File size: 70,216 Bytes
1ca7522 |
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 |
---
base_model: pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:137221
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What are the underlying physical mechanisms that allow for the
detection of rotation velocity using orbital angular momentum light spots, even
when they are completely deviated from the rotation center?
sentences:
- 'Pneumothorax following ultrasound-guided jugular vein puncture for central venous
access in interventional radiology: 4 years of experience. PURPOSE: The purpose
of our study was to review the rate of pneumothorax following central venous access,
using real-time ultrasound guidance. MATERIALS AND METHODS: Data related to ultrasound-guided
venous puncture, for central venous access, performed between July 1, 2004 and
June 30, 2008 was retrospectively and prospectively collected. Access route, needle
gauge, catheter type, and diagnosis of pneumothorax on the intraprocedure spot
radiographs and or the postprocedure chest radiographs, were recorded. RESULTS:
A total of 1262 ultrasound-guided jugular venous puncture for central venous access
were performed on a total of 1066 patients between July 1, 2004 and June 30, 2008.
Access vessels included 983 right internal jugular veins, 275 left internal jugular
veins, and 4 right external jugular veins. No pneumothorax (0%) was identified.
CONCLUSION: Due to an extremely low rate of pneumothorax following ultrasound-guided
central venous access, 0% in our study and other published studies, we suggest
that routine postprocedure chest radiograph to exclude pneumothorax may be dispensed
unless it is suspected by the operator or if the patient becomes symptomatic.'
- 'Targeting CDK4/6 in patients with cancer. The cyclin D-cyclin dependent kinase
(CDK) 4/6-inhibitor of CDK4 (INK4)-retinoblastoma (Rb) pathway controls cell cycle
progression by regulating the G1-S checkpoint. Dysregulation of the cyclin D-CDK4/6-INK4-Rb
pathway results in increased proliferation, and is frequently observed in many
types of cancer. Pathway activation can occur through a variety of mechanisms,
including gene amplification or rearrangement, loss of negative regulators, epigenetic
alterations, and point mutations in key pathway components. Due to the importance
of CDK4/6 activity in cancer cells, CDK4/6 inhibitors have emerged as promising
candidates for cancer treatment. Moreover, combination of a CDK4/6 inhibitor with
other targeted therapies may help overcome acquired or de novo treatment resistance.
Ongoing studies include combinations of CDK4/6 inhibitors with endocrine therapy
and phosphatidylinositol 3-kinase (PI3K) pathway inhibitors for hormone receptor-positive
(HR+) breast cancers, and with selective RAF and MEK inhibitors for tumors with
alterations in the mitogen activated protein kinase (MAPK) pathway such as melanoma.
In particular, the combination of CDK4/6 inhibitors with endocrine therapy, such
as palbociclib''s recent first-line approval in combination with letrozole, is
expected to transform the treatment of HR+ breast cancer. Currently, three selective
CDK4/6 inhibitors have been approved or are in late-stage development: palbociclib
(PD-0332991), ribociclib (LEE011), and abemaciclib (LY2835219). Here we describe
the current preclinical and clinical data for these novel agents and discuss combination
strategies with other agents for the treatment of cancer.'
- Rotation velocity detection with orbital angular momentum light spot completely
deviated out of the rotation center. Based on the rotational Doppler effect, an
orbital angular momentum beam can measure the lateral rotation velocity of an
object, which has broad application prospects. However, all existing research
focus on the light spot center coinciding with the rotation center, or only with
small center offset. This is difficult to ensure in remote detection applications.
In this paper, the rotational Doppler frequency shifts under three cases, including
no center offset, small center offset and large center offset, are analyzed theoretically.
Through theoretical research results, a novel method of measuring rotation velocity
is proposed, with the light spot completely deviated out of the rotation center.
A laboratory verification experiment shows that this proposed method breaks the
limit of center offset of lateral rotation velocity measurement and is of great
significance to the remote detection of non-cooperative rotation object.
- source_sentence: What are the implications of hydrogen bonding patterns on the supramolecular
assembly of molecules, and how can these interactions be manipulated or controlled?
sentences:
- M.V. Volkenstein, evolutionary thinking and the structure of fitness landscapes.
High dimensional fitness landscapes are robustly dominated by saddle points, not
isolated peaks. We present an argument to this effect that is reminiscent of May's
complexity stability analysis and trace out the significance for the dynamics
of speciation, the connection between the neutral and punctuated aspects of evolution
and evolution on moving landscapes. The paper is written in honor of M.V. Volkenstein,
who devoted his last papers to uniting dynamics with evolutionary thinking.
