Views
No views yet
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()
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("Lauther/d4-embeddings-v3.0-tl")
5# Run inference
6sentences = [
7 'PTE BRAGANÇA PAULISTA C',
8 'What is an Uncertainty Composition?\nAn 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.\n\nKey Aspects of an Uncertainty Component:\n- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.\n- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.\n- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.\nUnderstanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision.',
9 'What is a Measurement Unit?\nA Measurement Unit defines the standard for quantifying a physical magnitude (e.g., temperature, pressure, volume). It establishes a consistent reference for interpreting values recorded in a measurement system.\n\nEach measurement unit is associated with a specific magnitude, ensuring that values are correctly interpreted within their context. For example:\n\n- °C (Celsius) → Used for temperature\n- psi (pounds per square inch) → Used for pressure\n- m³ (cubic meters) → Used for volume\nMeasurement units are essential for maintaining consistency across recorded data, ensuring comparability, and enabling accurate calculations within measurement systems.',
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 1024]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]anchor, positive, and negative| anchor | positive | negative | |
|---|---|---|---|
| type | string | string | string |
| details |
|
|
|
| anchor | positive | negative |
|---|---|---|
Orifice Diameter (mm) | What is uncertainty?[object Object]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.[object Object][object Object]Types of uncertainty:[object Object]There are two main types of uncertainty:[object Object]1. Uncertainty of magnitudes (variables):[object Object] - Refers to the uncertainty of specific variables, such as temperature or pressure.[object Object] - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.[object Object] - This uncertainty serves as a starting point for further calculations related to the equipment.[object Object][object Object]2. Uncertainty of the measurement system:[object Object] - Refers to the uncertainty calculated for the overall flow measurement.[object Object] - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.[object Object][object Object]Key points:[object Object]- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ... | What is an Equipment Class?[object Object]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.[object Object][object Object]Each Equipment Class groups related equipment under a common category. Examples include:[object Object][object Object]Primary → Main measurement device in a system.[object Object]Secondary → Supporting measurement device, often used for verification.[object Object]Tertiary → Additional measurement equipment.[object Object]Valves → Flow control devices used in the system.[object Object]By defining Equipment Classes, the system ensures proper identification, tracking, and management of measurement-related assets. |
prueba_gonzalo | What is a measurement system?[object Object][object Object] are essential components in industrial measurement and processing. They are identified by a unique [object Object] and are associated with a specific [object Object] and [object Object]. These systems utilize different [object Object], including [object Object] and [object Object], depending on the application. Measurement systems can be classified based on their [object Object], such as [object Object] or [object Object]. | What is a Measured Magnitude Value?[object Object]A Measured Magnitude Value represents a [object Object] recorded physical measurement of a variable within a monitored fluid. These values are essential for tracking system performance, analyzing trends, and ensuring accurate monitoring of fluid properties.[object Object][object Object]Key Aspects of a Measured Magnitude Value:[object Object]- Measurement Date: The timestamp indicating when the measurement was recorded.[object Object]- Measured Value: The daily numeric result of the recorded physical magnitude.[object Object]- Measurement System Association: Links the measured value to a specific measurement system responsible for capturing the data.[object Object]- Variable Association: Identifies the specific variable (e.g., temperature, pressure, flow rate) corresponding to the recorded value.[object Object]Measured magnitude values are crucial for real-time monitoring, historical analysis, and calibration processes within measurement systems.[object Object][object Object]Database advices:[object Object]This values also are in [object Object]. Although, to go directl... |
Vazao Instantanea | What is uncertainty?[object Object]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.[object Object][object Object]Types of uncertainty:[object Object]There are two main types of uncertainty:[object Object]1. Uncertainty of magnitudes (variables):[object Object] - Refers to the uncertainty of specific variables, such as temperature or pressure.[object Object] - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.[object Object] - This uncertainty serves as a starting point for further calculations related to the equipment.[object Object][object Object]2. Uncertainty of the measurement system:[object Object] - Refers to the uncertainty calculated for the overall flow measurement.[object Object] - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.[object Object][object Object]Key points:[object Object]- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ... | What is a report index or historic index?[object Object]Indexes represent the recorded reports generated by flow computers, classified into two types: [object Object]- [object Object]: Store data for hourly events.[object Object]- [object Object]: Strore data for daily events.[object Object][object Object]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.[object Object][object Object]The index is essential for locating specific values within the report. |
TripletLoss with these parameters:
1{
2 "distance_metric": "TripletDistanceMetric.COSINE",
3 "triplet_margin": 0.5
4}anchor, positive, and negative| anchor | positive | negative | |
|---|---|---|---|
| type | string | string | string |
| details |
|
|
|
| anchor | positive | negative |
|---|---|---|
