sentence-transformers safetensors modernbert sentence-similarity feature-extraction generated_from_trainer dataset_size:7828 loss:TripletLoss arxiv:1908.10084 arxiv:1703.07737 base_model:answerdotai/ModernBERT-large base_model:finetune:answerdotai/ModernBERT-large model-index autotrain_compatible endpoints_compatible region:us
---
base_model: answerdotai/ModernBERT-large
library_name: sentence-transformers
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7828
- loss:TripletLoss
widget:
- source_sentence: Pleural effusion is a frequently observed lesion in the course
    of respiratory diseases such as inflammatory process and cancer metastasis. Its
    cause may be either tuberculosis (the most common extrapulmonary location is the
    pleura) and malignant disease of the pleura. Confirmation of tuberculosis is often
    troublesome. The primary site of cancer may be als difficult to find despite the
    application of difficult diagnostic methods. Below we present history of -year
    old female in whom carcinomatous cells and positive result of PCR for Mycobacterium
    tuberculosis in pleural fluid were discovered simultaneously suggesting the tuberculosis
    and cancer of unknown primary origin.
  sentences:
  - Coronaviruses are a large family of viruses that cause illness ranging from mild
    to severe symptoms. Coronaviruses are known to cause diseases that cause severe
    symptoms such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory
    Syndrome (SARS). This study aims to determine the factors related to compliance
    with the use of personal protective equipment by health workers during the COVID-
    pandemic at Bahteramas Hospital, Southeast Sulawesi province in . This study used
    a case control design. The population in this study were health workers at Bahtermas
    Hospital totaling health workers. The sample in this study amounted to respondents
    consisting of groups of health workers. Sampling using the Lemeshow formula. The
    results showed that based on the results of the chis square test, the P-value
    of the knowledge variable was , the Attitude variable, a P-Value of was obtained
    and the PPE availability variable was a P-Value of . From the research samples
    used, it can be concluded that the Knowledge, Attitude and availability of PPE
    are related to compliance with the use of PPE by health workes during the COVID-
    pandemic at Bahteramas Hospital, Southeast Sulawesi Province.
  - Recent developments in treatment have steadily raised the median predicted age
    of survival for people with Cystic Fibrosis (CF). We report the health-related
    quality of life (HRQoL) in CF adult patients and correlate our findings with the
    patients' demographic characteristics.The Cystic Fibrosis Quality of Life (CFQoL)
    questionnaire was answered by CF adult patients. The questionnaire included questions
    pertaining to age, sex and level of education and covered eight sections of functioning.The
    highest score was reported in the "Social Functioning" section, while the lowest
    in the "Concerns for the Future" section. When different age groups were compared,
    statistical significances were reported in "Physical Functioning", "Interpersonal
    Relationships", and the "Career Concerns" section, with older patients reporting
    statistically higher HRQoL scores than younger ones (p < ). No statistically significant
    difference was reported amongst the scoring between male and female CF patients.
    When different educational levels were compared, patients that had received a
    higher educational training scored statistically higher in all but one sections
    of the questionnaire when compared with patients of a lower educational level
    (p < ).More than half Greek adult CF patients report that they are capable to
    participate in social activities but most of them are worried about the outcome
    of their disease and its effect on their lives.
  - 'BACKGROUND: The global amount of investment in companies developing artificial
    intelligence (AI)-based software technologies for medical diagnostics reached
    million in , rose to million in , and is expected to continue growing. While software
    manufacturing companies should comply with existing clinical, bioethical, legal,
    and methodological frameworks and standards, there is a lack of uniform national
    and international standards and protocols for testing and monitoring AI-based
    software. AIM: This objective of this study is to develop a universal methodology
    for testing and monitoring AI-based software for medical diagnostics, with the
    aim of improving its quality and implementing its integration into practical healthcare.
    MATERIALS AND METHODS: The research process involved an analytical phase in which
    a literature review was conducted on the PubMed and eLibrary databases. The practical
    stage included the approbation of the developed methodology within the framework
    of an experiment focused on the use of innovative technologies in the field of
    computer vision to analyze medical images and further application in the health
    care system of the city of Moscow. RESULTS: A methodology for testing and monitoring
    AI-based software for medical diagnostics has been developed, aimed at improving
    its quality and introducing it into practical healthcare. The methodology consists
    of seven stages: self-testing, functional testing, calibration testing, technological
    monitoring, clinical monitoring, feedback, and refinement. CONCLUSION: Distinctive
    features of the methodology include its cyclical stages of monitoring and software
    development, leading to continuous improvement of its quality, the presence of
    detailed requirements for the results of the software work, and the participation
    of doctors in software evaluation. The methodology will allow software developers
    to achieve significant outcomes and demonstrate achievements across various areas.
    It also empowers users to make informed and confident choices among software options
    that have passed an independent and comprehensive quality check.'