- Differential impacts of smoke-free laws on indoor air quality. The authors assessed
the impacts of two different smoke-free laws on indoor air quality. They compared
the indoor air quality of 10 hospitality venues in Lexington and Louisville, Kentucky,
before and after the smoke-free laws went into effect. Real-time measurements
of particulate matter with aerodynamic diameter of 2.5 microm or smaller (PM2.5)
were made. One Lexington establishment was excluded from the analysis of results
because of apparent smoking violation after the law went into effect. The average
indoor PM2.5 concentrations in the nine Lexington venues decreased 91 percent,
from 199 to 18 microg/m3. The average indoor PM2.5 concentrations in the 10 Louisville
venues, however, increased slightly, from 304 to 338 microg/m3. PM2.5 levels in
the establishments decreased as numbers of burning cigarettes decreased. While
the Louisville partial smoke-free law with exemptions did not reduce indoor air
pollution in the selected venues, comprehensive and properly enforced smoke-free
laws can be an effective means of reducing indoor air pollution.
- 'Structures of three substituted arenesulfonamides from X-ray powder diffraction
data using the differential evolution technique. The structures of three substituted
arenesulfonamides have been solved from laboratory X-ray powder diffraction data,
using a new direct-space structure solution method based on a differential evolution
algorithm, and refined by the Rietveld method. In 2-toluenesulfonamide, C(7)H(9)NO(2)S
(I) (tetragonal I4(1)/a, Z = 16), the molecules are linked by N-H...O=S hydrogen
bonds into a three-dimensional framework. In 3-nitrobenzenesulfonamide, C(6)H(6)N(2)O(4)S
(II) (monoclinic P2(1), Z = 2), N-H...O=S hydrogen bonds produce molecular ladders,
which are linked into sheets by C-H...O=S hydrogen bonds: the nitro group does
not participate in the hydrogen bonding. Molecules of 4-nitrobenzenesulfonamide,
C(6)H(6)N(2)O(4)S (III) (monoclinic P2(1)/n, Z = 4), are linked into sheets by
four types of hydrogen bond, N-H...O=S, N-H...O(nitro), C-H...O=S and C-H...O(nitro),
and the sheets are weakly linked by aromatic pi...pi stacking interactions.'
- source_sentence: When ceritinib used instead of crizotinib?
sentences:
- 'Pemedolac: a novel and long-acting non-narcotic analgesic. Pemedolacindole-1-acetic
acid; AY-30,715] exhibited potent analgesic effects against chemically induced
pain in rats and mice and against inflammatory pain in rats. In each of the animal
models used the analgesic potency of pemedolac was defined by an ED50 of 2.0 mg/kg
p.o. or less. Significant analgesic activity was detected in rats at 16 hr after
administration of 1 mg/kg p.o. (paw pressure test) and at 10 hr after administration
of 10 mg/kg p.o. to mice (p-phenylbenzoquinone writhing). Inasmuch as pemedolac
was inactive in the hot plate and tail-flick tests; and its analgesic activity
was not antagonized by naloxone (1 mg/kg s.c.), and tolerance did not develop
upon multiple administration; this drug does not exert its analgesic effects through
an opiate mechanism. Pemedolac differed from standard nonsteroidal anti-inflammatory
drugs (NSAIDs) in that the doses which produced analgesia were much lower than
those required for either anti-inflammatory or gastric irritant effects. In acute
anti-inflammatory tests, pemedolac exhibited only weak activity as evidenced by
an ED50 approximately 100 mg/kg p.o. in the carrageenan paw edema procedure. This
demonstrates for pemedolac a separation of at least 50-fold between the acute
analgesic and anti-inflammatory activities, which was greater than that observed
with reference NSAIDs. The compound also had a low ulcerogenic liability with
an acute UD50 = 107 mg/kg p.o. and a subacute UD50 estimated to be 140 mg/kg/day
p.o. In contrast, the reference NSAIDS (piroxicam, indomethacin, naproxen and
ibuprofen) exhibited similar dose-response relationships for the analgesic, anti-inflammatory
and gastric irritant effects.(ABSTRACT TRUNCATED AT 250 WORDS).'
- Genome-wide detection of CNVs associated with beak deformity in chickens using
high-density 600K SNP arrays. Beak deformity (crossed beaks) is found in several
indigenous chicken breeds including Beijing-You studied here. Birds with deformed
beaks have reduced feed intake and poor production performance. Recently, copy
number variation (CNV) has been examined in many species and is recognized as
a source of genetic variation, especially for disease phenotypes. In this study,
to unravel the genetic mechanisms underlying beak deformity, we performed genome-wide
CNV detection using Affymetrix chicken high-density 600K data on 48 deformed-beak
and 48 normal birds using penncnv. As a result, two and eight CNV regions (CNVRs)
covering 0.32 and 2.45 Mb respectively on autosomes were identified in deformed-beak
and normal birds respectively. Further RT-qPCR studies validated nine of the 10
CNVRs. The ratios of six CNVRs were significantly different between deformed-beak
and normal birds (P < 0.01). Within these six regions, three and 21 known genes
were identified in deformed-beak and normal birds respectively. Bioinformatics
analysis showed that these genes were enriched in six GO terms and one KEGG pathway.
