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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7828
- loss:TripletLoss
base_model: answerdotai/ModernBERT-large
widget:
- source_sentence: Hypersensitivity to Chironomidae (non-biting midges) has been a
problem in Northern Sudan since about and is probably due to the working of dams
which have produced lake-like conditions on parts of the Blue and main Niles where
breeding has evidently increased. Studies were undertaken to determine whether
this hypersensitivity is mediated by IgE. Sixteen Sudanese, with bronchial asthma
associated with exposure to the chironomid, Cladotanytarsus lewisi ('green nimitti'),
were investigated. All patients gave a positive immediate-type skin reaction to
an extract of the midge and the majority had markedly elevated concentrations
of circulating IgE. Serum from all patients passively sensitized human lung fragments
in vitro for the release of histamine and slow-reacting substance of anaphylaxis
by the 'nimitti' antigen. This tissue-sensitizing activity could be removed by
immunoabsorption with an anti-IgE. These results indicate that this widespread
and important hypersensitivity in the Sudan is IgE-mediated and thus may potentially
be treated by desensitization.
sentences:
- 'Fifty-eight poultry farmers were questioned for possible respiratory reactions
suggesting a hypersensitivity reaction to chicken antigens. In addition, their
sera were tested for the presence of antibodies against chicken antigens by several
immunologic techniques. Of the poultry farmers examined, % had hemagglutinating
antibodies in titers over : against chicken antigens in their sera while none
of ten controls had antibodies at a level of : . Furthermore, % of the poultry
farmers had precipitins against chicken antigens in their sera, but no precipitating
antibodies could be demonstrated in the sera from ten controls. No poultry farmer,
however, had symptoms characteristic of a hypersensitivity pneumonitis following
contact with chickens.'
- 'Variations in the time course of flowering and in its intensity were recorded
for regions in Israel along with variable climatic conditions. Such variations
had an effect on pollen release into the air and on pollen counts. Two approaches
for forecasting allergenic pollen dispersion were compared: ( ) a survey of floral
development and flowering intensity and ( ) a survey of airborne pollen. There
was a positive correlation between the survey of flowering and airborne pollen
counts, although pollen was occasionally found after the flowering season. Further,
it predicted fairly accurately the beginning of the flowering time of each species
in each regio, the expected flowering duration, and intensity. Conducting a survey
of flowering is a simple, easy and inexpensive operation. It is highly recommended
for regions with variable climates.'
- Deficiency of surfactant in alveoli leads to increased resistance to breathing.
Histamine is a mediator in allergic respiratory diseases. Though the bronchoconstrictor
effect of histamine is well recognised, histamine may have additional actions
that contribute to pathogenesis in these diseases. The present study aimed to
observe the effect of histamine on lecithin, a major component of alveolar surfactant.
Lecithin content in broncho-alveolar lavage (BAL) fluid of healthy adult male
rats was estimated by enzymatic method using Boehringer-Mannheim kits. Lecithin
content in these control animals was compared with that in three groups of healthy
adult male rats following subcutaneous administration of mg of histamine diphosphate
at minutes, minutes and minutes intervals, respectively. A significant reduction
in lecithin levels in BAL fluid was observed up to one hour after administration
of histamine. The results indicate a possible additional action of histamine in
the pathogenesis of allergic respiratory diseases.
- source_sentence: Educational training of a select group of construction workers
on a nuclear power plant demonstrates the value of both training and experience.
Training classes were given to three groups of workers involved in the installation
of surfacemounted plates. These workers were then given an exam that covered the
topics presented in the training sessions. Each of the three groups differed in
some respect, notably the time delay between the training sessions and the examinations
and the amount of experience the workers had in installing the surfacemounted
plates. Scores are highest for workers who took the examination shortly after
the training session was given. Among the experienced workers, the workers with
more work experience have higher scores. It is noted that experienced workers
differed from the inexperienced workers in the answers given for different types
of questions. Furthermore, the experienced workers perform better on those exam
questions that related to issues encountered in the workplace on a regular basis.
Results also show that workers understand their work better when they are regularly
informed about the reasons for work items being rejected.
sentences:
- The Basal Triassic Sandstone is the basal member of the Lower Triassic Kockatea
Shale. It is widely distributed adjacent to, and on the Precambrian Greenough
Block in the northern Perth Basin, Western Australia. This member is lowermost
Lower Triassic in age in the subsurface of the Don-gara gas field. In outcrop
on the Greenough Block, the member is represented by a thin basal conglomerate,
conformably overlain by upper Lower Triassic Kockatea Shale. In this stratigraphic
study, detailed well-to-well correlations and lithologic studies were integrated
to reconstruct the depositional history of the interval encompassing the Basal
Triassic Sandstone. This sandstone is a composite of near-shore marine, and strand
line accumulations deposited around the flanks and on the Greenough Block during
a Lower Triassic marine transgression. The sandstone bodies were deposited on
a drowned, topography of low relief, on progressively truncated Permian formations
and Precambrian basement. The topography was formed following uplift and tilting
of the Greenough Block and the overlying Lower Permian formations during mild
Upper Permian tectonism.
