File size: 62,680 Bytes
16c59c8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 |
---
base_model: Snowflake/snowflake-arctic-embed-m
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@5
- cosine_precision@10
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@5
- cosine_ndcg@10
- cosine_mrr@5
- cosine_mrr@10
- cosine_map@5
- cosine_map@10
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- loss:CoSENTLoss
- dataset_size:7232
- loss:WeightedMultipleNegativesRankingLoss
widget:
- source_sentence: ', antenna, or other sensor to attain mission performance levels
that
currently cannot be achieved by a monolithic satellite. Most aspects of this concept
have been widely studied, but
the first implementation has yet to be realized, with the exception of a few initial
experiments.
A distributed satellite system taxonomy is shown in Fig. 1 with a discussion of
current and planned systems to
follow. At the end of this section, a candidate distributed space mission is presented
as a common reference for
Table 1 presents a selection of current distributed satellite systems, grouped
in the four typical mission
categories'
sentences:
- 'What is the precision that the system is aiming for in terms of tracking error?
'
- 'What is the main challenge in implementing a distributed satellite system?
'
- 'Who are the authors of the NASA document "Space Radiation Cancer Risk Projections
for Explorative Missions: Uncertainty Reduction and Mitigation"?
'
- source_sentence: ':250,000 scale for regional context) . Near-term efforts should
focus on high-priority locations .
[16] Terrain hazard (e .g ., slope, surface roughness), line-of-sight (i .e .,
viewshed), and time-dependent
illumination maps at appropriate scales (e .g ., best-available supported by the
data) are high-priority derived products essential in mission planning, and they
should be made available as soon as possible .
[17] South polar data products could be initially controlled to coarser data and
known surface reference points to support early Artemis missions and other surface
activities, but establishment of a local control network applied to all necessary
data layers would facilitate interoperability and provide more precision for specific
sites .
Higher-order data products are tied to controlled foundational data and are derived
from source data, such as measurements of elemental abundance, temperature or
reflectance at multiple wavelengths, observations of solar illumination, and output
from space weather models . Higher-order data products derived from these source
data will play an essential role in planning and executing south polar missions
. Planning the science activities to be carried out on the lunar surface will
be based on these higher-order data products, and, in turn, the science returned
by those activities will be used to update those same products . For example,
geologic maps based on remotely sensed data prior to early Artemis landings will
be a likely outcome of site assessments and will form the critical basis for traverse
plans and planning of science tasks . The observations, samples, and measurements
made during Artemis surface activities will feed back into updating the geologic
maps, to the benefit of future crewed or robotic missions to the same area . Similarly,
resource maps will drive the selection of landing sites for missions focused on
resource discovery, characterization, and utilization, and the findings of those
missions will be used to iteratively update the resource maps . In these cases,
and others'
sentences:
- 'What are the specifications of the Theia imager that make it suitable for quantitative
remote sensing studies?
'
- 'Who supported the first study?
'
- 'What are the essential derived products in mission planning, and why are they
crucial for south polar missions?
'
- source_sentence: ', there are still
some challenges to be overcome it is shown that it is possible to perform such
links. Furthermore,
recommendations for future operations of optical links were provided.
FLP is also integrated in the educational aspects of the Institute. Many future
aerospace engineers were
trained for satellite operations and Earth Observations and the satellite will
be used to train operators
Further investigation of the Attitude Control is required for the stabilization
of the optical links on
other G/S as Oberpfaffenhofen. However, future projects might benefit from more
standardization on
the side of G/S Feedback for optical links. Overall Flying Laptop is a stable
platform for technology demonstration, Earth Observation, and ed-
588. [Online]. Available'
sentences:
- 'What are the remaining challenges that need to be addressed for the successful
implementation of optical links?
'
- 'What are the benefits of enhancing the radiometric resolution of VLEO satellite
systems?
'
- 'What is the reason for using the uncoupled approach for the radiation calculations
in this study?
'
- source_sentence: ': they are visible on the waterfall plots with a very high amplitude.