Five candidate genes in the CNVRs were further validated using RT-qPCR. The expression
of LRIG2 (leucine rich repeats and immunoglobulin like domains 2) was lower in
birds with deformed beaks (P < 0.01). Therefore, the LRIG2 gene could be considered
a key factor in view of its known functions and its potential roles in beak deformity.
Overall, our results will be helpful for future investigations of the genomic
structural variations underlying beak deformity in chickens.
- 'Ceritinib: a new tyrosine kinase inhibitor for non-small-cell lung cancer. OBJECTIVE:
To review ceritinib for the treatment of anaplastic lymphoma kinase (ALK)-positive
metastatic non-small-cell lung cancer (NSCLC). DATA SOURCES: Literature searches
were conducted in PubMed, EMBASE (1974 to July week 5, 2014), and Google Scholar
using the terms ceritinib, LDK378, and non-small-cell lung cancer. STUDY SELECTION
AND DATA EXTRACTION: One phase 1 trial and 2 abstracts were identified. DATA SYNTHESIS:
Ceritinib is approved for the treatment of ALK-positive metastatic NSCLC in patients
who are intolerant to or have progressed despite therapy with crizotinib. In the
phase 1 clinical trial, the maximum tolerated dose was determined to be 750 mg
once daily. The overall response rate (ORR) was 58% (95% CI = 48-67) in patients
who received ≥400 mg daily (n = 114). In this group, the ORR was 56% (95% CI =
41-67) and 62% (95% CI = 44-78) among crizotinib-exposed and -naïve patients,
respectively. The ORR was 59% (95% CI = 47-70) in patients who received 750 mg
daily (n = 78). The ORR was 56% (95% CI = 41-70) in crizotinib-treated patients
and 64% (95% CI = 44-81) in crizotinib-naïve patients, respectively, in this subset.
The median duration of response was 8.2 months. Median progression-free survival
was 7.0 months. The most common adverse reactions included diarrhea, nausea, vomiting,
abdominal pain, anorexia, constipation, fatigue, and elevated transaminases. CONCLUSIONS:
Ceritinib has activity in crizotinib-resistant and crizotinib-naïve patients and
appears to be a viable alternative for ALK-positive NSCLC. Long-term data are
needed to further define the role of ceritinib in the treatment of NSCLC.'
- source_sentence: How do underlying physiological mechanisms influence the relationship
between muscle tension and headache disorders?
sentences:
- 'The correlation of lncRNA SNHG16 with inflammatory cytokines, adhesion molecules,
disease severity, and prognosis in acute ischemic stroke patients. BACKGROUND:
Long non-coding RNA small nucleolar RNA host gene 16 (lncRNA SNHG16) is involved
in the pathogenesis of acute ischemic stroke (AIS) through the regulation of brain
endothelial cell viability, inflammation, atherosclerotic plaque formation, and
neural apoptosis. This study aimed to evaluate the prognostic value of lncRNA
SNHG16 in AIS patients. METHODS: Newly diagnosed AIS patients (N = 120) were serially
recruited. Their lncRNA SNHG16 expressions in peripheral blood mononuclear cells
(PBMCs) were detected by reverse transcription-quantitative polymerase chain reaction
(RT-qPCR); serum inflammatory cytokines and adhesion molecules were determined
using enzyme-linked immunosorbent assay (ELISA). The accumulating recurrence-free
survival (RFS) and overall survival (OS) were analyzed. Moreover, controls (N
= 60) were recruited and their lncRNA SNHG16 expressions in PBMCs were detected.
RESULTS: LncRNA SNHG16 was declined in AIS patients compared to controls (p <
0.001). Moreover, lncRNA SNHG16 was not related to any comorbidities in AIS patients
(all p > 0.05). Interestingly, lncRNA SNHG16 was negatively related to tumor necrosis
factor alpha (TNF-α) (p < 0.001), interleukin 6 (IL-6) (p = 0.013), and intracellular
cell adhesion molecule-1 (ICAM-1) (p = 0.024), while positively correlated with
interleukin 10 (IL-10) (p = 0.022) in AIS patients. Besides, lncRNA SNHG16 was
inversely associated with the National Institutes of Health Stroke Scale (NIHSS)
score in AIS patients (p = 0.003). During the follow-up period, in 14 (11.7%)
patients occurred recurrence and 5 (4.2%) patients died. Unexpectedly, lncRNA
SNHG16 was not associated with accumulating RFS (p = 0.103) or OS (p = 0.150)
in AIS patients. CONCLUSION: LncRNA SNHG16 relates to lower inflammatory cytokines,
adhesion molecules, and milder disease severity, but fails to predict prognosis
in AIS patients.'
- 'Burning mouth syndrome: a discussion of a complex pathology. Burning mouth syndrome
is a complex pathology for which there is very little information about the etiology
and pathogenesis. This lack of knowledge leaves patients with suboptimal treatments.
This article discusses the existing scientific evidence about this disease. Since
topical oral use of clonazepam have been shown to be effective and safe to treat
some patients suffering with burning mouth syndrome, formulations including clonazepam
are included with this article. Compounding topical preparations of clonazepam
offers opportunities for compounding pharmacists to be more involved in improving
the quality of life of burning mouth syndrome patients.'
- 'Tension headaches and muscle tension: is there a role for magnesium? Although
many theories and hypotheses have been offered for the etiology of tension-type
headache (TH), no one previous hypothesis seems to adequately explain TH. This
may, in large measure, account for why it is often difficult to effectively treat
TH. Herein, we review current and old hypotheses of TH and offer a new hypothesis
which is consistent with what is known about TH. We show that magnesium (Mg) metabolism
may be pivotal in both the etiology and treatment of TH. Measurement of serum
ionized Mg2+ (IMg2+) levels and brain intracellular free Mg2+ (i) appear to offer
excellent methods for establishing the validity of our hypothesis. Since approximately
70% of patients who have a TH exhibit muscular tightness and tenderness, it is
distinctly possible that problems in Mg metabolism and dietary intake are the
links to concomitant muscle tension and TH. The significance of release of pain
mediators, muscle cramps, muscle strains (and damage) and muscle tension to TH,
and its relationship to Mg metabolism, are reviewed. These are all associated
with a Mg-deficient state. It seems clear from the available data that TH''s are
more associated with muscle tension or scalp tension than any other headache type.
From the data available, Mg supplementation appears to be of great benefit in
many of these situations. We believe there is a great need for clinicians to examine
Mg2+ metabolism, bioavailable Mg2+ in muscle tissues and blood, and the effectiveness
of Mg salts (in a double-blinded, placebo-controlled manner) in subjects with
TH and muscle tension.'
- source_sentence: How do the structural properties of nanoporous materials influence
their efficiency in catalytic reactions?
sentences:
- 'Rationale and design of the Kanyini guidelines adherence with the polypill (Kanyini-GAP)
study: a randomised controlled trial of a polypill-based strategy amongst indigenous
and non indigenous people at high cardiovascular risk. BACKGROUND: The Kanyini
Guidelines Adherence with the Polypill (Kanyini-GAP) Study aims to examine whether
a polypill-based strategy (using a single capsule containing aspirin, a statin
and two blood pressure-lowering agents) amongst Indigenous and non-Indigenous
people at high risk of experiencing a cardiovascular event will improve adherence
to guideline-indicated therapies, and lower blood pressure and cholesterol levels.
METHODS/DESIGN: The study is an open, randomised, controlled, multi-centre trial
involving 1000 participants at high risk of cardiovascular events recruited from
mainstream general practices and Aboriginal Medical Services, followed for an
average of 18 months. The participants will be randomised to one of two versions
of the polypill, the version chosen by the treating health professional according
to clinical features of the patient, or to usual care. The primary study outcomes
will be changes, from baseline measures, in serum cholesterol and systolic blood
pressure and self-reported current use of aspirin, a statin and at least two blood
pressure lowering agents. Secondary study outcomes include cardiovascular events,
renal outcomes, self-reported barriers to indicated therapy, prescription of indicated
therapy, occurrence of serious adverse events and changes in quality-of-life.
The trial will be supplemented by formal economic and process evaluations. DISCUSSION:
The Kanyini-GAP trial will provide new evidence as to whether or not a polypill-based
strategy improves adherence to effective cardiovascular medications amongst individuals
in whom these treatments are indicated. TRIAL REGISTRATION: This trial is registered
with the Australian New Zealand Clinical Trial Registry ACTRN126080005833347.'
- A highly robust cluster-based indium(III)-organic framework with efficient catalytic
activity in cycloaddition of CO2 and Knoevenagel condensation. The efficient catalytic
performance displayed by MOFs is decided by an appropriate charge/radius ratio
of defect metal sites, large enough solvent-accessible channels and Lewis base
sites capable of polarizing substrate molecules. Herein, the solvothermal self-assembly
led to a highly robust nanochannel-based framework of {·2DMF·5H2O}n (NUC-66) with
a 56.8% void volume, which is a combination of a tetranuclear cluster (abbreviated
as {In4}) and a conjugated tetracyclic pentacarboxylic acid ligand of 4,4'-(4-(4-carboxyphenyl)pyridine-2,6-diyl)diisophthalic
acid (H5CPDD). To the best of our knowledge, NUC-66 is a rarely reported {In4}-based
3D framework with embedded hierarchical triangular-microporous (2.9 Å) and hexagonal-nanoporous
(12.0 Å) channels, which are shaped by six rows of {In4} clusters. After solvent
exchange and vacuum drying, the surface of nanochannels in desolvated NUC-66a
is modified by unsaturated In3+ ions, Npyridine atoms and μ3-OH groups, all of
which display polarization ability towards polar molecules due to their Lewis
acidity or basicity. The catalytic experiments performed showed that NUC-66a had
high catalytic activity in the cycloaddition reactions of epoxides with CO2 under
mild conditions, which should be ascribed to its structural advantages including
nanoscale channels, rich bifunctional active sites, large surface areas and chemical
stability. Moreover, NUC-66a, as a heterogeneous catalyst, could greatly accelerate
the Knoevenagel condensation reactions of aldehydes and malononitrile. Hence,
this work confirms that the development of rigid nanoporous cluster-based MOFs
built on metal ions with a high charge and large radius ratio will be more likely
to realize practical applications, such as catalysis, adsorption and separation
of gas, etc.
- Absolute quantification of dehydroacetic acid in processed foods using quantitative
1H NMR. An absolute quantification method for the determination of dehydroacetic
acid in processed foods using quantitative (1)H NMR was developed and validated.
The level of dehydroacetic acid was determined using the proton signals of dehydroacetic
acid referenced to 1,4-bis (trimethylsilyl) benzene-d4 after simple solvent extraction
from processed foods. All the recoveries from three processed foods spiked at
two different concentrations were larger than 85%. The proposed method also proved
to be precise, with inter-day precision and excellent linearity. The limit of
quantification was confirmed as 0.13g/kg in processed foods, which is sufficiently
low for the purposes of monitoring dehydroacetic acid. Furthermore, the method
is rapid and easy to apply, and provides International System of Units traceability
without the need for authentic analyte reference materials. Therefore, the proposed
method is a useful and practical tool for determining the level of dehydroacetic
acid in processed foods.
---
# SentenceTransformer based on pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1](https://huggingface.co/pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1). It maps sentences & paragraphs to a 384-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:** [pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1](https://huggingface.co/pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1) <!-- at revision 0c787217b5a51d93286052fb773dea46ff9b1e57 -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 384 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': 1024, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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})
)
```
## 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("pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1-QA_100K-BioASQ-Epoch_5")
# Run inference
sentences = [
'How do the structural properties of nanoporous materials influence their efficiency in catalytic reactions?',
"A highly robust cluster-based indium(III)-organic framework with efficient catalytic activity in cycloaddition of CO2 and Knoevenagel condensation. The efficient catalytic performance displayed by MOFs is decided by an appropriate charge/radius ratio of defect metal sites, large enough solvent-accessible channels and Lewis base sites capable of polarizing substrate molecules. Herein, the solvothermal self-assembly led to a highly robust nanochannel-based framework of {·2DMF·5H2O}n (NUC-66) with a 56.8% void volume, which is a combination of a tetranuclear cluster (abbreviated as {In4}) and a conjugated tetracyclic pentacarboxylic acid ligand of 4,4'-(4-(4-carboxyphenyl)pyridine-2,6-diyl)diisophthalic acid (H5CPDD). To the best of our knowledge, NUC-66 is a rarely reported {In4}-based 3D framework with embedded hierarchical triangular-microporous (2.9 Å) and hexagonal-nanoporous (12.0 Å) channels, which are shaped by six rows of {In4} clusters. After solvent exchange and vacuum drying, the surface of nanochannels in desolvated NUC-66a is modified by unsaturated In3+ ions, Npyridine atoms and μ3-OH groups, all of which display polarization ability towards polar molecules due to their Lewis acidity or basicity. The catalytic experiments performed showed that NUC-66a had high catalytic activity in the cycloaddition reactions of epoxides with CO2 under mild conditions, which should be ascribed to its structural advantages including nanoscale channels, rich bifunctional active sites, large surface areas and chemical stability. Moreover, NUC-66a, as a heterogeneous catalyst, could greatly accelerate the Knoevenagel condensation reactions of aldehydes and malononitrile. Hence, this work confirms that the development of rigid nanoporous cluster-based MOFs built on metal ions with a high charge and large radius ratio will be more likely to realize practical applications, such as catalysis, adsorption and separation of gas, etc.",
'Absolute quantification of dehydroacetic acid in processed foods using quantitative 1H NMR. An absolute quantification method for the determination of dehydroacetic acid in processed foods using quantitative (1)H NMR was developed and validated. The level of dehydroacetic acid was determined using the proton signals of dehydroacetic acid referenced to 1,4-bis (trimethylsilyl) benzene-d4 after simple solvent extraction from processed foods. All the recoveries from three processed foods spiked at two different concentrations were larger than 85%. The proposed method also proved to be precise, with inter-day precision and excellent linearity. The limit of quantification was confirmed as 0.13g/kg in processed foods, which is sufficiently low for the purposes of monitoring dehydroacetic acid. Furthermore, the method is rapid and easy to apply, and provides International System of Units traceability without the need for authentic analyte reference materials. Therefore, the proposed method is a useful and practical tool for determining the level of dehydroacetic acid in processed foods.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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
#### Unnamed Dataset
* Size: 137,221 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 23.82 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 281.43 tokens</li><li>max: 915 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How do menstrual-related factors, such as pain and cycle irregularity, impact the mental health and well-being of young women in educational settings?</code> | <code>Determinants of premenstrual dysphoric disorder and associated factors among regular undergraduate students at Hawassa University Southern, Ethiopia, 2023: institution-based cross-sectional study. BACKGROUND: Premenstrual dysphoric disorder (PMDD) is a condition causing severe emotional, physical, and behavioral symptoms before menstruation. It greatly hinders daily activities, affecting academic and interpersonal relationships. Attention is not given to premenstrual disorders among female students in higher education. As a result, students are susceptible to stress, and their academic success is influenced by various factors, including their menstrual cycle, and the long-term outcomes and consequences are poorly researched. Even though PMDD has a significant negative impact on student's academic achievement and success limited research has been conducted in low- and middle-income countries including Ethiopia, especially in the study setting. Therefore, a study is needed to assess premenstrual dysphoric disorder and associated factors among regular undergraduate students at Hawassa University. METHODS: An institutional-based cross-sectional study was conducted among 374 regular undergraduate female students at Hawassa University, College of Medicine and Health Sciences. A self-administered structured premenstrual symptoms screening tool for adolescents was used to assess premenstrual dysphoric disorder. The collected data were loaded into a statistical package for the social science version 25 and analyzed using it. Both bivariate and multivariate logistic regression were used to identify factors associated with premenstrual dysphoric disorder. Each independent variable was entered separately into bivariate analysis, and a variable with a p-value less than 0.25 were included in the multivariate analysis to adjust the possible confounders. Statistically significant was declared at a 95% confidence interval when variable with a p-value less than 0.05 in the multivariate analysis with premenstrual dysphoric disorder. RESULTS: The magnitude of premenstrual dysphoric disorder in this study was 62.6% (95% CI 57.4-67.5). Having severe premenstrual pain (AOR = 6.44;95%CI 1.02-40.73), having irregular menstrual cycle (AOR = 2.21; 95% CI 1.32-3.70), students who had poor social support (AOR = 5.10;95%CI, (2.76-12.92) and moderate social support (AOR = 4.93;95%CI (2.18-11.18), and students who used contraception (AOR = 3.76;95%CI, 2.21-6,40) were statistically significant factors with the outcome variable. CONCLUSION: The prevalence of premenstrual dysphoric disorder was high as compared to other studies. There was a strong link between irregular menstrual cycle, severe menstrual pain (severe dysmenorrhea), poor social support, and contraception use with premenstrual dysphoric disorder. This needs early screening and intervention to prevent the complications and worsening of the symptoms that affect students' academic performance by the institution.</code> |
| <code>How do sleep patterns influence cognitive function and learning in humans, and what are the broader implications for understanding neurological disorders?</code> | <code>Neurochemical mechanisms for memory processing during sleep: basic findings in humans and neuropsychiatric implications. Sleep is essential for memory formation. Active systems consolidation maintains that memory traces that are initially stored in a transient store such as the hippocampus are gradually redistributed towards more permanent storage sites such as the cortex during sleep replay. The complementary synaptic homeostasis theory posits that weak memory traces are erased during sleep through a competitive down-selection mechanism, ensuring the brain's capability to learn new information. We discuss evidence from neuropharmacological experiments in humans to show how major neurotransmitters and neuromodulators are implicated in these memory processes. As to the major excitatory neurotransmitter glutamate that plays a prominent role in inducing synaptic consolidation, we show that these processes, while strengthening cortical memory traces during sleep, are insufficient to explain the consolidation of hippocampus-dependent declarative memories. In the inhibitory GABAergic system, we will offer insights how drugs may alter the intricate interplay of sleep oscillations that have been identified to be crucial for strengthening memories during sleep. Regarding the dopaminergic reward system, we will show how it is engaged during sleep replay, but that dopaminergic neuromodulation likely plays a side role for enhancing relevant memories during sleep. Also, we briefly go into basic evidence on acetylcholine and cortisol whose low tone during slow wave sleep (SWS) is crucial in supporting hippocampal-to-neocortical memory transmission. Finally, we will outline how these insights can be used to improve treatment of neuropsychiatric disorders focusing mainly on anxiety disorders, depression, and addiction that are strongly related to memory processing.</code> |
| <code>What are the underlying physiological mechanisms by which elevated brain natriuretic peptide levels interact with heart rate variability to increase the likelihood of cardiovascular events?</code> | <code>The Combination of Non-dipper Heart Rate and High Brain Natriuretic Peptide Predicts Cardiovascular Events: The Japan Morning Surge-Home Blood Pressure (J-HOP) Study. BACKGROUND: We hypothesized that the association between the dipping heart rate (HR) pattern and cardiovascular (CV) events differs according to the brain natriuretic peptide (BNP) level. METHODS: We examined a subgroup of 1,369 patients from the Japan Morning Surge Home Blood Pressure study; these were patients who had CV risk factors and had undergone ambulatory blood pressure (BP) monitoring. HR non-dipping status was defined as (awake HR - sleep HR)/awake HR <0.1, and high BNP was defined as ≥35 pg/ml. We divided the patients into four groups according to their HR dipper status (dipping or non-dipping) and BNP level (normal or high). RESULTS: The mean follow-up period was 60 ± 30 months. The primary endpoints were fatal/nonfatal CV events (myocardial infarction, angina pectoris, stroke, hospitalization for heart failure, and aortic dissection). During the follow-up period, 23 patients (2.8%) in the dipper HR with normal BNP group, 8 patients (4.4%) in the non-dipper HR with normal BNP group, 24 patients (9.5%) in the dipper HR with high-BNP group, and 25 patients (21.0%) in the non-dipper HR with high-BNP group suffered primary endpoints (log rank 78.8, P < 0.001). Non-dipper HR was revealed as an independent predictor of CV events (hazard ratio, 2.13; 95% confidence interval, 1.35-3.36; P = 0.001) after adjusting for age, gender and smoking, dyslipidemia, diabetes mellitus, chronic kidney disease, BNP, non-dipper BP, 24-h HR, and 24-h systolic blood pressure. CONCLUSIONS: The combination of non-dipper HR and higher BNP was associated with a higher incidence of CV events.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 15,247 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 24.38 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 280.0 tokens</li><li>max: 866 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What are the underlying mechanisms by which electroporation enhances the immunogenicity of low-dose DNA vaccines, and what implications does this have for vaccine design and efficacy?</code> | <code>Immunotherapeutic Effects of Different Doses of Mycobacterium tuberculosis ag85a/b DNA Vaccine Delivered by Electroporation. Background: Tuberculosis (TB) is a major global public health problem. New treatment methods on TB are urgently demanded. Methods: Ninety-six female BALB/c mice were challenged with 2×104 colony-forming units (CFUs) of MTB H37Rv through tail vein injection, then was treated with 10μg, 50μg, 100μg, and 200μg of Mycobacterium tuberculosis (MTB) ag85a/b chimeric DNA vaccine delivered by intramuscular injection (IM) and electroporation (EP), respectively. The immunotherapeutic effects were evaluated immunologically, bacteriologically, and pathologically. Results: Compared with the phosphate-buffered saline (PBS) group, the CD4+IFN-γ+ T cells% in whole blood from 200 μg DNA IM group and four DNA EP groups increased significantly (P<0.05), CD8+IFN-γ+ T cells% (in 200 μg DNA EP group), CD4+IL-4+ T cells% (50 μg DNA IM group) and CD8+IL-4+ T cells% (50 μg and 100 μg DNA IM group, 100 μg and 200 μg DNA EP group) increased significantly only in a few DNA groups (P< 0.05). The CD4+CD25+ Treg cells% decreased significantly in all DNA vaccine groups (P<0.01). Except for the 10 μg DNA IM group, the lung and spleen colony-forming units (CFUs) of the other seven DNA immunization groups decreased significantly (P<0.001, P<0.01), especially the 100 μg DNA IM group and 50 μg DNA EP group significantly reduced the pulmonary bacterial loads and lung lesions than the other DNA groups. Conclusions: An MTB ag85a/b chimeric DNA vaccine could induce Th1-type cellular immune reactions. DNA immunization by EP could improve the immunogenicity of the low-dose DNA vaccine, reduce DNA dose, and produce good immunotherapeutic effects on the mouse TB model, to provide the basis for the future human clinical trial of MTB ag85a/b chimeric DNA vaccine.</code> |
| <code>What is known about prostate cancer screening in the UK</code> | <code>Supporting informed decision making online in 20 minutes: an observational web-log study of a PSA test decision aid. BACKGROUND: Web-based decision aids are known to have an effect on knowledge, attitude, and behavior; important components of informed decision making. We know what decision aids achieve in randomized controlled trials (RCTs), but we still know very little about how they are used and how this relates to the informed decision making outcome measures. OBJECTIVE: To examine men's use of an online decision aid for prostate cancer screening using website transaction log files (web-logs), and to examine associations between usage and components of informed decision making. METHODS: We conducted an observational web-log analysis of users of an online decision aid, Prosdex. Men between 50 and 75 years of age were recruited for an associated RCT from 26 general practices across South Wales, United Kingdom. Men allocated to one arm of the RCT were included in the current study. Time and usage data were derived from website log files. Components of informed decision making were measured by an online questionnaire. RESULTS: Available for analysis were 82 web-logs. Overall, there was large variation in the use of Prosdex. The mean total time spent on the site was 20 minutes. The mean number of pages accessed was 32 (SD 21) out of a possible 60 pages. Significant associations were found between increased usage and increased knowledge (Spearman rank correlation [rho] = 0.69, P < .01), between increased usage and less favorable attitude towards PSA testing (rho = -0.52, P < .01), and between increased usage and reduced intention to undergo PSA testing (rho = -0.44, P < .01). A bimodal distribution identified two types of user: low access and high access users. CONCLUSIONS: Increased usage of Prosdex leads to more informed decision making, the key aim of the UK Prostate Cancer Risk Management Programme. However, developers realistically have roughly 20 minutes to provide useful information that will support informed decision making when the patient uses a web-based interface. Future decision aids need to be developed with this limitation in mind. We recommend that web-log analysis should be an integral part of online decision aid development and analysis. TRIAL REGISTRATION: ISRCTN48473735; http://www.controlled-trials.com/ISRCTN48473735 (Archived by WebCite at http://www.webcitation.org/5pqeF89tS).</code> |
| <code>How does early life adiposity influence long-term cardiovascular health, and what are the implications for prevention and intervention strategies?</code> | <code>Adiposity is associated with endothelial activation in healthy 2-3 year-old children. Adiposity is associated with C-reactive protein level in healthy 2-3 year-old children and with other markers of endothelial activation in adults, but data are lacking in very young children. Data from 491 healthy Hispanic children were analyzed. Mean age was 2.7 years (SD 0.5, range 2-3 years); mean body mass index (BMI) was 17.2 kg/m2 (SD 1.9) among boys and 17.1 kg/m2 (SD 2.1) among girls. E-selectin level was associated with BMI (R = 0.11; p < 0.02), ponderal index (p < 0.02), waist circumference (p = 0.02), fasting insulin (p < 0.02), and insulin resistance (p < or = 0.05); these associations remained significant after adjustment for age, sex and fasting glucose. sVCAM was also associated with BMI (R = 0.12; p < 0.05). These observations indicate that adiposity is associated with inflammation and endothelial activation in very early childhood.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `resume_from_checkpoint`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `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`: True
- `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`: 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`: True
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0932 | 100 | 0.3536 | - |
| 0.1864 | 200 | 0.227 | - |
| 0.2796 | 300 | 0.1599 | - |
| 0.3728 | 400 | 0.1448 | - |
| 0.4660 | 500 | 0.1276 | - |
| 0.5592 | 600 | 0.1187 | - |
| 0.6524 | 700 | 0.1191 | - |
| 0.7456 | 800 | 0.1082 | - |
| 0.8388 | 900 | 0.1026 | - |
| 0.9320 | 1000 | 0.0991 | - |
| 1.0 | 1073 | - | 0.0138 |
| 1.0252 | 1100 | 0.089 | - |
| 1.1184 | 1200 | 0.0759 | - |
| 1.2116 | 1300 | 0.0726 | - |
| 1.3048 | 1400 | 0.075 | - |
| 1.3979 | 1500 | 0.0732 | - |
| 1.4911 | 1600 | 0.07 | - |
| 1.5843 | 1700 | 0.0706 | - |
| 1.6775 | 1800 | 0.0708 | - |
| 1.7707 | 1900 | 0.0691 | - |
| 1.8639 | 2000 | 0.0713 | - |
| 1.9571 | 2100 | 0.0626 | - |
| 2.0 | 2146 | - | 0.0115 |
| 2.0503 | 2200 | 0.0564 | - |
| 2.1435 | 2300 | 0.0547 | - |
| 2.2367 | 2400 | 0.052 | - |
| 2.3299 | 2500 | 0.0491 | - |
| 2.4231 | 2600 | 0.0542 | - |
| 2.5163 | 2700 | 0.0506 | - |
| 2.6095 | 2800 | 0.0508 | - |
| 2.7027 | 2900 | 0.0493 | - |
| 2.7959 | 3000 | 0.0537 | - |
| 2.8891 | 3100 | 0.0499 | - |
| 2.9823 | 3200 | 0.0488 | - |
| 3.0 | 3219 | - | 0.0101 |
| 3.0755 | 3300 | 0.0444 | - |
| 3.1687 | 3400 | 0.0433 | - |
| 3.2619 | 3500 | 0.0425 | - |
| 3.3551 | 3600 | 0.0412 | - |
| 3.4483 | 3700 | 0.0451 | - |
| 3.5415 | 3800 | 0.0433 | - |
| 3.6347 | 3900 | 0.0429 | - |
| 3.7279 | 4000 | 0.0423 | - |
| 3.8211 | 4100 | 0.0445 | - |
| 3.9143 | 4200 | 0.0407 | - |
| 4.0 | 4292 | - | 0.0099 |
| 4.0075 | 4300 | 0.0415 | - |
| 4.1007 | 4400 | 0.0371 | - |
| 4.1938 | 4500 | 0.0376 | - |
| 4.2870 | 4600 | 0.037 | - |
| 4.3802 | 4700 | 0.0388 | - |
| 4.4734 | 4800 | 0.0352 | - |
| 4.5666 | 4900 | 0.0367 | - |
| 4.6598 | 5000 | 0.0377 | - |
| 4.7530 | 5100 | 0.0384 | - |
| 4.8462 | 5200 | 0.0355 | - |
| 4.9394 | 5300 | 0.0415 | - |
| 5.0 | 5365 | - | 0.0098 |
### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0
- Accelerate: 1.0.1
- Datasets: 3.0.2
- 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",
}
```
#### MultipleNegativesRankingLoss
```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.*
--> |