- Few people would contest the statement that 'training is an investment'. It is
an investment in the future, one which it is essential for industry to undertake
in order to meet the continually growing demand for skilled personnel to handle
the ever increasing pace of change. Rightly, then, training as an investment is
emphasised on basic training officer courses, it is the subject of many a Government
exhortation and prominent businessmen go out of their way to emphasise their belief
in it. It is widely publicised by the Industrial Training Boards. The slogan 'train
and gain' was not coined by an Industrial Training Board simply to advertise the
fact that they had been throwing money around as though it were going out of fashion,
it was also intended to emphasise the long term gains to be made by investing
in training. Looking even further back to the discussions which preceded the passing
of the Industrial Training Act, one of the objectives much stressed when the levy
and grant system was established was to spread the costs of this investment evenly
across industry. The firms who undertook training were to be subsidised by those
who benefited from it but did not undertake it. Talking and listening to training
practitioners it seems axiomatic that training is an investment and equally axiomatic
that, due to the inexplicable failure of management to recognise this, it is one
which is in danger of being seriously neglected. To doubt what is so evidently
the case is something which, in the training field, amounts to nothing less than
heresy.
- Out-of-sequence (OOS) construction is regarded as one of the most significant
factors contributing to construction inefficiencies and loss of labor productivity.
Nevertheless, no prior research efforts have attempted to study the impacts of
OOS work on productivity, cost, or even schedule. The goal of this paper is to
identify and study the causes and early warning signs of OOS work. Using an expert-based
analytic approach, the authors formed an industry panel of construction professionals
to gather the basic information related to OOS work and consequently surveyed
other industry respondents. The panel as well as the survey enabled the authors
to ( ) recognize the extent of OOS work, ( ) identify causes of OOS work and quantify
their characteristics, ( ) identify early warning signs of OOS work and investigate
their relationship with the occurrence of OOS events, ( ) classify the causes
and early warning signs of OOS into different categories, and ( ) investigate
the impacts of OOS work on project performance in terms of productivity, schedule,
cost, quality, and safety. Among many important and interesting results, it was
clear that late design deliverables represented the most important factor causing
OOS work, and late start of precommissioning activities was the most highly ranked
early warning sign for OOS work. This research adds to the body of knowledge by
providing unprecedented knowledge on why OOS work occurs and by providing a suite
of practices that contribute most to OOS work in construction projects. This paper
fills a significant gap in the literature and contributes in assisting project
participants in better understanding the overall industry perception on the different
causes, early warning signs, and impacts of OOS work.
- source_sentence: 'Asian-Pacific Economic LiteratureVolume , Issue p. - Book Review
Mineral Economics and Policy , J E Tilton and J I Guzman RFF Press, New York City,
Pp. . ISBN: - - - - Marian Radetzki, Marian Radetzki Lulea University of TechnologySearch
for more papers by this author Marian Radetzki, Marian Radetzki Lulea University
of TechnologySearch for more papers by this author First published: November the
full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack
citation ShareShare Give accessShare full text accessShare full-text accessPlease
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with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat
No abstract is available for this article. Volume00, Issue0November 0000Pages
- RelatedInformation'
sentences:
- 'The urgent need for very large increases in investment in infrastructure in most
developing countries in Asia is very clear. This paper surveys the challenges
facing policymakers in the region. Nearly all of the main concerns for policymakers
in Asia in addressing the global infrastructure imbalance are on the supply side.
In particular, there are seven related supplyside issues that are of high priority
for policymakers: selection and preparation of appropriate projects, finance,
pricing, access, governance and management, policy and regulatory policies, and
climate change. Governments and utilities need to improve their policies and performance
to build confidence among stakeholders. Access to infrastructure services needs
to be improved so that consumers will support realistic pricing policies, and
investors will be encouraged to provide finance for infrastructure sectors.'
- 'ADVERTISEMENT RETURN TO ISSUEPREVArticleThe Electromotive Determination of Small
Amounts of Ferric IronJ. F. KingJ. F. KingMore by J. F. King and R. N. WashburneR.