Moreover, some peaks appear on waterfall plots while they are not
visible on zero speed curves. These peaks correspond to first order unbalance,
engine orders or wheel eigenmodes. By repeating the tests with different configurations
(without ventilation, changing the axes, etc...), conclusions have been made and
are presented in table 4.
It is necessary to check if the modes presented in table 4 do not cross the order
1 unbalance or the rocking mode. The visible lines starting from the origin and
evolving with the rotation speed of the wheel are the engine orders due to the
imperfections of the wheel. When they cross modes of the wheel, the amplitudes
corresponding to the crossing are much higher as we can clearly see in Table 2,
on the x axis waterfall plots at 1050 Hz and 4000 RPM. The waterfall plots allow
to have a global view on the wheel structure. By looking at these curves, two
wheels can be compared. For example, higher amplitudes on engine orders mean that
the wheel has defects. Moreover, a shift of the rocking mode means that the parameters
of the wheel are different as shown in equations 4.
Table 3 summarizes the static and dynamic unbalances calculated on three wheels.
We notice that they all have the same order of magnitude. Environmental vibration
and shock tests can vary this value by damaging the wheel. On the other hand,
bearing defects can be reduced when the wheel is continuously rotated due to the
running-in process, which can decrease the unbalance value. In general, environmental
testing has more impact than running-in.
When the frequencies are low, the wheel has no trouble following the setpoint.
At high frequencies, the wheel follows the setpoint but with a loss of amplitude
and a phase shift'
sentences:
- 'What are the peaks that appear on waterfall plots but not on zero speed curves?
'
- 'Why is separately scheduling the imaging and download tasks a natural choice
for real-world complex systems?
'
- 'What are the dominant orbit determination uncertainties?
'
- source_sentence: ': Block diagram of the 7-band CCD-in-CMOS TDI sensor. Each TX
slice has two serializers and its own PLL.
The CCD bands operate continuously and time interleaved. The output stages for
the CCD arrays are implemented both at the top and bottom of each band to support
the bi-directional operation. All 14 output stages in one column are connected
to one delta-sigma column-level ADC with digital CDS implemented in the digital
decimator. The outputs of every 128 ADCs are serialized to one of 32 LVDS outputs.
Two clock signals are also provided via LVDS to synchronize the channels. These
outputs are capable of running at an aggregate data rate of >50Gb/s using on-chip
PLLs.
The sensor has been processed for Back-Side Illumination and it has been packaged
in a custom ceramic PGA package. Figure 15 shows a picture of the sensor with
its 7 bands. The figure shows the front-side and back-side versions of the chip
side by side.
(a) (b) Figure 15: 7-band CCD-in-CMOS TDI chip photograph. FSI shown only for
reference (a) and BSI version (b).
As a proof-of-concept, an RGB butcher-brick filter has been used as glass lid
for the sensor, to enable multicolor TDI, although filters may be processed directly
on the wafer as well [9]. The sensor,
camera system and a color image captured from the setup are depicted in Figure
16, providing evidence that multispectral TDI is viable with the sensor.
Figure 16: Colour TDI image captured from the sensor, sensor with RGB color filter
and camera set-up.
Table 3 below shows a comparison of different TDI sensors, including the first
iteration of our sensor.
Integrated drivers
The measurements on the first iteration of the SoC verified'
sentences:
- 'What is the primary objective of the Zodiac Pioneer Mission?
'
- 'What is the main topic of the papers listed in the context?
'
- 'What is the aggregate data rate of the outputs of the 7-band CCD-in-CMOS TDI
sensor?