- source_sentence: Abstract The molecule based bilayer system composed of hard Ni
    [Fe(CN) ] nH O and soft Ni [Cr(CN) ] nH O ferromagnetic Prussian blue analogues
    has been fabricated on a solid substrate by "layer by layer" deposition. The structure
    and morphology characterization as well as results of magnetic measurements are
    described. The thickness of the bilayer is ca. nm including a nm interface. This
    bilayer system shows anisotropic magnetic properties reflected in the shape of
    magnetic hysteresis measured in various film orientation with respect to the direction
    of external magnetic field. There is no exchange interaction between hard and
    soft magnetic layer and irrespective of bilayer orientation, the magnetization
    and demagnetization process of both Ni [Fe(CN) ] nH O and Ni [Cr(CN) ] nH O layers
    occurs independently.
  sentences:
  - 'Previous articleNext article No AccessBook ReviewsThe Gospel According to Renan:
    Reading, Writing, and Religion in Nineteenth-Century France. By Robert D. Priest.
    Oxford Historical Monographs. Edited by P. Clavin et al.Oxford: Oxford University
    Press, . Pp. xii+ . . La vie de Jesus de Renan: La fabrique d''un best-seller.
    By Nathalie Richard.Rennes: Presses Universitaires de Rennes, . Pp. . .Stephane
    GersonStephane GersonNew York University Search for more articles by this author
    PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints
    Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited
    by The Journal of Modern History Volume , Number 0September Article DOIhttps://doi.org/
    . Views: 00Total views on this site For permission to reuse, please contact [email
    protected]PDF download Crossref reports no articles citing this article.'
  - Purpose The purpose of this paper is to present a case study describing a collaboration
    with Last Mile Health, a non-governmental organization, to develop a framework
    to inform its community healthcare networks in remote Liberia. Design/methodology/approach
    The authors detail the process of using the unique problem setting and available
    data to inform modeling and solution approaches. Findings The authors show how
    the characteristics of the Liberian setting can be used to develop a two-tier
    modeling framework. Given the operating constraints and remote setting the authors
    are able to model the problem as a special case of the location-routing problem
    that is computationally simple to solve. The results of the models applied to
    three districts of Liberia are discussed, as well as the collaborative process
    of the multidisciplinary team. Originality/value Importantly, the authors describe
    how the problem setting can enable the development of a properly scoped model
    that is implementable in practice. Thus the authors provide a case study that
    bridges the gap between theory and practice.
  - Abstract Poor electrical conductivities, structural instabilities and long synthesis
    procedures, limit the application of metal organic frameworks (MOFs) in energy
    storage systems. In the present work, we synthesize a cobaltbenzene tricarboxylic
    acid based MOF (CoBTC MOF) via two different approaches i. e. solvothermal route
    and mechanochemical grinding for its utility in energy storage. When characterized
    structurally and electrochemically, the CoBTC MOF synthesized by mechanochemical
    method is found to be superior because of large surface area, enhanced porosity/diffusion
    process through MOF and structural robustness along with less time requirement.
    Further, its hybrid composite with graphene nanosheets (CoBTC MOF/GNS) was prepared
    for its performance as a supercapacitor material. The characterization reveals
    the formation of sandwich structure where CoBTC MOF rods (thickness ranging from
    to m) are placed in between GNS. This arrangement has resulted into high specific
    capacitance of F.g at current density of A.g in M KOH electrolyte along with excellent
    capacitance retention up to % after charge/discharge cycles. Also, a symmetric
    supercapacitor has been assembled for practical application of CoBTC MOF/GNS which
    demonstrates specific capacitance of F.g with high energy density and power density
    of Wh.kg and W.kg respectively, along with % retention of initial capacitance
    after chargedischarge cycles.
- source_sentence: Patients with cancer are at increased risk of venous thromboembolism
    (VTE). Risk assessment models can help identifying high-risk populations that
    might benefit from primary thromboprophylaxis. Currently, the Khorana score is
    suggested to select patients for primary thromboprophylaxis. However, risk stratification
    with the Khorana-score remains imperfect, which led to the development of subsequent
    clinical risk assessment models (PROTECHT-, CONKO-, ONKOTEV-, TiCat-, COMPASS-CAT-score).
    Further, recently, a simplified, personalized risk prediction tool for cancer-associated
    VTE, incorporating cancer type and D-Dimer levels has been proposed by Pabinger
    et al. (CATSCORE). Also, novel models have been designed specifically for specific
    tumour types, such as lung cancer (ROADMAP-CAT), gynaecological cancer (THROMBOGYN),
    lymphoma (THROLY), or multiple myeloma (SAVED-; IMPEDE VTE-score). In the present
    narrative review, we comprehensively summarize available data on currently available
    risk assessment models for VTE in patients with cancer, provide a critical discussion
    on their clinical utility, and give an outlook towards future developments.