N. WashburneMore by R. N. WashburneCite this: J. Phys. Chem. , , , 00000000Publication
Date (Print):December , 0000Publication History Published online0 May 0000Published
inissue December 0000https://doi.org/ /j000000a000Request reuse permissions Article
Views00Altmetric-Citations0LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant
sum of full text article downloads since November (both PDF and HTML) across all
institutions and individuals. These metrics are regularly updated to reflect usage
leading up to the last few days.Citations are the number of other articles citing
this article, calculated by Crossref and updated daily. Find more information
about Crossref citation counts.The Altmetric Attention Score is a quantitative
measure of the attention that a research article has received online. Clicking
on the donut icon will load a page at altmetric.com with additional details about
the score and the social media presence for the given article. Find more information
on the Altmetric Attention Score and how the score is calculated. Share Add toView
InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation
and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (
KB) Get e-Alerts'
- 'Research Article| June Malayan Iron Ore for Japan Virginia Thompson Virginia
Thompson Search for other works by this author on: This Site PubMed Google Scholar
Far Eastern Survey ( ) ( ): . Views Icon Views Article contents Figures & tables
Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn
Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Virginia
Thompson; Malayan Iron Ore for Japan. Far Eastern Survey June ; ( ): . doi: Download
citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers
EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search
input Search input auto suggest filter your search All ContentAsian Survey Search
This content is only available via PDF. Article PDF first page preview Close Modal
You do not currently have access to this content.'
- source_sentence: Elevated circulating endotoxin levels in the plasma of patients
with advanced hepatosplenic schistosomiasis caused by Schistosoma mansoni have
been reported, possibly caused by parasite egg-induced intestinal mucosal breaches
facilitating bacterial access to the bloodstream. Neither endotoxin levels in
people with S. mansoni but without hepatosplenic disease nor the impact of treatment
on endotoxin levels have been described. We used a methodically optimized Limulus
amebocyte lysate assay to measure plasma endotoxin in community-dwelling women
from an S. mansoni-endemic area without clinical hepatosplenic disease. We found
no difference in baseline mean plasma endotoxin levels between those with (n =
) and without (n = ) infection ( versus EU/mL, P = ). Endotoxin levels did not
change in schistosome-infected women after successful treatment ( versus EU/mL,
P = ) and were not correlated with circulating anodic antigen or stool egg burden.
Our findings do not support the hypothesis that translocating eggs in S. mansoni
infection introduce bacterial sources of endotoxin to the circulation.
sentences:
- In January , a survey of intestinal parasitic infections was performed in a primary
school, central Thailand. Of stool samples, Blastocystis was identified with a
prevalence of %. Genetic characterization of Blastocystis showed subtype ( %)
and subtype ( %). Study of the water supply in this school was performed to find
the possible sources of Blastocystis. Blastocystis from one water sample was identified
as subtype , which had a nucleotide sequence of small subunit (SSU) ribosomal
RNA (rRNA) gene that was % identical to that of Blastocystis infected in schoolchildren.
Our information supports the evidence of water-borne transmission in this population.
- 'Background: The acute phase proteins (APPs) are plasma proteins whose concentration
rise or reduce in reaction to infection, inflammation or trauma (Baumann and Gauldie,
). The circulating concentration of these proteins are related to the severity
of the underlying condition,thus quantification of their concentrations provide
a ready means of giving valuable clinical information and extent of the disease
processes (Thompson et. al., ). Materials and Methods: Serum levels of three acute
phase proteins (transferrin, a0-macroglobulin and haptoglobin) were determined
using single radial immuno-diffusion technique in one hundred and eight Nigerian
women aged between and years. Theywere made up of thirty pregnant women with urinary
schistosomiasis (P+USS), thirty-six pregnant women without USS (P-USS), eighteen
non-pregnant women with USS (NP+USS), and twenty-four healthy non-pregnant women
without USS (NP-USS) as controls.Results: The result shows that transferrin was
least in P-USS group and highest in NP+USS. The highest mean value of alpha- macroglobulin
was found in P+USS group and the least in NP+USS. Haptoglobin was significantly
reduced in P+USS compared with other groups.Conclusions: The finding of this study
suggests an independent effect of USS and pregnancy on serum levels of APPs, therefore
APPs could be used to distinguish P+USS from P-USS'
- Aging is associated with a loss of skeletal muscle mass and function that negatively
impacts the independence and quality of life of older individuals. Females demonstrate
a distinct pattern of muscle aging compared to males, potentially due to menopause,
when the production of endogenous sex hormones declines. This systematic review
aims to investigate the current knowledge about the role of estrogen in female
skeletal muscle aging. A systematic search of MEDLINE Complete, Global Health,
Embase, PubMed, SPORTDiscus, and CINHAL was conducted. Studies were considered
eligible if they compared a state of estrogen deficiency (e.g. postmenopausal
females) or supplementation (e.g. estrogen therapy) to normal estrogen conditions
(e.g. premenopausal females or no supplementation). Outcome variables of interest
included measures of skeletal muscle mass, function, damage/repair, and energy
metabolism. Quality assessment was completed with the relevant Johanna Briggs
critical appraisal tool, and data were synthesized in a narrative manner. Thirty-two
studies were included in the review. Compared to premenopausal women, postmenopausal
women had reduced muscle mass and strength, but the effect of menopause on markers
of muscle damage and expression of the genes involved in metabolic signaling pathways
remains unclear. Some studies suggest a beneficial effect of estrogen therapy
on muscle size and strength, but evidence is largely conflicting and inconclusive,
potentially due to large variations in the reporting and status of exposure and
outcomes. The findings from this review point toward a potential negative effect
of estrogen deficiency on aging skeletal muscle, but further mechanistic evidence
is needed to clarify its role.