'
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@5
value: 0.8407960199004975
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8843283582089553
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.16815920398009948
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08843283582089552
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.8407960199004975
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8843283582089553
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.749593576396566
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7638900783774348
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.7189676616915421
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.7249965450525153
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.7189676616915422
name: Cosine Map@5
- type: cosine_map@10
value: 0.7249965450525152
name: Cosine Map@10
- type: cosine_accuracy@5
value: 0.9198717948717948
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9551282051282052
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.18397435897435896
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0955128205128205
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.9198717948717948
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9551282051282052
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.786039298615645
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7975208279742617
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.740758547008547
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.7455369861619862
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.740758547008547
name: Cosine Map@5
- type: cosine_map@10
value: 0.7455369861619863
name: Cosine Map@10
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@5
value: 0.8345771144278606
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8781094527363185
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.16691542288557212
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08781094527363183
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.8345771144278606
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8781094527363185
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.7384076037005772
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7524024562602603
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.7060530679933663
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.7117739674642659
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.7060530679933666
name: Cosine Map@5
- type: cosine_map@10
value: 0.7117739674642659
name: Cosine Map@10
- type: cosine_accuracy@5
value: 0.907051282051282
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9519230769230769
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.1814102564102564
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09519230769230767
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.907051282051282
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9519230769230769
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.7793612708940784
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7942949173487753
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.7363247863247866
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.7427375864875867
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.7363247863247864
name: Cosine Map@5
- type: cosine_map@10
value: 0.7427375864875865
name: Cosine Map@10
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@5
value: 0.8146766169154229
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8631840796019901
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.16293532338308458
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08631840796019902
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.8146766169154229
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8631840796019901
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.7159371426767726
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.731814701526023
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.6826907131011605
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.6893587617468213
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.6826907131011608
name: Cosine Map@5
- type: cosine_map@10
value: 0.6893587617468214
name: Cosine Map@10
- type: cosine_accuracy@5
value: 0.8846153846153846
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9455128205128205
name: Cosine Accuracy@10
- type: cosine_precision@5
value: 0.1769230769230769
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09455128205128205
name: Cosine Precision@10
- type: cosine_recall@5
value: 0.8846153846153846
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9455128205128205
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.7547512036424451
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.7747939646301274
