Lauther/measuring-embeddings-v4.2

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-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
• Base model: intfloat/multilingual-e5-large-instruct
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • measuring-embeddings-v4

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-v4.2")
# 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
2.3953	460	0.8121	-
2.4473	470	1.7843	-
2.4993	480	3.0975	-
2.5514	490	0.8585	-
2.6034	500	2.7931	-
2.6554	510	1.4479	-
2.7074	520	1.6132	-
2.7594	530	0.8279	-
2.8114	540	2.0968	-
2.8635	550	1.5086	-
2.9155	560	1.7022	-
2.9675	570	1.7252	-
3.0208	580	0.329	-
3.0728	590	3.0231	-
3.1248	600	1.2077	0.4939
3.1769	610	1.7389	-
3.2289	620	1.747	-
3.2809	630	2.608	-
3.3329	640	2.3748	-
3.3849	650	0.9898	-
3.4369	660	3.6768	-
3.4889	670	1.7257	-
3.5410	680	1.2324	-
3.5930	690	1.4847	-
3.6450	700	0.5312	-
3.6970	710	2.6352	-
3.7490	720	3.3293	-
3.8010	730	1.0756	-
3.8531	740	1.2176	-
3.9051	750	1.4641	0.2318
3.9571	760	0.4642	-
4.0052	770	0.8467	-
4.0572	780	0.6422	-
4.1092	790	1.2341	-
4.1612	800	1.2382	-
4.2133	810	0.8518	-
4.2653	820	2.2545	-
4.3173	830	1.0461	-
4.3693	840	1.4097	-
4.4213	850	1.6382	-
4.4733	860	3.3653	-
4.5254	870	1.6778	-
4.5774	880	2.4592	-
4.6294	890	2.3244	-
4.6814	900	0.7048	0.2351
4.7334	910	1.507	-
4.7854	920	1.9508	-
4.8375	930	0.9046	-
4.8895	940	1.3923	-
4.9415	950	2.8222	-
4.9935	960	0.8341	-
5.0416	970	1.7129	-
5.0936	980	0.5792	-
5.1456	990	1.5091	-
5.1977	1000	0.8392	-
5.2497	1010	1.3499	-
5.3017	1020	1.1315	-
5.3537	1030	0.8192	-
5.4057	1040	0.3839	-
5.4577	1050	0.887	0.3572
5.5098	1060	0.9957	-
5.5618	1070	1.4341	-
5.6138	1080	0.5888	-
5.6658	1090	1.4963	-
5.7178	1100	1.5912	-
5.7698	1110	1.3382	-
5.8218	1120	1.4406	-
5.8739	1130	1.0845	-
5.9259	1140	0.2931	-
5.9779	1150	0.8994	-
6.0260	1160	1.1391	-
6.0780	1170	1.4646	-
6.1300	1180	0.509	-
6.1821	1190	0.4108	-
6.2341	1200	0.418	0.2573
6.2861	1210	1.4609	-
6.3381	1220	1.4237	-
6.3901	1230	0.6612	-
6.4421	1240	1.52	-
6.4941	1250	0.9426	-
6.5462	1260	1.5047	-
6.5982	1270	0.2918	-
6.6502	1280	0.96	-
6.7022	1290	1.6685	-
6.7542	1300	0.6779	-
6.8062	1310	0.0522	-
6.8583	1320	1.5055	-
6.9103	1330	0.2947	-
6.9623	1340	0.7499	-
7.0104	1350	2.6794	0.1881
7.0624	1360	1.4322	-
7.1144	1370	0.1859	-
7.1664	1380	1.0946	-
7.2185	1390	1.0941	-
7.2705	1400	0.8873	-
7.3225	1410	0.3996	-
7.3745	1420	0.159	-
7.4265	1430	0.7672	-
7.4785	1440	0.6511	-
7.5306	1450	0.2682	-
7.5826	1460	1.5488	-
7.6346	1470	0.4513	-
7.6866	1480	0.7482	-
7.7386	1490	1.4327	-
7.7906	1500	1.0277	0.1801
7.8427	1510	0.4197	-
7.8947	1520	3.3415	-
7.9467	1530	0.7131	-
7.9987	1540	0.7276	-
8.0468	1550	1.1939	-
8.0988	1560	0.4333	-
8.1508	1570	1.3594	-
8.2029	1580	0.9792	-
8.2549	1590	0.4581	-
8.3069	1600	0.5785	-
8.3589	1610	0.4015	-
8.4109	1620	0.5693	-
8.4629	1630	1.4925	-
8.5150	1640	0.6028	-
8.5670	1650	0.2087	0.1802
8.6190	1660	1.0404	-
8.6710	1670	0.8293	-
8.7230	1680	1.1231	-
8.7750	1690	0.4747	-
8.8270	1700	1.0668	-
8.8791	1710	1.2665	-
8.9311	1720	0.3004	-
8.9831	1730	0.1333	-
9.0312	1740	1.0171	-
9.0832	1750	1.3999	-
9.1352	1760	0.1939	-
9.1873	1770	0.1591	-
9.2393	1780	0.1243	-
9.2913	1790	0.8689	-
9.3433	1800	0.4325	0.1501
9.3953	1810	0.5094	-
9.4473	1820	0.3178	-
9.4993	1830	0.211	-
9.5514	1840	1.3497	-
9.6034	1850	0.6287	-
9.6554	1860	0.4895	-
9.7074	1870	0.3925	-
9.7594	1880	0.4384	-
9.8114	1890	0.8487	-
9.8635	1900	0.9134	-
9.9155	1910	0.1522	-
9.9675	1920	0.3798	-

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},
}