File size: 54,465 Bytes
9b39880 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5220
- loss:CosineSimilarityLoss
base_model: intfloat/multilingual-e5-large-instruct
widget:
- source_sentence: Identify the column that stores the uncertainty value.
sentences:
- "What is measuring equipment?\nMeasuring equipment refers to the devices that\
\ make up a measurement system. Each piece of equipment has:\n- A unique serial\
\ number for identification.\n- A technical name, such as transmitter, plate,\
\ thermometer, etc.\n\nHow is equipment assigned to a measurement system?\nWhen\
\ equipment is assigned to a measurement system, it is given a unique identifier\
\ called an \"\"Equipment Tag.\"\"\n- If a piece of equipment has a tag, it is\
\ considered in use in a measurement system.\n- If it does not have a tag, it\
\ is considered spare or unused\n\nEquipment assignment based on technology:\n\
The type of equipment assigned to a measurement system depends on the technology\
\ used, for example:\n1. Differential technology (for gas measurement):\n -\
\ Static pressure transmitters\n - Differential pressure transmitters\n \
\ - Temperature transmitters\n - RTDs (thermometers)\n - Orifice plates\n\
\ - Straight stretch\n\n2. Linear technology (for gas measurement):\n -\
\ Temperature transmitters\n - RTDs\n - Static pressure transmitters\n \
\ - Ultrasonic meters\n\nRelationship between equipment and measurement systems:\n\
- A measurement system can have multiple pieces of equipment.\n- However, a piece\
\ of equipment can only be assigned to one measurement system.\n\nDatabase management:\n\
- The database includes a special table to manage the list of equipment assigned\
\ to measurement systems.\n- When a user refers to an \"\"Equipment Tag\"\", they\
\ are searching for operational equipment assigned to a measurement system.\n\
- If a user is looking for spare or unused equipment, they are searching for equipment\
\ not listed in the tagged equipment table.\n- Commonly used when user refers\
\ directly to an \"\"Equipment Tag\""
- '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.'
- source_sentence: SELECT * FROM EquipmentType LIMIT 1
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 does a flow computer generate and store reports?\nA 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.\n\nReport structure:\n\
- Each report includes:\n- Date and time of the data recording.\n- Data recorded\
\ from flow computers.\n\nData storage in tables:\nThe reports are saved in two\
\ tables:\n1. Main table (Index):\n - Stores the date, time, and flow computer\
\ identifier.\n2. Detail table:\n - Stores the measured values associated with\
\ the report.\n\nConnection to the Modbus table:\nThe 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 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).
Main function:
The flow computer sends the collected data to a measurement system. This allows
measurement engineers to analyze the data and perform their tasks effectively.'
- source_sentence: What tables store measurement system data?
sentences:
- "What is uncertainty?\nUncertainty 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.\n\nTypes\
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\
\ such as temperature or pressure.\n - It is calculated after calibrating a\
\ device or obtained from the equipment manufacturer's manual.\n - This uncertainty\
\ serves as a starting point for further calculations related to the equipment.\n\
\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated\
\ for the overall flow measurement.\n - It depends on the uncertainties of\
\ the individual variables (magnitudes) and represents the combined margin of\
\ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
\ (variables) are the foundation for calculating the uncertainty of the measurement\
\ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
\ types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific\
\ to individual variables (e.g., temperature, pressure).\n - **Uncertainty\
\ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
\ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
\ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores\
\ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values\
\ for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n\
- To find the uncertainty of the measurement system, join the measurement systems\
\ table with the uncertainty of the measurement system table.\n- To find the uncertainty\
\ of a specific variable (magnitude), join the measurement systems table with\
\ the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not\
\ confuse the two types of uncertainty:\n- If the user requests the uncertainty\
\ of the measurement system, use the first join (measurement systems table + uncertainty\
\ of the measurement system table).\n- 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)."