name: Cosine Ndcg@10
- type: cosine_mrr@5
value: 0.7107905982905985
name: Cosine Mrr@5
- type: cosine_mrr@10
value: 0.7192778286528287
name: Cosine Mrr@10
- type: cosine_map@5
value: 0.7107905982905982
name: Cosine Map@5
- type: cosine_map@10
value: 0.7192778286528286
name: Cosine Map@10
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co./Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co./Snowflake/snowflake-arctic-embed-m) <!-- at revision 71bc94c8f9ea1e54fba11167004205a65e5da2cc -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("federicovolponi/Snowflake-snowflake-arctic-embed-m-space-sup")
# Run inference
sentences = [
': Block diagram of the 7-band CCD-in-CMOS TDI sensor. Each TX slice has two serializers and its own PLL.\nThe CCD bands operate continuously and time interleaved. The output stages for the CCD arrays are implemented both at the top and bottom of each band to support the bi-directional operation. All 14 output stages in one column are connected to one delta-sigma column-level ADC with digital CDS implemented in the digital decimator. The outputs of every 128 ADCs are serialized to one of 32 LVDS outputs. Two clock signals are also provided via LVDS to synchronize the channels. These outputs are capable of running at an aggregate data rate of >50Gb/s using on-chip PLLs.\nThe sensor has been processed for Back-Side Illumination and it has been packaged in a custom ceramic PGA package. Figure 15 shows a picture of the sensor with its 7 bands. The figure shows the front-side and back-side versions of the chip side by side.\n(a) (b) Figure 15: 7-band CCD-in-CMOS TDI chip photograph. FSI shown only for reference (a) and BSI version (b).\nAs a proof-of-concept, an RGB butcher-brick filter has been used as glass lid for the sensor, to enable multicolor TDI, although filters may be processed directly on the wafer as well [9]. The sensor,\ncamera system and a color image captured from the setup are depicted in Figure 16, providing evidence that multispectral TDI is viable with the sensor.\nFigure 16: Colour TDI image captured from the sensor, sensor with RGB color filter and camera set-up.\nTable 3 below shows a comparison of different TDI sensors, including the first iteration of our sensor.\nIntegrated drivers\nThe measurements on the first iteration of the SoC verified',
'What is the aggregate data rate of the outputs of the 7-band CCD-in-CMOS TDI sensor?\n\n',
'What is the primary objective of the Zodiac Pioneer Mission?\n\n',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@5 | 0.8408 |
| cosine_accuracy@10 | 0.8843 |
| cosine_precision@5 | 0.1682 |
| cosine_precision@10 | 0.0884 |
| cosine_recall@5 | 0.8408 |
| cosine_recall@10 | 0.8843 |
| cosine_ndcg@5 | 0.7496 |
| cosine_ndcg@10 | 0.7639 |
| cosine_mrr@5 | 0.719 |
| cosine_mrr@10 | 0.725 |
| cosine_map@5 | 0.719 |
| **cosine_map@10** | **0.725** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.8346 |
| cosine_accuracy@10 | 0.8781 |
| cosine_precision@5 | 0.1669 |
| cosine_precision@10 | 0.0878 |
| cosine_recall@5 | 0.8346 |
| cosine_recall@10 | 0.8781 |
| cosine_ndcg@5 | 0.7384 |
| cosine_ndcg@10 | 0.7524 |
| cosine_mrr@5 | 0.7061 |
| cosine_mrr@10 | 0.7118 |
| cosine_map@5 | 0.7061 |
| **cosine_map@10** | **0.7118** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.8147 |
| cosine_accuracy@10 | 0.8632 |
| cosine_precision@5 | 0.1629 |
| cosine_precision@10 | 0.0863 |
| cosine_recall@5 | 0.8147 |
| cosine_recall@10 | 0.8632 |
| cosine_ndcg@5 | 0.7159 |
| cosine_ndcg@10 | 0.7318 |
| cosine_mrr@5 | 0.6827 |
| cosine_mrr@10 | 0.6894 |
| cosine_map@5 | 0.6827 |
| **cosine_map@10** | **0.6894** |
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.9199 |
| cosine_accuracy@10 | 0.9551 |
| cosine_precision@5 | 0.184 |
| cosine_precision@10 | 0.0955 |
| cosine_recall@5 | 0.9199 |
| cosine_recall@10 | 0.9551 |
| cosine_ndcg@5 | 0.786 |
| cosine_ndcg@10 | 0.7975 |
| cosine_mrr@5 | 0.7408 |
| cosine_mrr@10 | 0.7455 |
| cosine_map@5 | 0.7408 |
| **cosine_map@10** | **0.7455** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.9071 |
| cosine_accuracy@10 | 0.9519 |
| cosine_precision@5 | 0.1814 |
| cosine_precision@10 | 0.0952 |
| cosine_recall@5 | 0.9071 |
| cosine_recall@10 | 0.9519 |
| cosine_ndcg@5 | 0.7794 |
| cosine_ndcg@10 | 0.7943 |
| cosine_mrr@5 | 0.7363 |
| cosine_mrr@10 | 0.7427 |
| cosine_map@5 | 0.7363 |
| **cosine_map@10** | **0.7427** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@5 | 0.8846 |
| cosine_accuracy@10 | 0.9455 |
| cosine_precision@5 | 0.1769 |
| cosine_precision@10 | 0.0946 |
| cosine_recall@5 | 0.8846 |
| cosine_recall@10 | 0.9455 |
| cosine_ndcg@5 | 0.7548 |
| cosine_ndcg@10 | 0.7748 |
| cosine_mrr@5 | 0.7108 |
| cosine_mrr@10 | 0.7193 |
| cosine_map@5 | 0.7108 |
| **cosine_map@10** | **0.7193** |
<!--
## 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
#### Unnamed Dataset
* Size: 7,232 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 354.69 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 19.21 tokens</li><li>max: 40 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
| <code>, using diverse software or hardware designs may double design and verification costs due to having to build two different components for the same functionality. Hence, although DCLS execution also halves performance efficiency (the corresponding functionality is executed twice), it allows reusing the same design (e.g. the same core design) for the primary and the redundant paths (e.g. with staggered execution), thus containing design and verification costs.<br>Redundancy can be applied at different granularities accord- ing to the sphere of replication (SoR). Choosing the right SoR depends on several tradeoffs like area overheads, re- design costs, fault detection time, and overall system costs. In the context of DCLS, the SoR is placed at the level of the CPU (core), as done for the AURIX processors. This requires including two replicas of the same core and compare their memory transactions, which requires roughly duplicating com- putational resources in the chip and being able to ensure that replicas can provide independent behavior. On the other hand, storage (memories, caches) and communication means (buses, crossbars) do not need to be fully replicated and can build upon Error Correction Codes (ECC) and Cyclic Redundancy Check (CRC) as a form of lightweight redundancy with diversity.<br>HPC ASIL-D capable platforms typically combine a low- performance microcontroller amenable for the automotive do- main (i.e. ASIL-D capable) and an HPC accelerator deliv- ering high computation throughput, but whose adherence to ISO26262 requirements is unknown, so its appropriate use for ASIL-C/D systems needs to be investigated. Without loss of generality, we consider an NVIDIA GPU accelerator, thus analogous to those in NVIDIA Drive and Xavier families for the automotive domain. However, the findings in this paper can easily be extrapolated to other products.<br>Software faults and some hardware faults are regarded as systematic, and it must be proven that their failure risk is residual. However, random hardware faults cannot be avoided, and means are required to prevent them from causing hazards. Those faults can be caused by, for example, voltage droops</code> | <code>What are the advantages of using the same design for the primary and redundant paths in DCLS execution?<br><br></code> |
| <code>: First, the TT&C spectrum requirements of the new satellites shall be assessed. Second, the utilization of existing TT&C frequency allocations and their potential to incorporate the future number of satellites is studied. Only for the case that this study results in the need for new spectrum, the study groups were asked to investigate new potential TT&C frequency allocations in the frequency ranges 150.05-174 MHz and 400.15-420 MHz. The studies shall be completed for WRC-19.<br>This paper presents the intermediate results of the study groups. A study of the spectrum requirements of small satellites has been completed. The required spectrum for TT&C is expected to be less than 2.5 MHz for downlink and less than 1 MHz for uplink. Consequently, the study groups conducted sharing studies in various bands which will be summarized and evaluated from a satellite developer’s perspective.<br>After the Cubesat design standard was introduced in 1999 and first satellites of this new class have been launched in the subsequent years, small satellites have become increasingly popular in the past five years. Today not only universities use small satellite platforms for education and technology demonstration, but also commercial operators started to develop and deploy satellites with masses of typically less than 50 kg and reasonably short development times. Currently more than hundred new satellites are currently launched into space per year. The increase of launches was recognized by the International Telecommunication Union (ITU) which is responsible for the coordination of the shared use of frequencies. As the first Cubesats were mainly launched by new entrants into the space sector, mandatory regulatory procedures like frequency coordination were omitted or underestimated by the developers. Additionally, the new developers complaint that the existing regulatory procedures are too complicated and time-consuming for satellites with short development times. The ITU therefore decided at the WRC-12 to study the characteristics of picosatellites and nanosatellites and their current practice in filing satellites to the ITU. The studies were concluded in 2015 with two reports on the characteristics [1] and current filing practice [2]. In these reports it was identified that the characteristics that define small satellites (low mass, small dimensions, low power, …) are not relevant from a frequency coordination perspective and that the short development times are still long enough to properly file the systems to the ITU. As a result</code> | <code>What are the spectrum requirements for TT&C of small satellites?<br><br></code> |
| <code>:287–299, Dec 2019.<br>[20] Tam´as Vink´o and Dario Izzo. Global optimi- sation heuristics and test problems for prelimi- nary spacecraft trajectory design. Technical re- port, 2008.<br>[21] Matej Petkovic, Luke Lucas, Dragi Kocev, Saˇso Dˇzeroski, Redouane Boumghar, and Nikola Simidjievski. Quantifying the effects of gyro- less flying of the mars express spacecraft with machine learning. In 2019 IEEE International<br>[22] Janhavi H. Borse, Dipti D. Patil, Vinod Kumar, and Sudhir Kumar. Soft landing parameter measurements for candidate navigation trajec- tories using deep learning and ai-enabled plan- etary descent. Mathematical Problems in Engi- neering, 2022</code> | <code>What are some of the research topics and methods explored in the provided references?<br><br></code> |
* Loss: <code>losses.WeightedMultipleNegativesRankingLoss</code> with these parameters:
```json
{
"scale": 20,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 804 evaluation samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 351.15 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 19.36 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|
| <code>, the total number of test thermocouples has been rationalized taking into account redundancy needs, accommodation constraints and hardware passivation needs for flight. The test is subdivided into 19 phases (see Figure 12) with two phases before and after the test for the health check functional tests under room conditions. Functional tests demonstrate anomalies such as the PCDU Reset and operational malfunctions of the RAX instrument at its high temperatures. The PCDU Reset anomaly was solved during the test by a software patch and validated during the final hot and cold plateaus. To address the RAX anomaly at hot, various test configurations were simulated using the thermal numerical model during the test to actually perform RAX functional test at an intermediate plateau facilitating mission operational constraints for flight. Data collected from hot and cold thermal balance test phases, as well as the rover OFF transition from hot to cold, are the inputs for correlation activities conducted post-TV/TB test. The thermal numerical model updates mainly focus on conductive couplings</code> | <code>What was the solution to the PCDU Reset anomaly during the test?<br><br></code> |
| <code>, where +Z axis orients to the earth, and sun pointing attitude mode during day time<br>orienting -Z axis to the sun. Therefore, attitude control subsystem is required to maneuver the satellite attitude twice per revolution around its pitch axis. Figure 6 shows concept of the attitude maneuverer. Another attitude maneuverer is necessary to perform SAR observation and SAR data download to a to ground station, because X-band transmit antenna is oriented to +Z, so the satellite has to offset its attitude to orient the X-band transmit antenna toward the ground station.<br>3.4 High pointing accuracy<br>Disturbance torque and system momentum profiles during few revolutions were estimated as shown in Figure 7 and 8. Four micro reaction wheels, which can respond to these profiles were selected which enable attitude maneuvers within a short period of time. In order to perform a pitch attitude maneuver quickly, two wheels are located on pitch axis while one wheel was located on each of the remaining roll and yaw axes. Figure 9 shows the satellite attitudes during SAR observation. There are three kinds of attitude, strip map mode, sliding spot light mode, and spotlight mode. Large change of momentum is required for pitch axis when the satellite is in spotlight mode. However, two pitch reaction wheels do not generate enough momentum to execute spotlight mode. So, sliding spotlight mode was selected for high resolution SAR observation mode instead of spotlight mode, in order to relax the torque and momentum requirements to the pitch wheels. In addition, two pitch<br>Figure 7. Disturbance torque profile Figure 8. System momentum profile<br>reaction wheels are accelerated to plus direction or minus direction by using magnet torque before observation. In order to obtain a high resolution SAR data, high attitude control accuracy is required for spotlight mode observation. To achieve high pointing accuracy against a defined ground target point, the attitude control loop applied feed forward compensation with estimated attitude angle and rate. Figure 10 shows an example of dynamic error during a spotlight mode observation maneuver.[4]<br>Equipment for SAR mission consumes total large power more than 1300W, therefore PCDU has a risk of causing electrical and RF influence to the bus power and signal line. In order to research the system, electrical interface check was performed using bread board model of PCDU, battery</code> | <code>What is the reason for selecting sliding spotlight mode instead of spotlight mode for high resolution SAR observation?<br><br></code> |