FQI-4301.4522B | What is a measurement system?[object Object][object Object] are essential components in industrial measurement and processing. They are identified by a unique [object Object] and are associated with a specific [object Object] and [object Object]. These systems utilize different [object Object], including [object Object] and [object Object], depending on the application. Measurement systems can be classified based on their [object Object], such as [object Object] or [object Object]. | What is uncertainty?[object Object]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.[object Object][object Object]Types of uncertainty:[object Object]There are two main types of uncertainty:[object Object]1. Uncertainty of magnitudes (variables):[object Object] - Refers to the uncertainty of specific variables, such as temperature or pressure.[object Object] - It is calculated after calibrating a device or obtained from the [object Object] manufacturer's manual.[object Object] - This uncertainty serves as a starting point for further calculations related to the equipment.[object Object][object Object]2. Uncertainty of the measurement system:[object Object] - Refers to the uncertainty calculated for the overall flow measurement.[object Object] - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.[object Object][object Object]Key points:[object Object]- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty... |
PTE GUARATINGUETA B | What are historical report values?[object Object]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.[object Object][object Object]Flow computer reports store two types of data values:[object Object][object Object]- [object Object]: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on an hourly basis.[object Object]- [object Object]: Contain measured or calculated values (e.g., operational minutes, alarms set, etc.) recorded on a daily basis.[object Object]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. | What is Equipment?[object Object]An Equipment represents a physical device that may be used within a measurement system. Equipment can be active or inactive and is classified by type, such as transmitters, thermometers, or other measurement-related devices.[object Object][object Object]Key Aspects of Equipment:[object Object]- Serial Number: A unique identifier assigned to each equipment unit for tracking and reference.[object Object]- Current State: Indicates whether the equipment is currently in use (ACT) or inactive (INA).[object Object]- Associated Equipment Type: Defines the category of the equipment (e.g., transmitter, thermometer), allowing classification and management.[object Object]Equipment plays a critical role in measurement systems, ensuring accuracy and reliability in data collection and processing. |
PTE BRAGANÇA PAULISTA B | What is an Uncertainty Composition?[object Object]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.[object Object][object Object]Key Aspects of an Uncertainty Component:[object Object]- Component Name: Defines the uncertainty factor (e.g., diameter, density, variance, covariance) influencing the measurement system.[object Object]- Value of Composition: Quantifies the component’s contribution to the total uncertainty, helping to analyze which factors have the greatest impact.[object Object]- Uncertainty File ID: Links the component to a specific uncertainty dataset for traceability and validation.[object Object]Understanding these components is critical for uncertainty analysis, ensuring compliance with industry standards and improving measurement precision. | What is uncertainty?[object Object]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.[object Object][object Object]Types of uncertainty:[object Object]There are two main types of uncertainty:[object Object]1. Uncertainty of magnitudes (variables):[object Object] - Refers to the uncertainty of specific variables, such as temperature or pressure.[object Object] - It is calculated after calibrating a device or obtained from the [object Object] manufacturer's manual.[object Object] - This uncertainty serves as a starting point for further calculations related to the equipment.[object Object][object Object]2. Uncertainty of the measurement system:[object Object] - Refers to the uncertainty calculated for the overall flow measurement.[object Object] - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.[object Object][object Object]Key points:[object Object]- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty... |
TripletLoss with these parameters:
1{
2 "distance_metric": "TripletDistanceMetric.COSINE",
3 "triplet_margin": 0.5
4}eval_strategy: stepsper_device_train_batch_size: 80per_device_eval_batch_size: 80weight_decay: 0.01max_grad_norm: 0.5num_train_epochs: 10lr_scheduler_type: cosinewarmup_ratio: 0.1fp16: Truedataloader_num_workers: 4overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 80per_device_eval_batch_size: 80per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.01adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 0.5num_train_epochs: 10max_steps: -1lr_scheduler_type: cosinelr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Truefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 4dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: proportional| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.2885 | 15 | 0.447 | - |
| 0.5769 | 30 | 0.1328 | - |
| 0.8654 | 45 | 0.0629 | - |
| 1.1538 | 60 | 0.027 | - |
| 1.4423 | 75 | 0.0258 | - |
| 1.7308 | 90 | 0.0296 | - |
| 2.0192 | 105 | 0.0168 | - |
| 2.3077 | 120 | 0.0155 | - |
| 2.5962 | 135 | 0.0183 | - |
| 2.8846 | 150 | 0.0106 | 0.0153 |
| 3.1731 | 165 | 0.0258 | - |
| 3.4615 | 180 | 0.0128 | - |
| 3.75 | 195 | 0.007 | - |
| 4.0385 | 210 | 0.0089 | - |
| 4.3269 | 225 | 0.0079 | - |
| 4.6154 | 240 | 0.0094 | - |
| 4.9038 | 255 | 0.0052 | - |
| 5.1923 | 270 | 0.0084 | - |
| 5.4808 | 285 | 0.0071 | - |
| 5.7692 | 300 | 0.0075 | 0.0098 |
| 6.0577 | 315 | 0.0057 | - |
| 6.3462 | 330 | 0.0048 | - |
| 6.6346 | 345 | 0.0037 | - |
| 6.9231 | 360 | 0.0053 | - |
| 7.2115 | 375 | 0.0039 | - |
| 7.5 | 390 | 0.0027 | - |
| 7.7885 | 405 | 0.0069 | - |
| 8.0769 | 420 | 0.0033 | - |
| 8.3654 | 435 | 0.0021 | - |
| 8.6538 | 450 | 0.0038 | 0.0086 |
| 8.9423 | 465 | 0.0034 | - |
| 9.2308 | 480 | 0.0034 | - |
| 9.5192 | 495 | 0.003 | - |
| 9.8077 | 510 | 0.0028 | - |
1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}1@misc{hermans2017defense,
2 title={In Defense of the Triplet Loss for Person Re-Identification},
3 author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
4 year={2017},
5 eprint={1703.07737},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV}
8}