- "What is a measurement system?\nA measurement system, also referred to as a delivery\
\ point, measurement point, or reception point, is used to measure and monitor\
\ fluids in industrial processes.\n\nKey characteristics of a measurement system:\n\
1. Measurement technology:\n - Differential: Used for precise measurements.\n\
\ - Linear: Used for straightforward measurements.\n\n2. System identifier\
\ (TAG):\n - A unique identifier for the system.\n\n3. Fluid type:\n - The\
\ system can measure gases, oils, condensates, water, steam, or other fluids.\n\
4. System type:\n - Specifies the category or purpose of the system.\n\nMeasurement\
\ technology by fluid type:\n- Gas measurement systems: Use both linear and differential\
\ measurement technologies.\n- Oil measurement systems: Do not use linear or differential\
\ technologies; they are programmed differently.\"\n\n\nClassification of measurement\
\ systems:\nMeasurement systems are classified based on the stage of the process\
\ in which they are used. Common classifications include:\n- Fiscal\n- Operational\n\
- Appropriation\n- Custody\n- Production Poços"
- '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.'
- source_sentence: What is the table structure for equipment types?
sentences:
- "How does a flow computer generate and store reports?\nA 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.\n\nReport structure:\n\
- Each report includes:\n- Date and time of the data recording.\n- Data recorded\
\ from flow computers.\n\nData storage in tables:\nThe reports are saved in two\
\ tables:\n1. Main table (Index):\n - Stores the date, time, and flow computer\
\ identifier.\n2. Detail table:\n - Stores the measured values associated with\
\ the report.\n\nConnection to the Modbus table:\nThe 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 measuring equipment?\nMeasuring equipment refers to the devices that\
\ make up a measurement system. Each piece of equipment has:\n- A unique serial\
\ number for identification.\n- A technical name, such as transmitter, plate,\
\ thermometer, etc.\n\nHow is equipment assigned to a measurement system?\nWhen\
\ equipment is assigned to a measurement system, it is given a unique identifier\
\ called an \"\"Equipment Tag.\"\"\n- If a piece of equipment has a tag, it is\
\ considered in use in a measurement system.\n- If it does not have a tag, it\
\ is considered spare or unused\n\nEquipment assignment based on technology:\n\
The type of equipment assigned to a measurement system depends on the technology\
\ used, for example:\n1. Differential technology (for gas measurement):\n -\
\ Static pressure transmitters\n - Differential pressure transmitters\n \
\ - Temperature transmitters\n - RTDs (thermometers)\n - Orifice plates\n\
\ - Straight stretch\n\n2. Linear technology (for gas measurement):\n -\
\ Temperature transmitters\n - RTDs\n - Static pressure transmitters\n \
\ - Ultrasonic meters\n\nRelationship between equipment and measurement systems:\n\
- A measurement system can have multiple pieces of equipment.\n- However, a piece\
\ of equipment can only be assigned to one measurement system.\n\nDatabase management:\n\
- The database includes a special table to manage the list of equipment assigned\
\ to measurement systems.\n- When a user refers to an \"\"Equipment Tag\"\", they\
\ are searching for operational equipment assigned to a measurement system.\n\
- If a user is looking for spare or unused equipment, they are searching for equipment\
\ not listed in the tagged equipment table.\n- Commonly used when user refers\
\ directly to an \"\"Equipment Tag\""
- "What is uncertainty?\nUncertainty 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.\n\nTypes\
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\
\ such as temperature or pressure.\n - It is calculated after calibrating a\
\ device or obtained from the equipment manufacturer's manual.\n - This uncertainty\
\ serves as a starting point for further calculations related to the equipment.\n\
\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated\
\ for the overall flow measurement.\n - It depends on the uncertainties of\
\ the individual variables (magnitudes) and represents the combined margin of\
\ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
\ (variables) are the foundation for calculating the uncertainty of the measurement\
\ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
\ types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific\
\ to individual variables (e.g., temperature, pressure).\n - **Uncertainty\
\ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
\ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
\ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores\
\ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values\
\ for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n\
- To find the uncertainty of the measurement system, join the measurement systems\
\ table with the uncertainty of the measurement system table.\n- To find the uncertainty\
\ of a specific variable (magnitude), join the measurement systems table with\
\ the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not\
\ confuse the two types of uncertainty:\n- If the user requests the uncertainty\
\ of the measurement system, use the first join (measurement systems table + uncertainty\
\ of the measurement system table).\n- 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 columns store the uncertainty values?
