.2114 0.2259
7.8322 1510 1.6341 -
7.8843 1520 0.7665 -
7.9363 1530 0.7204 -
7.9883 1540 0.6557 -
8.0364 1550 2.0155 -
8.0884 1560 0.4718 -
8.1404 1570 0.1254 -
8.1925 1580 0.8067 -
8.2445 1590 0.3196 -
8.2965 1600 0.7162 -
8.3485 1610 0.1727 -
8.4005 1620 0.7634 -
8.4525 1630 0.2472 -
8.5046 1640 0.264 -
8.5566 1650 0.5994 0.1935
8.6086 1660 0.4445 -
8.6606 1670 0.9039 -
8.7126 1680 0.7927 -
8.7646 1690 0.4908 -
8.8166 1700 0.7486 -
8.8687 1710 1.377 -
8.9207 1720 1.025 -
8.9727 1730 1.1134 -
9.0208 1740 0.271 -
9.0728 1750 1.0931 -
9.1248 1760 0.7956 -
9.1769 1770 1.2794 -
9.2289 1780 0.3901 -
9.2809 1790 0.9033 -
9.3329 1800 0.4934 0.1680
9.3849 1810 0.5104 -
9.4369 1820 0.2879 -
9.4889 1830 0.6565 -
9.5410 1840 0.4523 -
9.5930 1850 0.7147 -
9.6450 1860 0.354 -
9.6970 1870 0.277 -
9.7490 1880 0.2066 -
9.8010 1890 0.6588 -
9.8531 1900 0.3789 -
9.9051 1910 0.8525 -
9.9571 1920 0.366 -

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},
}
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Dataset used to train Lauther/measuring-embeddings-v4.3