- source_sentence: 'normal subjects, residents of the Ural region, were examined by
a dichromatic bone densitometer of "GE/Lunar" firm (USA). After that they were
divided according to their somatotype: normosthenics, hypersthenics and asthenics.
Age-related groups in girls were formed from the age of years, in youths from
years, up to years every other year, after years every years up to the age of
years. The somatotype has been revealed to influence the mineral density (MD)
of skeleton, the mass of muscular, connective and fatty tissues: MD in girls has
been formed at the age of years, in youths at that of years. In normosthenics
and asthenics MD at the same age was % and %, respectively. At the age of years
MD in women with hypersthenia was % less than peak bone mass, in those with normosthenia
it was % less and in women with asthenia % less. In men these measurements were
and %, respectively.'
sentences:
- The C control chart is mostly used to monitor the number of non-conformities per
inspection unit of constant size. It is known that the classic C-chart control
limits often experience a high false alarm rate and thus lead to the increase
of unnecessary costs of inspection. Among many works performed to improve C-charts,
this paper presents the optimised control limits approach to the C-chart. In addition,
different C-charts are evaluated through factors related to the design of the
control chart. Optimally selecting design parameters depends on several process
parameters from statistical and/or economic aspects in the literature. This study
presents multi-objective economic-statistical design of different C control charts
under single assignable cause. An algorithm using the data envelopment analysis
(DEA) is employed to solve the models. A numerical example is used to illustrate
the algorithm procedure and to evaluate the performances of different designs.
- Early adult changes in the facial profile were studied longitudinally from to
years of age in a Swedish Caucasian sample of female and male dental students.
Lateral cephalometric radiographs were analysed by the conventional point-based
method and by the structure-based method of superimposing serial films, adapted
for computerized numerical analysis. Skeletal and soft tissue changes were described
by linear and angular variables. The magnitude of linear dimensional changes was
similar in the two sexes. The largest changes were found in the vertical dimensions.
Total anterior facial height increased by about mm in the -year period, suggesting
that the major part of the increase in vertical facial dimensions during the third
decade of life takes place in the first half of this decade. Sagittal jaw relationship
increased by about in both sexes. Soft tissue changes reflected those of the vertical
skeletal dimensions.
- x-ray images of patients with posttraumatic defects of forearm bones have been
analyzed using DiaMorph computer-assisted complex. Mean optical density of regenerated
bone shadows has been evaluated for the purpose of studying the dynamics of osteogenesis
and mineralization of newly formed bone tissue during osteosynthesis. By planimetry
of distraction regenerated bones it was established that osteogenesis developed
by normoplastic type. Typical distraction regenerated bones were formed while
filling defect-diastases; the regenerated bones lost their zonal structure at
the end of fixation period. During formation of wedge-shaped regenerated bones
clear zonal structure of newly formed tissue was not traced, the area of interlayer
occupied significantly less part than it was in case of filling the defects of
forearm bones by fragment lengthening and formation of typical distraction regenerated
bone.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on answerdotai/ModernBERT-large
results:
- task:
type: triplet
name: Triplet
dataset:
name: modernBERT
type: modernBERT
metrics:
- type: cosine_accuracy
value: 0.6726342710997443
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: modernBERT disciplines
type: modernBERT_disciplines
metrics:
- type: cosine_accuracy
value: 0.6756066411238825
name: Cosine Accuracy
---
# SentenceTransformer based on answerdotai/ModernBERT-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision e829787a68677321312ff287fda2f8ef1a36e02a -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("m7n/discipline-bert-modern-large_01")
# Run inference
sentences = [
'normal subjects, residents of the Ural region, were examined by a dichromatic bone densitometer of "GE/Lunar" firm (USA). After that they were divided according to their somatotype: normosthenics, hypersthenics and asthenics. Age-related groups in girls were formed from the age of years, in youths from years, up to years every other year, after years every years up to the age of years. The somatotype has been revealed to influence the mineral density (MD) of skeleton, the mass of muscular, connective and fatty tissues: MD in girls has been formed at the age of years, in youths at that of years. In normosthenics and asthenics MD at the same age was % and %, respectively. At the age of years MD in women with hypersthenia was % less than peak bone mass, in those with normosthenia it was % less and in women with asthenia % less. In men these measurements were and %, respectively.',