sentences:
- "What is a measurement system?\nA measurement system, also referred to as a delivery\
\ point, measurement point, or reception point, is used to measure and monitor\
\ fluids in industrial processes.\n\nKey characteristics of a measurement system:\n\
1. Measurement technology:\n - Differential: Used for precise measurements.\n\
\ - Linear: Used for straightforward measurements.\n\n2. System identifier\
\ (TAG):\n - A unique identifier for the system.\n\n3. Fluid type:\n - The\
\ system can measure gases, oils, condensates, water, steam, or other fluids.\n\
4. System type:\n - Specifies the category or purpose of the system.\n\nMeasurement\
\ technology by fluid type:\n- Gas measurement systems: Use both linear and differential\
\ measurement technologies.\n- Oil measurement systems: Do not use linear or differential\
\ technologies; they are programmed differently.\"\n\n\nClassification of measurement\
\ systems:\nMeasurement systems are classified based on the stage of the process\
\ in which they are used. Common classifications include:\n- Fiscal\n- Operational\n\
- Appropriation\n- Custody\n- Production Poços"
- '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.'
- "What is uncertainty?\nUncertainty 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.\n\nTypes\
\ of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of\
\ magnitudes (variables):\n - Refers to the uncertainty of specific variables,\
\ such as temperature or pressure.\n - It is calculated after calibrating a\
\ device or obtained from the equipment manufacturer's manual.\n - This uncertainty\
\ serves as a starting point for further calculations related to the equipment.\n\
\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated\
\ for the overall flow measurement.\n - It depends on the uncertainties of\
\ the individual variables (magnitudes) and represents the combined margin of\
\ error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes\
\ (variables) are the foundation for calculating the uncertainty of the measurement\
\ system. Think of them as the \"building blocks.\"\n- Do not confuse the two\
\ types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific\
\ to individual variables (e.g., temperature, pressure).\n - **Uncertainty\
\ of the measurement system**: Specific to the overall flow measurement.\n\nDatabase\
\ storage for uncertainties:\nIn the database, uncertainty calculations are stored\
\ in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores\
\ the uncertainty values for specific variables (e.g., temperature, pressure).\n\
\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values\
\ for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n\
- To find the uncertainty of the measurement system, join the measurement systems\
\ table with the uncertainty of the measurement system table.\n- To find the uncertainty\
\ of a specific variable (magnitude), join the measurement systems table with\
\ the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not\
\ confuse the two types of uncertainty:\n- If the user requests the uncertainty\
\ of the measurement system, use the first join (measurement systems table + uncertainty\
\ of the measurement system table).\n- 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)."
datasets:
- Lauther/embeddings-train-semantic
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) 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](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision c9e87c786ffac96aeaeb42863276930883923ecb -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Lauther/emb-multilingual-e5-large-instruct-3e")
# Run inference
sentences = [
'What columns store the uncertainty values?',
'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 is uncertainty?\nUncertainty 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.\n\nTypes of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of magnitudes (variables):\n - Refers to the uncertainty of specific variables, such as temperature or pressure.\n - It is calculated after calibrating a device or obtained from the equipment manufacturer\'s manual.\n - This uncertainty serves as a starting point for further calculations related to the equipment.\n\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated for the overall flow measurement.\n - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of the measurement system. Think of them as the "building blocks."\n- Do not confuse the two types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).