  sentences:
  - Besides the cancer itself, venous thromboembolism (VTE) is the leading cause of
    death in cancer patients receiving outpatient chemotherapy (CT). Data on VTE development
    and impact on treatment course and outcome in real-life NSCLC patients receiving
    immune check-point inhibitors (ICI) is currently sparse. More knowledge within
    this area is warranted due to the emerging use of ICI in clinical practice. To
    quantify risk of VTE and recurrent VTE in NSCLC patients receiving ICI. Explore
    the clinical impact of VTE on ICI course and survival and explore potential risk
    factors for VTE. Patients with advanced/metastatic NSCLC treated with an immune
    checkpoint inhibitor (ICI) at the University Hospital of Odense, Denmark during
    were identified and data gathered retrospectively from electronic medical records
    (n = ). All patients had finished ICI at the time of data-cut off. Baseline Khorana
    Score (KRS) was calculated within one week prior to ICI initiation. Based on follow-up
    data cumulative incidence of VTE and its impact on outcome and survival was performed
    using Kaplan Meier and cox-regression hazard estimation. Risk of VTE was % during
    ICI and % at any time point after ICI initiation. Cumulative incidence rates of
    VTE at , , and months after first ICI was %, %, % and % respectively. Median time
    to VTE during ICI was months [IQR .0]. Having VTE during ICI lead to discontinuation
    of ICI in % of cases, most due to fatal PE. History of VTE before onset of ICI
    was a significant risk factor for recurrent VTE during ICI ( % within this subgroup)
    despite use of anticoagulant therapy. The incidence and impact of VTE during ICI
    for real-life NSCLC patients is not negligible with almost % developing VTE leading
    to termination of further ICI in the majority of cases - many due to fatal PE.
    The risk of recurrent anticoagulant resistant VTE in patients with known VTE during
    ICI is also considerable, which calls for better management and prevention of
    VTE including development of treatment specific VTE risk assessment models.
  - In September , the New York Supreme Court, Second District, reversed a decision
    made by the Division of Human Rights for a dentist to pay a patient in compensatory
    damages. The agency ruled that disability-based discrimination is prohibited in
    places of public accommodation. The state Supreme Court, however, found that dental
    offices are not places of public accommodation as defined by the state human rights
    law. The Division of Human Rights plans to appeal the ruling to the New York Court
    of Appeals, citing case law which supports the proposition that private medical
    offices are places of public accommodation.
  - Hemoglobin concentrations in endometriotic cyst fluids have been found to be associated
    with distinct clinical manifestations, such as pelvic pain and infertility, as
    well as with malignant transformation. However, the measurement of the hemoglobin
    concentration in cyst fluid is an invasive procedure. The present study aimed
    to evaluate the usefulness of visible and nearinfrared interactance spectroscopy
    as a noninvasive technique for estimating the hemoglobin concentration in endometriotic
    cystic fluid. Optical fibers were directly placed onto sliced raw pork (up to
    00mmthick as an anatomical barrier on the cyst's surface) that covers a cuvette
    containing hemoglobin solution or endometriotic cyst fluid. Partial least square
    regression based on the second derivative using visible and nearinfrared interactance
    spectroscopy (wavelength region, nm) was used to estimate the hemoglobin concentration.
    The samples were categorized into the evaluation sets (i.e., calibration set)
    to create calibration curves and test sets (i.e., validation set) to validate
    equations. The cyst fluid at mm of pork thickness achieved a high correlation
    between actual and predicted hemoglobin concentrations (calibration (R0= ) and
    validation (R0= ) data). However, the correlation slightly decreased at 00mm pork
    thickness (i.e., calibration (R0= ) and validation (R0= ) data). Interactance
    spectroscopy may thus be a noninvasive tool which can be used to estimate the
    hemoglobin concentration in endometriotic cyst fluid when the anatomical barrier
    is mm. This technology is a reliable modality for predicting the severity of dysmenorrhea
    and infertility, as well as malignant transformation, in a number of patients
    with endometriotic cysts. Such quantitative optical spectroscopic imaging technologies
    may enable the accurate diagnosis of the pathological processes in endometriotic
    cysts in clinical practice.
- source_sentence: Numerous industries provide investors with various funding options
    in today's rapidly evolving business and technology landscape. One particularly
    intriguing area in this regard is investment. Investment refers to allocating
    cash into various assets for a specific duration to generate profits, such as
    income or capital appreciation. Infrastructure development has led to the management
    of several industries, including property and real estate. Property can stimulate
    other economic sectors by providing employment opportunities and enhancing overall
    societal well-being. This is further bolstered by the rapid growth of the property
    sector, driven by the consistent availability of land and the rising public demand
    for housing and office spaces. Based on the data results, it is evident that there
    was an upsurge in demand for property and real estate in . In contrast, production
    was sluggish expansion across all industries during the Covid- pandemic. Share
    prices will rise with increased demand and fall with less demand. This is evident
    in the company's effective management of shareholders. Financial reports are crucial
    for the company's future. Financial report data can be utilized as a decisive
    factor in decision-making. By assessing the financial performance of PT. Alam
    Sutera Reality Tbk, PT. Bumi Serpong Tbk, and PT. Bekasi Fajar Industri Estate
    Tbk, investors can make well-informed investment decisions. The liquid or illiquid
    ratio, which is based on the company's debt-to-equity ratio, current ratio, net
    profit margin, and total asset turnover, can be calculated to complete this assessment.
  sentences:
  - Fraud in accounting reporting is one of the factors that need to consider in presenting
    quality financial reports. Based on the existing phenomena, this study investigates
    accounting fraud that is suspected to be influenced by Good Corporate Governance
    (GCG), compliance with accounting rules to present financial reports and information
    asymmetry, and internal control. Testing the hypotheses secondary data from BUMN
    listed on the Jakarta Stock Exchange is used to test the allegations. Testing
    the hypothesis proposed using a quantitative approach with a sample of BUMNs listed
    on the Jakarta Stock Exchange. The calculation results show that all the proposed
    hypotheses are empirically proven. This condition indicates that accounting fraud
    to be influenced by Good Corporate Governance (GCG), adherence to accounting rules
    for the presentation of financial statements and information asymmetry, and internal
    control.
  - The number of multilingual signs in Japan was increasing rapidly; however, there
    were still disputes over the information of signs, such as low recognition of
    information and language selection, etc. In this case, this study was carried
    out.BR The purpose of the study was to define benchmarks for foreigner-friendly
    multilingual signs. Moreover, the possibility of how Chinese information was marked
    in the multilingual signs of Japanese Tourist Attractions was explored.BR The
    research contents and results were as follows. Firstly, the representative tourist
    attractions in Tokyo were surveyed on the spot and photographed for record. Secondly,
    the data from the fieldwork were organized into charts and graphs and analyzed
    for multilingual markers. Thirdly, through interviews with H Tourism Association
    in Tokyo, some issues with the signs of the current situation of scenic spots
    were revealed. Fourthly, from the perspective of the characteristics of Chinese
    language and the thinking method about Chinese characters, the field surveys and
    interviews about the need for a large area of multilingual information marking
    in signs were analyzed. The possibility of marking Chinese messages in signs of
    Tourist Attractions in Japan was discussed.BR Guidance signs and induction signs
    were more informative, and the information was generally presented in words rather
    than sentences. If adopted together with non-verbal communication such as map
    and diagram, the Chinese characters in the guidance signs and induction signs
    of historical scenic spots with a high proportion of Chinese characters could
    be omitted.BR So far, there have been many studies on the issue of multilingual
    signs from the perspective of fonts and layout. What's more, from this new perspective
    on language features, the issue of multilingual signs was explored in this study.
    It was expected that the results of this research can be applied into practice
    in practical projects.
  - Dialect Recognition Systems (DRS) are systems that group dialects, according to
    similar acoustic features found in dialect regions. The speaker's age, gender,
    and dialect characteristics negatively affect the performance of speech recognition
    systems. To handle dialect differences, dialect recognition systems can be integrated
    into speech recognition systems. By determining the spoken dialect, the system
    can be switched to the corresponding speech recognition model. There is no dataset
    that can be used for Turkish automatic dialect recognition systems. In this study,
    it is thought that this deficiency should be eliminated in some way. In addition,
    an experimental study has been carried out to classify the generated data set
    by convolutional neural networks. The resulting % accuracy is satisfactory.
- source_sentence: The social sciences have long shown that health is not born of
    pure biology, empirically (re)centred the social and material causes of disease,
    and affirmed the subjective experiences of disease. Disputed both in popular and
    academic discourses, social health has variously attempted to stress the social
    aspects of health. Existing conceptions remain analytically limited as they are
    predominantly used as descriptors for populational health. This article theorises
    social health as an analytical lens for making sense of the relations, affects
    and events where health unfolds and comes into expression. Drawing on social practice
    theory, feminist care ethics and posthumanism this conceptual paper re-imagines
    how social health might be conceived as lived social practices anchored in care.
    Care within our framework acknowledges the unavoidable interdependency foundational
    to the existence of beings and stresses the 'know how' and embodied practices
    of care in the mundane in order to emphasise that care itself is absolutely integral
    to the maintenance of social health. The article argues that health needs to be
    understood as a verb intrinsically (re)made in and through social contexts and
    structures and comprised of meaningful, human-human and human-non-human interactions.
    Ultimately, in theorising social health through mundane care practices, we hope
    to open up research to making sense of how the doing of health unfolds inside
    often banal, patterned forms of social activity. Such taken-for-granted social
    practices exemplify the often overlooked lived realities that comprise our health.
    To understand health in its own right, we argue, these everyday practices need
    to be interrogated.
  sentences:
  - This paper proposes a methodology to create an interpretable fuzzy model for monthly
    rainfall time series prediction. The proposed methodology incorporates the advantages
    of artificial neural network, fuzzy logic and genetic algorithm. In the first
    step, the differences between the time series data are calculated and they are
    used to define the interval between the membership functions of a Mamdani-type
    fuzzy inference system. Next, artificial neural network is used to develop the
    model from input-output data and the established model is then used to extract
    the fuzzy rules. The parameters of the created fuzzy model are then optimized
    by using genetic algorithm. The proposed model was applied to eight monthly rainfall
    time series data in the northeast region of Thailand. The experimental results
    showed that the proposed model provided satisfactory prediction accuracy when
    compared to other commonly-used prediction models. Due to the interpretability
    nature of the model, human analysts can gain insight knowledge of the data to
    be modeled.
  - A dB dynamic range and cm spatial resolution tunable photon-counting optical time-domain
    reflectometer (PC-OTDR) is presented along with a Field Programmable Gate Array
    (FPGA)-based detection management system that allows several regions of the fiber
    to be interrogated by the same optical pulse, increasing the data acquisition
    rate when compared to previous solutions. The optical pulse generation is implemented
    by a tunable figure- passive mode-locked laser providing pulses with the desired
    bandwidth and center wavelength for WDM applications in the C-band. The acquisition
    rate is limited by the afterpulse effect and dead time of the employed gated avalanche
    single-photon detectors. The devised acquisition system not only allows for centimeter-resolution
    monitoring of fiber links as long as km in under minutes but is also readily adapted
    to any other photon-counting strategy for increased acquisition rate. The system
    provides a -fold decrease in acquisition times when compared with state-of-the-art
    solutions, allowing affordable times in centimeter-resolution long-distance fiber
    measurements.
  - Care has been theorised in relationship to eating disorders as a central consideration
    across diagnoses. In the context of avoidant restrictive food intake disorder
    (ARFID) specifically, there is room to further develop the nuances around layers
    of care involved in working towards well-being. In this paper, we engage with
    the stories of caregivers of people with ARFID, exploring their pathways to care
    (or lack thereof) through the healthcare system in Aotearoa New Zealand. We explore
    the material, affective and relational aspects of care and care-seeking, engaging
    with the power and politics of care as it flows through care-seeking assemblages.
    Using postqualitative methods of analysis, we discuss how while participants were
    seeking care, they received (or, at times, did not receive) treatment, and unpack
    how care and treatment are not always synonymous. We work up extracts from parents'
    stories surrounding their caring for their children and how their actions were,
    at times, interpreted in ways that made them feel blame and shame rather than
    care. Participants' stories also offer glimmers of care within a resource-strapped
    healthcare system, which invite us to consider the potentiality of a relational
    ethics of care as an assemblage-shifting moment.
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: 0.9846547314578005
      name: Cosine Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: modernBERT disciplines
      type: modernBERT_disciplines
    metrics:
    - type: cosine_accuracy
      value: 0.9789272030651341
      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_v02")
# Run inference
sentences = [
    "The social sciences have long shown that health is not born of pure biology, empirically (re)centred the social and material causes of disease, and affirmed the subjective experiences of disease. Disputed both in popular and academic discourses, social health has variously attempted to stress the social aspects of health. Existing conceptions remain analytically limited as they are predominantly used as descriptors for populational health. This article theorises social health as an analytical lens for making sense of the relations, affects and events where health unfolds and comes into expression. Drawing on social practice theory, feminist care ethics and posthumanism this conceptual paper re-imagines how social health might be conceived as lived social practices anchored in care. Care within our framework acknowledges the unavoidable interdependency foundational to the existence of beings and stresses the 'know how' and embodied practices of care in the mundane in order to emphasise that care itself is absolutely integral to the maintenance of social health. The article argues that health needs to be understood as a verb intrinsically (re)made in and through social contexts and structures and comprised of meaningful, human-human and human-non-human interactions. Ultimately, in theorising social health through mundane care practices, we hope to open up research to making sense of how the doing of health unfolds inside often banal, patterned forms of social activity. Such taken-for-granted social practices exemplify the often overlooked lived realities that comprise our health. To understand health in its own right, we argue, these everyday practices need to be interrogated.",
    "Care has been theorised in relationship to eating disorders as a central consideration across diagnoses. In the context of avoidant restrictive food intake disorder (ARFID) specifically, there is room to further develop the nuances around layers of care involved in working towards well-being. In this paper, we engage with the stories of caregivers of people with ARFID, exploring their pathways to care (or lack thereof) through the healthcare system in Aotearoa New Zealand. We explore the material, affective and relational aspects of care and care-seeking, engaging with the power and politics of care as it flows through care-seeking assemblages. Using postqualitative methods of analysis, we discuss how while participants were seeking care, they received (or, at times, did not receive) treatment, and unpack how care and treatment are not always synonymous. We work up extracts from parents' stories surrounding their caring for their children and how their actions were, at times, interpreted in ways that made them feel blame and shame rather than care. Participants' stories also offer glimmers of care within a resource-strapped healthcare system, which invite us to consider the potentiality of a relational ethics of care as an assemblage-shifting moment.",
    'A dB dynamic range and cm spatial resolution tunable photon-counting optical time-domain reflectometer (PC-OTDR) is presented along with a Field Programmable Gate Array (FPGA)-based detection management system that allows several regions of the fiber to be interrogated by the same optical pulse, increasing the data acquisition rate when compared to previous solutions. The optical pulse generation is implemented by a tunable figure- passive mode-locked laser providing pulses with the desired bandwidth and center wavelength for WDM applications in the C-band. The acquisition rate is limited by the afterpulse effect and dead time of the employed gated avalanche single-photon detectors. The devised acquisition system not only allows for centimeter-resolution monitoring of fiber links as long as km in under minutes but is also readily adapted to any other photon-counting strategy for increased acquisition rate. The system provides a -fold decrease in acquisition times when compared with state-of-the-art solutions, allowing affordable times in centimeter-resolution long-distance fiber measurements.',
]
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** | **0.9847** | **0.9789**             |

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## 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: 86 tokens</li><li>mean: 240.32 tokens</li><li>max: 633 tokens</li></ul> | <ul><li>min: 84 tokens</li><li>mean: 243.66 tokens</li><li>max: 668 tokens</li></ul> | <ul><li>min: 88 tokens</li><li>mean: 237.15 tokens</li><li>max: 681 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Flash memory devices are investigated to confirm their application as physically unclonable functions (PUFs). Inherent fluctuations in the characteristics of flash memory devices, even with identical fabrication processes, produce different outputs, which are useful for device fingerprints. A difference in programming/erasing efficiency arises from a widely distributed threshold voltage. However, statistical fluctuations in the threshold voltage represent an advantage for PUF applications. The characteristics of PUFs, such as their unclonability, uncontrollability, unpredictability, and robustness, are investigated using fabricated flash memory devices. A simulation study is performed to support the experimental results and to show that the unpredictability is induced by variations in the gate dielectric thickness.</code>                                                                                   | <code>Ternary Content Addressable Memory (TCAM) is used in applications that require a low power dissipation and fast data retrieval. This paper presents a domain wall-based spintronic TCAM cell. The proposed design exploits the resistive behavior of this nonvolatile memory, reduces total power dissipation by reducing the voltage swing at the match line, and minimizes delay by employing a tiny sensing unit within each cell. Our experimental evaluation on nm technology for a -bit word-size TCAM at an V supply voltage and mV sense margin show that the delay is less than ps. The per-bit search energy is approximately fJ. Experimental evaluation on benchmark applications on the AMD Southern Islands GPU reveal that the GPU always dissipates less power when enhanced with the proposed TCAM design. Furthermore, the proposed method consumes at least % less energy when compared to state-of-the-art TCAM designs.</code>                                                                                                | <code>Abstract. The main focus of the paper is to present a flood and landslide early warning system, named HEWS (Hydrohazards Early Warning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfallstreamflow model, based on the SCS-CN method, and on the u...</code> |
  | <code>A new method for the determination of trace levels of bromates by selective membrane collection is presented. Various membranes containing a few micrograms of different complexing reagents in a poly(vinyl chloride) matrix were tested. These membranes were produced on the surface of quartz glass (reflectors), and they were immersed in solutions containing bromate and bromide ions. At the first stage the prepared membranes collected both bromate and bromide ions, so different bromide masking agents were put in the analyzed solutions to avoid bromide collection. By the end of the equilibration time, the reflectors were left to dry, and they were analyzed by total reflection X-ray fluorescence (TXRF). The poly(vinyl chloride) with aliquat- membrane and o-dianisidin complexing agent gave the best results. The minimum detection limit was equal to ng/mL for ultrapure water and ng/mL for drinking water.</code> | <code>ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTVoltammetric anion responsive sensors based on modulation of ion permeability through Langmuir-Blodgett films containing synthetic anion receptorsShinobu. Nagase, Masamitsu. Kataoka, Ryuichi. Naganawa, Ryoko. Komatsu, Kazunori. Odashima, and Yoshio. UmezawaCite this: Anal. Chem. , , , 00000000Publication Date (Print):July , 0000Publication History Published online0 May 0000Published inissue July 0000https://pubs.acs.org/doi/ /ac00000a000https://doi.org/ /ac00000a000research-articleACS PublicationsRequest reuse permissionsArticle Views000Altmetric-Citations00LEARN 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 abo...</code> | <code>This study investigated whether performance of an interceptive skill requires an intact visual-perception-action cycle. Eleven skilled male Australian rules football athletes (M age = , SD = ) were recruited from an elite developmental pathway squad for a within-subject study. Participants were required to kick a ball directly at a goal from a -meter distance while wearing a pair of stroboscopic glasses. The glasses were used to create four vision conditions. Condition one kept intact the visual-perception-action cycle with uninterrupted vision of the motor skill. Three other conditions included stroboscopic vision that presented temporal samples of vision, which interrupted the perception-action cycle through progressive increases to intermittent vision occlusion of the motor skill. Goal kick error of ball position relative to a central target line within the goal and number of successful goals kicked were measured. Written report of internal and external focus of attention was also m...</code> |
  | <code>The study aimed to determine the effectiveness of Contextual Teaching and Learning (CTL) in reducing and improving learning outcomes and math anxiety among students at a private elementary school in Indonesia. The research utilized a one-group control pre-posttest design with a sample of 0th-grade students. The study used a combination of pre-test and post-test and a closed-ended questionnaire as the data collection instruments. The independent variable in the study was CTL, while the dependent variables were learning outcomes and math anxiety. The paired t-test showed a significant increase in the students' average learning outcomes and a decrease in the average math anxiety levels. The findings suggest that implementing CTL is a practical approach to reducing math anxiety and improving student learning outcomes.</code>                                                                                    | <code>This study aims to determine the problem-solving ability of field independent (FI) and field dependent (FD) students in solving HOTS story problems. This type of research is qualitative research. The research strategy used is a descriptive model. This research was carried out at a junior school in Malang, Indonesia. The respondent was tenth-grade students. Data collection methods in this study include tests and interviews. Data analysis techniques include data collection, reduction, presentation, and concluding. The results of this study show that FI and FD students understand the problem. There is no difference between the two; FI and FD students are good at understanding the problem. FI students plan solutions well and can correctly create mathematical models, while FD students have difficulty developing mathematical models. In getting answers, FI and FD students have something in common: they are not quite right in the final solution.</code>                                                     | <code>The recently proposed recursive least-squares (RLS) algorithm for trilinear forms, namely RLS-TF, was designed for the identification of third-order tensors of rank one. In this context, a high-dimension system identification problem can be efficiently addressed (gaining in terms of both performance and complexity) based on tensor decompositions and modelling. In this paper, following the framework of the RLS-TF, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming at attenuating the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments.</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: 237.84 tokens</li><li>max: 629 tokens</li></ul> | <ul><li>min: 93 tokens</li><li>mean: 239.31 tokens</li><li>max: 610 tokens</li></ul> | <ul><li>min: 83 tokens</li><li>mean: 234.79 tokens</li><li>max: 499 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The aim of the study was to determine the relationship between emotional intelligence and cohesion in a sports team of girls engaged in synchronized figure skating. The following psychological tests were used in the study: the Emotional Intelligence test by D.V. Lyusin, a test to determine the index of group cohesion of the Sisor. The study was conducted on the basis of the sports school "Yunost" in Yekaterinburg. Two teams of different age groups took part in the experiment: athletes performing in the category of "novices" ( years old), girls performing in the team of "CMS" ( - years old). Testing was conducted twice: at the beginning of the season and after the competitive season. The study revealed positive dynamics of the development of cohesion in both teams. It also revealed reliable relationships between interpersonal emotional intelligence and the level of cohesion in the team. Further research may be aimed at developing a strategy to increase emotional intelligence as a factor...</code> | <code>Recreational swimming can be used as a reliable preventive measure for those diseases that are widespread among students. The purpose of the research is to study the effect of swimming on the functional state of students. The study involved male students who are selfemployed in swimming and male students who are professionally engaged in the swimming section. The research methods used samples of Martinet-Kushelevsky, Rufier, Stange and Genchi, as well as chest excursions. It was revealed that students, who practice swimming in the section, have more favorable conditions for a comprehensive effect on the body than students who swim independently, due to a greater load during training and their systematic nature.</code>                                                                                                                                                                                                                                                                                            | <code>Mg Ni + x% Ti . Mn . V . ( x = ,00,and ) composites were prepared by hydriding combustion synthesis( HCS) and the HCS products were mechanically milled( MM) to obtain Mg-based hydrogen-storage composites. The dehydriding properties,phase structure,surface morphology,and particle composition were studied by pressure-composition-temperature( pcT),X-ray diffraction( XRD) and scanning electron microscopy( SEM). Results showed that addition of %( mass fraction) Ti . Mn . V . exhibited the best desorption property for the HCS + MM product of Mg Ni , which could completely desorb . % H in s at K. The apparent dehydrogenation activation energy of the system was decreased to . kJ / mol from . kJ / mol of Mg Ni . The improvement of the desorption property could be attributed to the enhancement of diffusion and the hydrogen pumpingof Ti . Mn . V . .</code>                                                                                                                                                          |
  | <code>This article has been retracted: please see Elsevier Policy on Article Withdrawal ( ). This article has been retracted at the request of authors due to scientific errors reported by authors. The author reported errors are: : In the " Case Description" section, Fig. A0 (wind and PV output power) is the input data for the simulation calculation. The authors report that, due to an oversight, they did not use real wind and PV output power data, which would lead to inaccurate results for the system simulation calculations. : For the " Model solving algorithm", the authors found that it is incorrect to use the properties of Gaussian functions to improve the CDE algorithm because Gaussian functions do not have the properties of concave functions. This is evidenced in the literature "DOI: : Fig. (Iterative Convergence Curve of Rastrigin Function) is tested using the benchmark test function (Rastrigin function) in order to demonstrate the feasibility of the GCDE algorithm. However, it is clear ...</code> | <code>Energy accessibility especially electrical energy is considered as one of the most appealing factors to achieve energy sustainability. The purpose of this study is to investigate energy sustainability using renewable energies for two high potential cities in the south-east of Iran until the year . In this regard, Homer software is used to evaluate economic and technical analyses of PV-wind-diesel hybrid system for the two cities by the data gathering which was collected from Iran's meteorological organization. Therefore, the average of solar radiation per month for Zabol and Zahak were about and (h/d). Also, mean wind speeds are calculated m/s and m/s for Zabol and Zahak respectively which proposed that these cities have high potential in order to electrical production by a hybrid system. Furthermore, the amount of electricity production by PV array for Zabol and Zahak were (kWh/yr) and (kWh/yr) respectively, and the amount of electricity production by wind turbine were (kWh/yr) and (k...</code> | <code>The philosophy that built by German Idealism is obtained and never neglected religion, this is not about the religious dogmas or the fantasy and legendary nature of religion, but it is about the spirit and the crux of religion. Nevertheless, there is always struggled to deprive it from fantasies and rebuilt by philosophical ideas. These ideal philosophers are asserted to reconstruct the stories and imaginary schemes of religion into philosophical and rational thinking. There is a change in the result of this process which is religion is retreated and the metaphysics is slightly appeared. In other word, this change is directed from revelation to metaphysical views. In the light of this, the German Idealism is taking two different ways toward religion: the negative direction; which is involved to the critical studies of the basis and construction of religion, and the positive direction; this direction is returned to religion, but this return is happened after reconstruct religion by the ...</code> |
  | <code>In this paper measurements of momentum and current transport caused by current driven tearing instability are reported. The measurements are done in the Madison Symmetric Torus reversed-field pinch [R. N. Dexter, D. W. Kerst, T. W. Lovell, S. C. Prager, and J. C. Sprott, Fusion Technol. , ( )] in a regime with repetitive bursts of tearing instability causing magnetic field reconnection. It is established that the plasma parallel momentum profile flattens during these reconnection events: The flow decreases in the core and increases at the edge. The momentum relaxation phenomenon is similar in nature to the well established relaxation of the parallel electrical current and could be a general feature of self-organized systems. The measured fluctuation-induced Maxwell and Reynolds stresses, which govern the dynamics of plasma flow, are large and almost balance each other such that their difference is approximately equal to the rate of change of plasma momentum. The Hall dynamo, which is d...</code> | <code>We present measurements of magnetic fields generated in laser-driven coil targets irradiated by laser pulses of nanosecond duration, m wavelength, J energy, and W/cm0 intensity, at the LULI0000 facility. Using two perpendicular probing axes, proton deflectometry is used to characterize the coil current and static charge at different times. Results reveal various deflection features that can be unambiguously linked to a looping quasi-steady current of well-understood polarity or to a static charging of the coil surface. Measured currents are broadly consistent with predictions from a laser-driven diode-current source and lumped circuit model, supporting the quasi-steady assessment of the discharges. Peak magnetic fields of T at the center of -m-diameter coils, obtained at the moderate laser intensity, open up the use of such laser-driven coil targets at facilities worldwide to study numerous phenomena in magnetized high-energy-density plasmas, and its potential applications.</code>                | <code>EU , , , , , . . . , . . , . , - -EU . .In August , the UK launched a new export strategy to increase UK total exports as a proportion of gross domestic product (GDP) to % and to build trading relationships around the world after Brexit. And the government aims to strengthen UK's position as one of the 00st century's great trading nations and to expand the export of traders by setting the five principle. These principles are a business-led approach, doing what only government can do, joining up across government with local partners and the private sector, digital by design and value for money. This paper examines the background, purpose and main contents of the UK new export strategy in UK and the countermeasures for the new UK export strategy. First of all, we should prepare a scenarios based on directions of Brexit. Second, it is necessary to discuss the redefinition of relationship with Korea-UK and Korea-EU. And finally, Korean companies should enter the UK by utilizing the e-comme...</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
- `num_train_epochs`: 2
- `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`: 2
- `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    | -             | -               | 0.8951                     | -                                      |
| 0.0511 | 100  | 0.0064        | 0.0049          | 0.9616                     | -                                      |
| 0.1022 | 200  | 0.002         | 0.0071          | 0.9565                     | -                                      |
| 0.1533 | 300  | 0.0076        | 0.0034          | 0.9795                     | -                                      |
| 0.2044 | 400  | 0.0074        | 0.0039          | 0.9668                     | -                                      |
| 0.2555 | 500  | 0.0036        | 0.0036          | 0.9693                     | -                                      |
| 0.3066 | 600  | 0.0035        | 0.0029          | 0.9770                     | -                                      |
| 0.3577 | 700  | 0.004         | 0.0035          | 0.9693                     | -                                      |
| 0.4088 | 800  | 0.0027        | 0.0034          | 0.9770                     | -                                      |
| 0.4599 | 900  | 0.0044        | 0.0032          | 0.9719                     | -                                      |
| 0.5110 | 1000 | 0.0037        | 0.0053          | 0.9565                     | -                                      |
| 0.5621 | 1100 | 0.0048        | 0.0029          | 0.9795                     | -                                      |
| 0.6132 | 1200 | 0.0032        | 0.0031          | 0.9744                     | -                                      |
| 0.6643 | 1300 | 0.0023        | 0.0036          | 0.9744                     | -                                      |
| 0.7154 | 1400 | 0.0044        | 0.0029          | 0.9821                     | -                                      |
| 0.7665 | 1500 | 0.0022        | 0.0032          | 0.9795                     | -                                      |
| 0.8176 | 1600 | 0.0036        | 0.0034          | 0.9770                     | -                                      |
| 0.8687 | 1700 | 0.0022        | 0.0031          | 0.9821                     | -                                      |
| 0.9198 | 1800 | 0.0028        | 0.0025          | 0.9821                     | -                                      |
| 0.9709 | 1900 | 0.0054        | 0.0025          | 0.9821                     | -                                      |
| 1.0220 | 2000 | 0.003         | 0.0029          | 0.9770                     | -                                      |
| 1.0731 | 2100 | 0.0018        | 0.0026          | 0.9795                     | -                                      |
| 1.1242 | 2200 | 0.0021        | 0.0024          | 0.9847                     | -                                      |
| 1.1753 | 2300 | 0.0015        | -               | -                          | 0.9789                                 |


### 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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