'x-ray images of patients with posttraumatic defects of forearm bones have been analyzed using DiaMorph computer-assisted complex. Mean optical density of regenerated bone shadows has been evaluated for the purpose of studying the dynamics of osteogenesis and mineralization of newly formed bone tissue during osteosynthesis. By planimetry of distraction regenerated bones it was established that osteogenesis developed by normoplastic type. Typical distraction regenerated bones were formed while filling defect-diastases; the regenerated bones lost their zonal structure at the end of fixation period. During formation of wedge-shaped regenerated bones clear zonal structure of newly formed tissue was not traced, the area of interlayer occupied significantly less part than it was in case of filling the defects of forearm bones by fragment lengthening and formation of typical distraction regenerated bone.',
'Early adult changes in the facial profile were studied longitudinally from to years of age in a Swedish Caucasian sample of female and male dental students. Lateral cephalometric radiographs were analysed by the conventional point-based method and by the structure-based method of superimposing serial films, adapted for computerized numerical analysis. Skeletal and soft tissue changes were described by linear and angular variables. The magnitude of linear dimensional changes was similar in the two sexes. The largest changes were found in the vertical dimensions. Total anterior facial height increased by about mm in the -year period, suggesting that the major part of the increase in vertical facial dimensions during the third decade of life takes place in the first half of this decade. Sagittal jaw relationship increased by about in both sexes. Soft tissue changes reflected those of the vertical skeletal dimensions.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Datasets: `modernBERT` and `modernBERT_disciplines`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | modernBERT | modernBERT_disciplines |
|:--------------------|:-----------|:-----------------------|
| **cosine_accuracy** | **0.6726** | **0.6756** |
<!--
## 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: 7,828 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 82 tokens</li><li>mean: 236.35 tokens</li><li>max: 620 tokens</li></ul> | <ul><li>min: 82 tokens</li><li>mean: 237.05 tokens</li><li>max: 663 tokens</li></ul> | <ul><li>min: 82 tokens</li><li>mean: 247.65 tokens</li><li>max: 653 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Implementing management systems in organisations of all types and sizes often raises the following question: "What benefits will this bring?" Initial resistance and criticism are common as potential challenges are identified during the implementation process. To address this, it is essential to highlight the advantages of these systems and engage stakeholders in supporting management efforts. While the planning, implementation, use, maintenance, auditing, and improvement of management systems are generally voluntary, certification is frequently driven by external factors, particularly customer demands. Employees also stand to gain significantly, with knowledge and information serving as valuable resources, especially for leveraging artificial intelligence. This article explores the management's readiness to adopt and fully utilise two management systems based on international standards: the ISO Knowledge management system (KMS) and the ISO/IEC Artificial intelligence management system ...</code> | <code>Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk...</code> | <code>This study aims to obtain user satisfaction factors for a knowledge management system so that a questionnaire can be made for evaluation or measurement. The SECI method is used with the CISE sequence which consists of four knowledge creation steps, namely C-combination, I-nternalization, S-socialization, and ending E-externalization. The stage begins with literature studies and then modifications are made with the selection, addition, and incorporation of existing models. From understanding and analyzing several models, discussions or brainstorming with colleagues were then carried out so that a final model was obtained to compile a list of keywords and statements as a questionnaire based on indicators related to knowledge management and the satisfaction of knowledge management system users. The results obtained there are eight user satisfaction factors divided into technical aspects (knowledge quality, knowledge sharing, system quality, service quality) and social aspects (management ...</code> |
| <code>This study examines the effect of alloying elements of Ni and W on the repassivation properties of stainless steel (SS) as evaluated by a rapid scratching electrode technique and stress corrosion cracking (SCC) test. The SS specimens were grouped into two different grades according to Ni content (00Cr-0Ni duplex, Type 000LMN [UNS S00000] austenitic SS). Major considerations regarding alloy design were Ni content and the substitution of W for Mo. However, a similar pitting resistance equivalent number (PREN) of to was maintained for all specimens. The main factors for evaluation of repassivation properties are the peak current for the scratched surface and repassivation rate. In M magnesium chloride (MgCl0) and N sulfuric acid containing chloride ions (H0SO0 + % Cl) solution, repassivation test results showed that repassivation properties decreased as Ni content increased. However, W substitution was effective on the repassivation process and increased the resistance of SCC property for...