\n - **Uncertainty of the measurement system**: Specific to the overall flow measurement.\n\nDatabase storage for uncertainties:\nIn the database, uncertainty calculations are stored in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores the uncertainty values for specific variables (e.g., temperature, pressure).\n\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n- To find the uncertainty of the measurement system, join the measurement systems table with the uncertainty of the measurement system table.\n- To find the uncertainty of a specific variable (magnitude), join the measurement systems table with the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not confuse the two types of uncertainty:\n- If the user requests the uncertainty of the measurement system, use the first join (measurement systems table + uncertainty of the measurement system table).\n- 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).',
]
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### embeddings-train-semantic
* Dataset: [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) at [ce90f53](https://huggingface.co/datasets/Lauther/embeddings-train-semantic/tree/ce90f531bc39037053d223b27868ad178852f330)
* Size: 5,220 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 8 tokens</li><li>mean: 18.3 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 257.3 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>What is the data type of differential pressure in the measurement system?</code> | <code>What is uncertainty?<br>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.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>0.15000000000000002</code> |
| <code>What is the structure of the &&&equipment_data&&& table?</code> | <code>How are flow computers and measurement systems related?<br>Flow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.<br><br>Database terminology:<br>In the database, this relationship is referred to as:<br>- Meter streams<br>- Meter runs<br>- Sections<br><br>Storage of the relationship:<br>The relationship between a flow computer and its assigned measurement system is stored in a special table.<br><br>User context:<br>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.</code> | <code>0.35000000000000003</code> |
| <code>Find the columns in the flow computer table that identify the flow computer.</code> | <code>What kind of data store an equipment?<br>Equipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.<br><br>Data storage:<br>- 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.<br>- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.<br>- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.<br><br>Accessing the data:<br>- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.<br>- The readings are stored in a "variable values" table within the database.<br><br>Linking variable names:<br>If the user needs to kno...</code> | <code>0.1</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### embeddings-train-semantic
* Dataset: [embeddings-train-semantic](https://huggingface.co/datasets/Lauther/embeddings-train-semantic) at [ce90f53](https://huggingface.co/datasets/Lauther/embeddings-train-semantic/tree/ce90f531bc39037053d223b27868ad178852f330)
* Size: 652 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 652 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 8 tokens</li><li>mean: 17.8 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 253.84 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.24</li><li>max: 0.9</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>How can I filter uncertainty reports by equipment tag?</code> | <code>How does a flow computer generate and store reports?<br>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.<br><br>Report structure:<br>- Each report includes:<br>- Date and time of the data recording.<br>- Data recorded from flow computers.<br><br>Data storage in tables:<br>The reports are saved in two tables:<br>1. Main table (Index):<br> - Stores the date, time, and flow computer identifier.<br>2. Detail table:<br> - Stores the measured values associated with the report.<br><br>Connection to the Modbus table:<br>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.</code> | <code>0.09999999999999999</code> |