| <code>, body shape and motion assumptions. Then, ORSAT uses DCA to determine the reentry risk posed to the Earth’s<br>population based on the year of reentry and orbit inclination. It also predicts impact kinetic energy (impact velocity and impact mass) of objects that survive reentry[18]. ORSAT has been in use for the last decade and currently in its 6.0 version. However, unlike DAS, OR-<br>SAT is not readily available. Only personnel at the Johnson Space Center, Orbital Debris Program Office run ORSAT. ORSAT is limited to ballistic reentry, only tumbling motions or<br>stable orientations of objects are allowed which produce no lift. Partial melting of objects is considered by a demise factor and almost all materials in the database are temperature de- pendent. Heating by oxidation is also considered [20]. Therefore, ORSAT determines when<br>and if a reentry object demises by using integrated trajectory, atmospheric, aerodynamic, aero-thermodynamic, and thermal models as outlined in section 3.1 [17, 18, 20].<br>Reentry demisability analysis using DAS requires the spacecraft to be defined to the level of each individual hardware part constituting the spacecraft. This step facilitates population<br>of the DAS Spacecraft Definition Module . Section 3.2.1 illustrates a generic spacecraft subdivision approach that can be followed to itemize the individual parts spacecraft parts.<br>Subsequently, non-demisable parts are identified before or by the actual reentry analysis as explained in section 3.2.2.<br>Itemization of the demisable spacecraft basic parts can be best approached by decompos- ing the spacecraft according to the Hierarchical System Terminology defined in the NASA Systems Engineering Handbook [14]. Tables 3.2, 3.3 and 3.4 illustrate a generic approach<br>to decompose a spacecraft into basic parts [29, 30, 9] excluding the payload. Description of the specific product for the basic part identified completes the process. Though slight vari-<br>ations are likely to occur in the decomposition of different missions, the Generic Spacecraft Subsystems Hierarchical Subdivision approach is robust, hence</code> | <code>What is the limitation of ORSAT in terms of object motion?<br><br></code> |
* Loss: <code>losses.WeightedMultipleNegativesRankingLoss</code> with these parameters:
```json
{
"scale": 20,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 3e-06
- `weight_decay`: 0.001
- `num_train_epochs`: 20
- `bf16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### 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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 3e-06
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 20
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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`: True
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | dim_256_cosine_map@10 | dim_512_cosine_map@10 | dim_768_cosine_map@10 |
|:------:|:----:|:-------------:|:------:|:---------------------:|:---------------------:|:---------------------:|
| 0.4425 | 100 | 0.5883 | - | - | - | - |
| 0.8850 | 200 | 0.2765 | - | - | - | - |
| 1.3274 | 300 | 0.2047 | - | - | - | - |
| 1.7699 | 400 | 0.1628 | - | - | - | - |
| 2.2124 | 500 | 0.1519 | 0.1204 | 0.7094 | 0.7271 | 0.7266 |
| 2.6549 | 600 | 0.1309 | - | - | - | - |
| 3.0973 | 700 | 0.1228 | - | - | - | - |
| 3.5398 | 800 | 0.1062 | - | - | - | - |
| 3.9823 | 900 | 0.097 | - | - | - | - |
| 4.4248 | 1000 | 0.0853 | 0.1026 | 0.7281 | 0.7409 | 0.7468 |
| 4.8673 | 1100 | 0.086 | - | - | - | - |
| 5.3097 | 1200 | 0.0723 | - | - | - | - |
| 5.7522 | 1300 | 0.0678 | - | - | - | - |
| 6.1947 | 1400 | 0.0655 | - | - | - | - |
| 6.6372 | 1500 | 0.0583 | 0.0970 | 0.7252 | 0.7479 | 0.7502 |
| 7.0796 | 1600 | 0.0586 | - | - | - | - |
| 7.5221 | 1700 | 0.0521 | - | - | - | - |
| 7.9646 | 1800 | 0.049 | - | - | - | - |
| 8.4071 | 1900 | 0.0437 | - | - | - | - |
| 8.8496 | 2000 | 0.0443 | 0.0974 | 0.7193 | 0.7427 | 0.7455 |
### Framework Versions
- Python: 3.12.0
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu118
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## 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",
}
```
#### WeightedMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## 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.*
--> |