</code> | <code>Abstract High-nitrogen (N) stainless steels (SS) are receiving increased attention because of their strength advantages over carbon (C)-alloyed materials, but they have been found susceptible to dichromium nitride (Cr0N) precipitation during thermal exposure between 000C and ,000C. Sensitization susceptibility of a high-N, low-C austenitic SS by Cr0N precipitation at 000C and 000C was determined using the single-loop electrochemical potentiokinetic reactivation (EPR) test. High-N SS was found susceptible to sensitization caused by grain boundary (GB) precipitation of Cr0N, with the degree of sensitization increasing systematically with aging time at 000C. Sensitization of high-N materials did not require the concomitant precipitation of chromium (Cr)-rich metal carbide (M00C0). Materials aged at 000C were not sensitized, although the rate of precipitation was greater than at 000C. This indicated the minimum Cr level in the Cr-depleted zone of the matrix associated with nitride precipit...</code> | <code>The anodic dissolution characteristics of nickel, molybdenum, and stainless steel have been examined in pure and eutectic melt. Molybdenum and nickel show Tafeltype dissolution kinetics in pure eutectic which permit estimates of longterm corrosion rates as a function of voltage. Nickel exhibits a sharp threshold potential for dissolution in melt, forming a nonpassivating layer. Comparative voltammetry and opencircuit potential measurements with iron in this melt suggest that care may be required in using nickel as an iron sulfide current collector. The anodic dissolution of stainless steel in melt appears to be rate limited by diffusion through a reaction layer, showing a dependence that may be applicable to longterm corrosion predictions. Dissolution is strongly inhibited by dissolved , apparently by formation of a protective anodic oxide layer. Molybdenum appears to owe its excellent anodic corrosion resistance in melt both to a chemically formed prepassive film and to a welldefined ...</code> |
| <code>FY-0E WindRAD (Fengyun-0E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The diversity of the radar view geometry varies across the swaths, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence, NOCant is applied for the analysis of wind retrieval from the Ku-band and C-band. An extra engineering correction of dB and dB is applied on Ku-band and C-band backscatter values...</code> | <code>Spaceborne synthetic aperture radar (SAR) represents a powerful source of data for enhancing maritime domain awareness (MDA). Wakes generated by traveling vessels hold a crucial role in MDA since they can be exploited both for ship route and velocity estimation and as a marker of ship presence. Even if deep learning (DL) has led to an impressive performance boost on a variety of computer vision tasks, its usage for automatic target recognition (ATR) in SAR images to support MDA is still limited to the detection of ships rather than ship wakes. A dataset is presented in this paper and several state-of-the-art object detectors based on convolutional neural networks (CNNs) are tested with different backbones. The dataset, including more than wake chips, is realized by visually inspecting Sentinel- images over highly trafficked maritime sites. Extensive experiments are shown to characterize CNNs for the wake detection task. For the first time, a deep-learning approach is implemented to spe...</code> | <code>With the publication of Part Wind Actions of the South African Loading Code SANS : , several issues concerning adjustments from the reference standard Eurocode EN - - : could not be resolved due to lack of sufficient updated background information on South African conditions. The need for updating the map for the free field wind speed is related also to the improved representation of the mixed and complex strong wind climate of the country. Furthermore, strong wind probability models are used for the reliability assessment and calibration of wind design procedures. Updating of the reliability provisions for the revised wind loading process was a further need identified at the time. This paper provides a review of the historical development of the representation of the free field wind, used as input to design wind loading procedures for South Africa. The review considers: (i) the historical representations of the geographic distribution of free field wind, (ii) the climatic influences c...</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.05
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 391 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 391 samples:
| | anchor | positive | negative |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 85 tokens</li><li>mean: 239.86 tokens</li><li>max: 592 tokens</li></ul> | <ul><li>min: 87 tokens</li><li>mean: 229.31 tokens</li><li>max: 542 tokens</li></ul> | <ul><li>min: 93 tokens</li><li>mean: 239.99 tokens</li><li>max: 592 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Industrial Relations: A Journal of Economy and SocietyVolume , Issue p. - Internet Resources Selected by the Institute for Research on Labor and Employment Library University of California, Berkeley TERENCE K. HUWE, TERENCE K. HUWE Director of Library & Information ResourcesSearch for more papers by this authorJANICE KIMBALL, JANICE KIMBALL Library AssistantSearch for more papers by this author TERENCE K. HUWE, TERENCE K. HUWE Director of Library & Information ResourcesSearch for more papers by this authorJANICE KIMBALL, JANICE KIMBALL Library AssistantSearch for more papers by this author First published: April the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a ...</code> | <code>Industrial Relations: A Journal of Economy and SocietyVolume , Issue p. - Recent Publications Selected by the Institute for Research on Labor and Employment Library University of California, Berkeley Terence K. Huwe, Terence K. Huwe Director of Library & Information ResourcesSearch for more papers by this authorJanice Kimball, Janice Kimball Library AssistantSearch for more papers by this author Terence K. Huwe, Terence K. Huwe Director of Library & Information ResourcesSearch for more papers by this authorJanice Kimball, Janice Kimball Library AssistantSearch for more papers by this author First published: September the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to sha...</code> | <code>This paper suggests that all the models of industrial relations, not just the more statist ones, have been characterized throughout their history by complex and sometimes troublesome relationships with the state. These models have always been conditioned, and in certain sense shaped, by the latter's more or less direct intervention at the moment of their formation and as they have expanded or declined. An intervention which is also influenced by the nature of economic problems that national political economies have to cope with. Such difficulties of relationship are to a large extent due to the fact that political regulation and regulation through industrial relations only partially overlap in their goals and contents. More frequently they compete with each other and have methods and logics that tend to diverge. Whereas decisions are taken by majority principle in the political sphere, in industrial relations they can only be taken unamimously - and especially so in collective bargaini...</code> |
| <code>Poor response rates to follow-up questionnaires can adversely affect the progress of a randomised controlled trial and the validity of its results. This embedded 'study within a trial' aimed to investigate the impact of including a pen with the postal -month questionnaire completed by the trial participants on the response rates to this questionnaire.This study was a two-armed randomised controlled trial nested in the Gentle Years Yoga (GYY) trial. Participants in the intervention group of the GYY trial were allocated : using simple randomisation to either receive a pen (intervention) or no pen with their -month questionnaire (control). The primary outcome was the proportion of participants sent a -month questionnaire who returned it. Secondary outcomes were time taken to return the questionnaire, proportion of participants sent a reminder to return the questionnaire, and completeness of the questionnaire. Binary outcomes were analysed using logistic regression, time to return by Cox P...</code> | <code>Background Poor response rates to follow-up questionnaires can adversely affect the progress of a randomised controlled trial and the validity of its results. This embedded 'study within a trial' aimed to investigate the impact of including a pen with the postal -month questionnaire completed by the trial participants on the response rates to this questionnaire. Methods This study was a two-armed randomised controlled trial nested in the Gentle Years Yoga (GYY) trial. Participants in the intervention group of the GYY trial were allocated : using simple randomisation to either receive a pen (intervention) or no pen with their -month questionnaire (control). The primary outcome was the proportion of participants sent a -month questionnaire who returned it. Secondary outcomes were time taken to return the questionnaire, proportion of participants sent a reminder to return the questionnaire, and completeness of the questionnaire. Binary outcomes were analysed using logistic regression, tim...</code> | <code>Patients' failure to adhere on tuberculosis (TB) treatment leads to drug resistance, relapse and death. Non-adherence to TB treatment is higher during continuation treatment phase. The study aimed to evaluate effectiveness of combined pill refilling and medication reminders on adherence to TB treatment.A two-arm randomised controlled trial on adult patients with TB was used during continuation treatment phase. In the first arm, in addition to usual care, participants will receive cellphone-based daily medication and weekly pill refilling reminders. In the control arm, participants will receive only usual care. The study will use a covariate adaptive randomisation technique to balance covariates during allocation. The primary outcome is patients' adherence to TB treatment and secondary outcomes are attendance to clinic and treatment outcomes. We apply intention to treat with generalised linear mixed model.Ethical approval was obtained from Institutional Review Board of University of Gon...</code> |
| <code>EthologyVolume , Issue p. i-i Front CoverFree Access A male Swainson's Spurfowl, Pternistis swainsonii, calling out a raucous 'krrrraaak-krrrraaak-krrrraaak' in the bushveld of Kruger National Park, South Africa. Photograph reproduced by permission of Emmanuel Do Linh San - First published: June ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume000, Issue0July 0000Pages i-i RelatedInformation</code> | <code>EthologyVolume , Issue p. i-i Front CoverFree Access Breeding male Southern Masked-Weaver, Ploceus velatus, building a nest in Addo Elephant National Park, South Africa. Photograph reproduced by permission of Emmanuel Do Linh San First published: July ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume000, Issue0August 0000Pages i-i RelatedInformation</code> | <code>IbisVolume , Issue p. - Do male Chaffinches Fringilla coelebs copy song sequencing and bout length from their tutors? Katharina Riebel, Corresponding Author Katharina Riebel School of Environmental and Evolutionary Biology, University of St Andrews, File KY00 0TS, UK*Behavioural Biology, Institute of Evolutionary and Ecology Sciences, PO Box , RA Leiden, The Nederlands. Email: for more papers by this authorPeter J. B. Slater, Peter J. B. Slater School of Environmental and Evolutionary Biology, University of St Andrews, File KY00 0TS, UKSearch for more papers by this author Katharina Riebel, Corresponding Author Katharina Riebel School of Environmental and Evolutionary Biology, University of St Andrews, File KY00 0TS, UK*Behavioural Biology, Institute of Evolutionary and Ecology Sciences, PO Box , RA Leiden, The Nederlands. Email: for more papers by this authorPeter J. B. Slater, Peter J. B. Slater School of Environmental and Evolutionary Biology, University of St Andrews, File KY00 0TS...</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `warmup_ratio`: 0.1
- `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`: 4
- `per_device_eval_batch_size`: 4
- `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`: 1e-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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | modernBERT_cosine_accuracy | modernBERT_disciplines_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:--------------------------:|:--------------------------------------:|
| 0 | 0 | - | - | 0.4783 | - |
| 0.0511 | 100 | 0.0534 | 0.0495 | 0.5090 | - |
| 0.1022 | 200 | 0.0502 | 0.0474 | 0.5243 | - |
| 0.1533 | 300 | 0.0486 | 0.0465 | 0.5499 | - |
| 0.2044 | 400 | 0.0465 | 0.0457 | 0.5831 | - |
| 0.2555 | 500 | 0.0468 | 0.0467 | 0.5754 | - |
| 0.3066 | 600 | 0.0465 | 0.0444 | 0.6113 | - |
| 0.3577 | 700 | 0.0426 | 0.0467 | 0.5831 | - |
| 0.4088 | 800 | 0.0445 | 0.0454 | 0.5857 | - |
| 0.4599 | 900 | 0.0441 | 0.0441 | 0.6215 | - |
| 0.5110 | 1000 | 0.0432 | 0.0423 | 0.6189 | - |
| 0.5621 | 1100 | 0.0433 | 0.0417 | 0.6189 | - |
| 0.6132 | 1200 | 0.0395 | 0.0416 | 0.6240 | - |
| 0.6643 | 1300 | 0.0408 | 0.0403 | 0.6419 | - |
| 0.7154 | 1400 | 0.0414 | 0.0414 | 0.6445 | - |
| 0.7665 | 1500 | 0.044 | 0.0423 | 0.6343 | - |
| 0.8176 | 1600 | 0.0436 | 0.0418 | 0.6292 | - |
| 0.8687 | 1700 | 0.0392 | 0.0402 | 0.6624 | - |
| 0.9198 | 1800 | 0.039 | 0.0434 | 0.6419 | - |
| 0.9709 | 1900 | 0.0413 | 0.0439 | 0.5959 | - |
| 1.0220 | 2000 | 0.0396 | 0.0437 | 0.6087 | - |
| 1.0731 | 2100 | 0.0402 | 0.0414 | 0.6266 | - |
| 1.1242 | 2200 | 0.0402 | 0.0411 | 0.6496 | - |
| 1.1753 | 2300 | 0.0362 | 0.0415 | 0.6419 | - |
| 1.2264 | 2400 | 0.0371 | 0.0393 | 0.6496 | - |
| 1.2775 | 2500 | 0.0353 | 0.0396 | 0.6445 | - |
| 1.3286 | 2600 | 0.0322 | 0.0418 | 0.6496 | - |
| 1.3797 | 2700 | 0.0329 | 0.0412 | 0.6394 | - |
| 1.4308 | 2800 | 0.0311 | 0.0400 | 0.6445 | - |
| 1.4819 | 2900 | 0.0318 | 0.0385 | 0.6573 | - |
| 1.5330 | 3000 | 0.0306 | 0.0387 | 0.6726 | - |
| 1.5841 | 3100 | 0.0273 | 0.0387 | 0.6803 | - |
| 1.6352 | 3200 | 0.0285 | 0.0384 | 0.6803 | - |
| 1.6863 | 3300 | 0.0299 | 0.0375 | 0.6675 | - |
| 1.7374 | 3400 | 0.0304 | 0.0378 | 0.6522 | - |
| 1.7885 | 3500 | 0.03 | 0.0388 | 0.6496 | - |
| 1.8396 | 3600 | 0.028 | 0.0383 | 0.6803 | - |
| 1.8906 | 3700 | 0.0264 | 0.0380 | 0.6957 | - |
| 1.9417 | 3800 | 0.0275 | 0.0388 | 0.6573 | - |
| 1.9928 | 3900 | 0.0314 | 0.0378 | 0.6803 | - |
| 2.0439 | 4000 | 0.03 | 0.0388 | 0.6777 | - |
| 2.0950 | 4100 | 0.0308 | 0.0380 | 0.6752 | - |
| 2.1461 | 4200 | 0.0263 | 0.0382 | 0.6598 | - |
| 2.1972 | 4300 | 0.0215 | 0.0391 | 0.6573 | - |
| 2.2483 | 4400 | 0.017 | 0.0413 | 0.6471 | - |
| 2.2994 | 4500 | 0.0173 | 0.0398 | 0.6726 | - |
| 2.3505 | 4600 | 0.0183 | 0.0393 | 0.6752 | - |
| 2.4016 | 4700 | 0.0189 | 0.0399 | 0.6957 | - |
| 2.4527 | 4800 | 0.0123 | 0.0407 | 0.6803 | - |
| 2.5038 | 4900 | 0.0155 | 0.0405 | 0.6803 | - |
| 2.5549 | 5000 | 0.0108 | 0.0413 | 0.6726 | - |
| 2.6060 | 5100 | 0.0112 | 0.0416 | 0.6650 | - |
| 2.6571 | 5200 | 0.0134 | 0.0414 | 0.6777 | - |
| 2.7082 | 5300 | 0.0133 | 0.0406 | 0.6624 | - |
| 2.7593 | 5400 | 0.0109 | 0.0408 | 0.6701 | - |
| 2.8104 | 5500 | 0.0121 | 0.0408 | 0.6726 | - |
| 2.8615 | 5600 | 0.0124 | 0.0408 | 0.6752 | - |
| 2.9126 | 5700 | 0.012 | 0.0407 | 0.6752 | - |
| 2.9637 | 5800 | 0.0127 | 0.0406 | 0.6726 | - |
| 3.0 | 5871 | - | - | - | 0.6756 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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