| <code>What is the purpose of the flow_data table?</code> | <code>What is uncertainty?<br>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.<br><br>Types of uncertainty:<br>There are two main types of uncertainty:<br>1. Uncertainty of magnitudes (variables):<br> - Refers to the uncertainty of specific variables, such as temperature or pressure.<br> - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.<br> - This uncertainty serves as a starting point for further calculations related to the equipment.<br><br>2. Uncertainty of the measurement system:<br> - Refers to the uncertainty calculated for the overall flow measurement.<br> - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.<br><br>Key points:<br>- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of ...</code> | <code>0.15000000000000002</code> |
| <code>What is the column name for the report date in the Reports table?</code> | <code>What is equipment calibration?<br>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.<br><br>Purpose of calibration:<br>The calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data.<br><br>Calibration cycles:<br>There are two main calibration cycles:<br>1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented.<br>2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements.<br><br>Calibration uncertainty:<br>- Uncertainty is included in the results of a calibration.<br>- Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or ...</code> | <code>0.1</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### 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
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `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`: 3
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0307 | 10 | 1.5374 | - |
| 0.0613 | 20 | 1.0251 | - |
| 0.0920 | 30 | 0.361 | - |
| 0.1226 | 40 | 0.1819 | - |
| 0.1533 | 50 | 0.186 | - |
| 0.1839 | 60 | 0.1697 | - |
| 0.2146 | 70 | 0.1437 | - |
| 0.2452 | 80 | 0.172 | - |
| 0.2759 | 90 | 0.1199 | - |
| 0.3065 | 100 | 0.1278 | - |
| 0.3372 | 110 | 0.1037 | - |
| 0.3678 | 120 | 0.1156 | - |
| 0.3985 | 130 | 0.0971 | - |
| 0.4291 | 140 | 0.0911 | - |
| 0.4598 | 150 | 0.1158 | 0.0249 |
| 0.4904 | 160 | 0.0906 | - |
| 0.5211 | 170 | 0.106 | - |
| 0.5517 | 180 | 0.0921 | - |
| 0.5824 | 190 | 0.0748 | - |
| 0.6130 | 200 | 0.0741 | - |
| 0.6437 | 210 | 0.0894 | - |
| 0.6743 | 220 | 0.0815 | - |
| 0.7050 | 230 | 0.0771 | - |
| 0.7356 | 240 | 0.1156 | - |
| 0.7663 | 250 | 0.0857 | - |
| 0.7969 | 260 | 0.0566 | - |
| 0.8276 | 270 | 0.0716 | - |
| 0.8582 | 280 | 0.0662 | - |
| 0.8889 | 290 | 0.0963 | - |
| 0.9195 | 300 | 0.0678 | 0.0212 |
| 0.9502 | 310 | 0.077 | - |
| 0.9808 | 320 | 0.0642 | - |
| 1.0092 | 330 | 0.0725 | - |
| 1.0398 | 340 | 0.0701 | - |
| 1.0705 | 350 | 0.0549 | - |
| 1.1011 | 360 | 0.0699 | - |
| 1.1318 | 370 | 0.0714 | - |
| 1.1625 | 380 | 0.0745 | - |
| 1.1931 | 390 | 0.0754 | - |
| 1.2238 | 400 | 0.0486 | - |
| 1.2544 | 410 | 0.047 | - |
| 1.2851 | 420 | 0.076 | - |
| 1.3157 | 430 | 0.0689 | - |
| 1.3464 | 440 | 0.0629 | - |
| 1.3770 | 450 | 0.0657 | 0.0178 |
| 1.4077 | 460 | 0.0622 | - |
| 1.4383 | 470 | 0.0657 | - |
| 1.4690 | 480 | 0.0498 | - |
| 1.4996 | 490 | 0.0653 | - |
| 1.5303 | 500 | 0.0715 | - |
| 1.5609 | 510 | 0.0615 | - |
| 1.5916 | 520 | 0.0441 | - |
| 1.6222 | 530 | 0.0566 | - |
| 1.6529 | 540 | 0.0524 | - |
| 1.6835 | 550 | 0.0423 | - |
| 1.7142 | 560 | 0.0441 | - |
| 1.7448 | 570 | 0.0553 | - |
| 1.7755 | 580 | 0.0572 | - |
| 1.8061 | 590 | 0.0686 | - |
| 1.8368 | 600 | 0.06 | 0.0146 |
| 1.8674 | 610 | 0.0562 | - |
| 1.8981 | 620 | 0.0517 | - |
| 1.9287 | 630 | 0.0498 | - |
| 1.9594 | 640 | 0.0424 | - |
| 1.9900 | 650 | 0.0729 | - |
| 2.0184 | 660 | 0.0347 | - |
| 2.0490 | 670 | 0.06 | - |
| 2.0797 | 680 | 0.0441 | - |
| 2.1103 | 690 | 0.0409 | - |
| 2.1410 | 700 | 0.0416 | - |
| 2.1716 | 710 | 0.0345 | - |
| 2.2023 | 720 | 0.024 | - |
| 2.2330 | 730 | 0.0458 | - |
| 2.2636 | 740 | 0.0465 | - |
| 2.2943 | 750 | 0.0494 | 0.0132 |
| 2.3249 | 760 | 0.0388 | - |
| 2.3556 | 770 | 0.0363 | - |
| 2.3862 | 780 | 0.0441 | - |
| 2.4169 | 790 | 0.0378 | - |
| 2.4475 | 800 | 0.0484 | - |
| 2.4782 | 810 | 0.051 | - |
| 2.5088 | 820 | 0.0464 | - |
| 2.5395 | 830 | 0.036 | - |
| 2.5701 | 840 | 0.0423 | - |
| 2.6008 | 850 | 0.0278 | - |
| 2.6314 | 860 | 0.0474 | - |
| 2.6621 | 870 | 0.0357 | - |
| 2.6927 | 880 | 0.0386 | - |
| 2.7234 | 890 | 0.0334 | - |
| 2.7540 | 900 | 0.0199 | 0.0127 |
| 2.7847 | 910 | 0.0381 | - |
| 2.8153 | 920 | 0.0415 | - |
| 2.8460 | 930 | 0.0274 | - |
| 2.8766 | 940 | 0.0353 | - |
| 2.9073 | 950 | 0.0423 | - |
| 2.9379 | 960 | 0.0267 | - |
| 2.9686 | 970 | 0.042 | - |
### 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
```bibtex
@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",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |