sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:7828 loss:TripletLoss arxiv:1908.10084 arxiv:1703.07737 base_model:allenai/specter base_model:finetune:allenai/specter model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: allenai/specter
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: 'The Taiwan Government follows the policy of active aging to prevent
    frailty. However, the current services lack cultural safety toward the Indigenous
    peoples and would benefit from a broader perspective on what active aging may
    entail. In this research, we study local perceptions of active aging among older
    Indigenous Tayal taking part in a local day club. The study identifies two formal
    activities that foster active aging: (a) information meetings about health and
    illness and (b) physical activities. In addition, two informal activities highlighted
    by the participants themselves were identified as necessary for promoting healthy
    and active aging: Cisan and Malahang. While Cisan means "social care," Malahang
    means "interrelational care practices." In conclusion, we argue for the relevance
    of listening to Indigenous older adults'' voices to develop long-term care services
    adapted to their cultural values, linguistic competence, and cosmology.'
  sentences:
  - 'ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTLaser Raman spectroscopy as a mechanistic
    probe of the phosphate transfer from adenosine triphosphate in a model systemAaron
    Lewis, Nathan Nelson, and Efraim RackerCite this: Biochemistry , , , 00000000Publication
    Date (Print):April , 0000Publication History Published online0 May 0000Published
    inissue April 0000https://pubs.acs.org/doi/ /bi00000a000https://doi.org/ /bi00000a000research-articleACS
    PublicationsRequest reuse permissionsArticle 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 InRedditEmail Other access optionsGet e-Alertsclose
    Get e-Alerts'
  - Existing cognitive health literature focuses on the perspectives of older adults
    with dementia. However, little is known about the ways in which healthy older
    adults without dementia understand their cognitive health. In rural communities,
    early dementia diagnosis may be impeded by numerous factors including transportation
    challenges, cultural obstacles, and inadequate access to health and support services.
    Based on participant observation and two waves of semi-structured interviews,
    this study examined healthy, rural older adults' perceptions of cognitive health.
    By providing an innovative theoretical foundation informed by local perspectives
    and culture, findings reveal a complex and multidimensional view of cognitive
    health. Rural older adults described four key areas of cognitive health ranging
    from independence to social interaction. As policy makers, community leaders,
    and researchers work to address the cognitive health needs of the rural aging
    demographic, it is essential that they listen to the perspectives of rural older
    adults.
  - 'Juvenile Court Judges JournalVolume , Issue p. - From the President-Elect JUDGE
    CLAYTON W. ROSE, JUDGE CLAYTON W. ROSESearch for more papers by this author JUDGE
    CLAYTON W. ROSE, JUDGE CLAYTON W. ROSESearch for more papers by this author First
    published: October 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. Volume00, Issue0October
    0000Pages - RelatedInformation'
- source_sentence: 'Mount Ciremai National Park (TNGC) is a National Park (TN) which
    has enormous ecological functions, especially as a water catchment area. Forest
    fires in TNGC occur every year and fluctuated from year to year. Forst fires destroy
    ecosystems and interfere the function of TNGC. Anti Fire Community (MPA) is a
    partnership which consist of local communities involved in forest fire control.
    Community partnership will never succeess without the MPA''s participation. The
    research objectives are to describe the perception and participation of MPA on
    the forest fires control in TNGC and the implementation of MPA policies. This
    research method is done by questionnaires, observation and interviews. The results
    showed that MPA positively perceive that dry season as supporting factors and
    community activities that involve a fire as direct factors of forest fires. The
    public perception is not always in line with the participation. A strong perception
    does not guarantee a high participation, it might be the opposit (low participation).
    The highest MPA''s participation in forest fires control is in forest fighting
    activities. Affecting factors on MPA''s participation in forest fire control activities
    are economic factors ie wage, logistics dan goods. Occuring gap between the ideal
    conditions and real conditions is percent. Perceptions which is not in line with
    the participation and the emerge gap is suspected to cause unoptimized of forest
    fire control conducted by MPA in TNGC. Keywords: forest fire control, gap, MPA,
    particiation, perception'
  sentences:
  - 'Objective To investigate the current status of clinical medical students'' time
    engagement in extracurricular activities and its association with perceived stress,
    so to provide reference for promoting the moral, intellectual and physical development
    of clinical medical students as well as promoting the professional level of extracurricular
    activities in medical universities in China. Methods In December , altogether
    students of clinical medicine major in a medical university enrolled in were investigated.
    General information questionnaire, extracurricular activities time engagement
    questionnaire of medical students and -items perceived stress scale were used
    to conduct questionnaire survey. The relationship between time engagement of extracurricular
    activities and perceived stress of medical students was analyzed by orderly logistic
    regression. Results The medical students, % ( / ) and % ( / ) of them, spent more
    than hours per week on extracurricular study and leisure and recreation, % ( /
    ) of the students spent less than hours per week on physical exercise, % ( / )
    of the students did not devote their time to volunteering. The perceived stress
    score of students was ( , ). After controlling personal and family characteristics,
    the results of ordinal logistic regression analysis indicated that the ones with
    high perceived stress devoted less time to extracurricular learning (OR= , %CI=
    ~ ), volunteering (OR= , %CI= ~ ) and exercising (OR= , %CI= ~ ). Conclusions
    Clinical medical students showed a low level of perceived stress. Perceived stress
    has a significant negative impact on students'' time engagement in extracurricular
    learning, volunteering and physical exercise. Medical schools should focus on
    maintaining low level of perceived stress among clinical medical students. Key
    words: Medical students;Extracurricular activities;Time engagement;Perceived stress;Ordinal
    logistic regression'
  - 'ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTLOCAL ANESTHETICS IN THE PYRROLE
    SERIES. IIF. F. Blicke and E. S. BlakeCite this: J. Am. Chem. Soc. , , , 00000000Publication
    Date (Print):March , 0000Publication History Published online0 May 0000Published
    inissue March 0000https://pubs.acs.org/doi/ /ja00000a000https://doi.org/ /ja00000a000research-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 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 InRedditEmail Other access optionsGet e-Alertsclose
    Get e-Alerts'
  - 'Recently a comprehensive source of data and information on carbon storage in
    various types of forest ecosystems and other land use in Java Island are still
    limited. This study was carried out in a conservation area of Bromo Tengger Semeru
    National Park (TNBTS) that represents the ecosystem types of lowland rain forest,
    sub-montane forests and mountain forests in Java. The information on carbon sequestration
    and carbon stocks at TNBTS becomes important. The main objective of this study
    was to estimate biomass and carbon storage in various types of forests in TNBTS
    using allometric approaches. The additional objectives were to estimate carbon
    storage on various land cover and to estimate the changes in carbon storage by
    land cover changes during the period , and . The measurement of forest carbon
    include aboveground, understorey, necromass and litter pools covering all ecosystem
    such as primary forest, secondary forest with high- and low- canopy density. This
    study found that the average of carbon stocks in primary forest were tonC/ha,
    and were tonC/ha in secondary forest. The total carbon stocks in the period has
    decreased about tonC/ha/year and in the period has increased about tonC/ha/year.
    The enhancement of carbon stocks in this area was driven by an intensive forest
    protection, good monitoring and land rehabilitation. Keywords: biomass, carbon
    storage, carbon stock, land cover , national park'
- source_sentence: In this paper, we present a novel multiple input multiple output
    (MIMO) linear parameter varying (LPV) state-space refinement system identification
    algorithm that uses tensor networks. Its novelty mainly lies in representing the
    LPV sub-Markov parameters, data and state-revealing matrix condensely and in exact
    manner using specific tensor networks. These representations circumvent the 'curse-of-dimensionality'
    as they inherit the properties of tensor trains. The proposed algorithm is 'curse-of-dimensionality'-free
    in memory and computation and has conditioning guarantees. Its performance is
    illustrated using simulation cases and additionally compared with existing methods.
  sentences:
  - This paper proposes a new predictive controller approach for nonlinear process
    based on a reduced complexity homogeneous, quadratic discrete-time Volterra model
    called quadratic S-PARAFAC Volterra model. The proposed model is yielded by using
    the symmetry property of the Volterra kernels and their tensor decomposition using
    the PARAFAC technique that provides a parametric reduction compared to the conventional
    Volterra model. This property allows synthesising a new nonlinear-model-based
    predictive control (NMBPC). We develop the general form of a new predictor, and
    therefore, we propose an optimisation algorithm formulated as a quadratic programming
    under linear and nonlinear constraints. The performances of the proposed quadratic
    S-PARAFAC Volterra model and the developed NMBPC algorithm are illustrated on
    a numerical simulation and validated on a benchmark as a continuous stirred-tank
    reactor system. Moreover, the efficiency of the proposed quadratic S-PARAFAC Volterra
    model and the NMBPC approach are validated on an experimental communicating two-tank
    system.
  - Importance Several studies have found no temporal or demographic differences in
    the incidence of retinoblastoma except for age at diagnosis, whereas other studies
    have reported variations in incidence by sex and race/ethnicity. Objective To
    examine updated US retinoblastoma incidence patterns by sex, age at diagnosis,
    laterality, race/ethnicity, and year of diagnosis. Design, Setting, and Participants
    The Surveillance, Epidemiology, and End Results (SEER) databases were examined
    for retinoblastoma incidence patterns by demographic and tumor characteristics.
    We studied children in SEER registries, in SEER registries, and in SEER registries.
    Main Outcomes and Measures Incidence rates, incidence rate ratios (IRRs), and
    annual percent changes in rates. Results During - in SEER , there was a significant
    excess of total retinoblastoma among boys compared with girls (IRR, ; % CI, to
    ), in contrast to earlier reports of a female predominance. Bilateral retinoblastoma
    among white Hispanic boys was significantly elevated relative to white non-Hispanic
    boys (IRR, ; % CI, to ) and white Hispanic girls (IRR, ; % CI, to ) because of
    less rapid decreases in bilateral rates since the 0000s among white Hispanic boys
    than among the other groups. Retinoblastoma rates among white non-Hispanics decreased
    significantly since among those younger than year and since among those with bilateral
    disease. Conclusions and Relevance Although changes in the availability of prenatal
    screening practices for retinoblastoma may have contributed to these incidence
    patterns, further research is necessary to determine their actual effect on the
    changing incidence of retinoblastoma in the US population. In addition, consistent
    with other cancers, an excess of retinoblastoma diagnosed in boys suggests a potential
    effect of sex on cancer origin.
  - 'Improving the health behaviour can help prevent stroke recurrence. The existing
    health education interventions require more human resource. There is a lack of
    constructing a low-cost, highly universal, and easy-to-use stroke secondary prevention
    platform based on the existing medical resources.This was a randomized controlled
    trial to test the effects of a digital learning platform on the health knowledge,
    beliefs, and behaviours of stroke patients from baseline to months after discharge.
    The control group received routine health education while the intervention group
    received health belief education during hospitalization and used a digital learning
    platform for months after discharge. The health knowledge was assessed by The
    Stroke Health Knowledge Questionnaire, health beliefs by The Short Form Health
    Belief Model Scale for Stroke Patients, and health behaviours by the Stroke Health
    Behavior Scale. A total of patients were included: each in the intervention group
    and the control group, of whom and completed the study, respectively. At months
    after discharge, ( ) the health knowledge score of the intervention group was
    insignificantly higher than that of the control group, ( ) the health belief score
    of the intervention group was significantly higher than that of the control group,
    and ( ) the intervention group had higher health behaviour scores especially in
    physical activity than that of the control group. Other health behaviour dimensions
    have time effect, but not significant.The digital learning platform can improve
    health behaviours of stroke patients months after discharge, especially in physical
    activity.ChiCTR0000000000.'
- source_sentence: The present investigation was undertaken to study the involvement
    of Apatani women of Arunachal Pradesh in farm and home activities with the objective
    to study the selected socio-personal characteristics of Apatani women of Arunachal
    Pradesh and to identify the extent of involvement of Apatani women in selected
    farm and home activities.The study was conducted in Lower Subansiri district of
    Arunachal Pradesh.Four villages were selected for the present study.Data were
    collected with the help of interview schedule.Statistical technique viz., frequency,
    percentage, mean and standard deviation and coefficient correlation were used
    for analyzing the data.The study revealed that majority of the respondents were
    within the age group of - , belonged to Hindu religion were mostly illiterate,
    married, having nuclear family and member of one organization.Observations revealed
    that all the respondents independently participated in sowing of seed, nursery
    raising, leveling of field, weeding, gap filing and application of organic manure.The
    findings revealed that correlation between extent of participation in farm activities
    and land holding was negative and significant.While relationship between extent
    of participation in home activities with family size was positive and significantt.The
    mass media exposure and occupation of the family had positive and significant
    relationship with extent of participation in decision making pattern in home activities.The
    correlation between extent of participation in farm activities and land holding
    was negative and significant while relationship between extent of participation
    in home activities with family size was positive and significant.
  sentences:
  - The present investigation was conducted to assess the knowledge of farm women
    about farm broadcast 'Kheti Ri Baata' of state Department of Agriculture, Rajasthan.The
    study was conducted in four villages viz., Gadoli, Nandwel, Mavli and Thamla of
    randomly selected Mavli Panchayat Samiti of Udaipur district of Rajasthan.A sample
    of farm women was selected for the present study.Personal interview method was
    used for data collection.Frequency, percentage and mean per cent score were used
    for analysis of the data.More than half of the respondents ( %) were not aware
    about the farm broadcast and very few ( %) were viewing the programme regularly.
  - Reninangiotensin aldosterone system inhibitors are for a long time extensively
    used for the treatment of cardiovascular and renal diseases. AT0 receptor blockers
    (ARBs or sartans) act as antihypertensive drugs by blocking the octapeptide hormone
    Angiotensin II to stimulate AT0 receptors. The antihypertensive drug candesartan
    (CAN) is the active metabolite of candesartan cilexetil (Atacand, CC). Complexes
    of candesartan and candesartan cilexetil with -hydroxylpropyl--cyclodextrin (
    -HP--CD) were characterized using high-resolution electrospray ionization mass
    spectrometry and solid state 00C cross-polarization/magic angle spinning nuclear
    magnetic resonance (CP/MAS NMR) spectroscopy. The 00C CP/MAS results showed broad
    peaks especially in the aromatic region, thus confirming the strong interactions
    between cyclodextrin and drugs. This experimental evidence was in accordance with
    molecular dynamics simulations and quantum mechanical calculations. The synthesized
    and characterized complexes were evaluated biologically in vitro. It was shown
    that as a result of CAN's complexation, CAN exerts higher antagonistic activity
    than CC. Therefore, a formulation of CC with -HP--CD is not indicated, while the
    formulation with CAN is promising and needs further investigation. This intriguing
    result is justified by the binding free energy calculations, which predicted efficient
    CC binding to -HP--CD, and thus, the molecule's availability for release and action
    on the target is diminished. In contrast, CAN binding was not favored, and this
    may allow easy release for the drug to exert its bioactivity.
  - Heterogeneous electron-transfer rate measurements using the scanning electrochemical
    microscope are reported for the [M(TCTA)](-/ ) couples (M = Mn, Fe, and Ni) in
    aqueous solution. Solution IR spectroscopy indicates that N( )O( ) coordination
    is preserved for each couple within the pH range of - , and susceptibility measurements
    indicate little or no interference from spin-state changes at room temperature.
    Marcus-Hush expressions were used to quantitatively relate structural differences
    between oxidation states to measured standard heterogeneous electron-transfer
    rate constants. Good correlation was obtained for the Fe couple, and structural
    changes associated with the Mn and Ni couples were estimated. In addition, the
    structure of the Fe(II) complex was determined by X-ray crystallography. The molecule
    [Fe(H( )O)( )][Fe(TCTA)]( ) is trigonal, space group P0( )/c (no. ) with a = b
    = ( ) A, c = ( ) A, and Z = . A notable feature of the structure is that the [Fe(TCTA)](-)
    complex is distributed between two different geometries, one being rigorously
    trigonal prismatic and the other having a antiprismatic twist.
- source_sentence: 'To deal with the theme of the "unrepresented" it is necessary
    to clarify what we mean by representation. Depending on whether a formal, substantial,
    descriptive or symbolic concept of representation is adopted, in fact, the answer
    to the question: "Who are the unrepresented?" changes. Based on a formal concept,
    the unrepresented are those formally excluded from political rights. On the basis
    of a substantial conception, instead, they too can be considered represented,
    if there is someone who pursues their interests in the institutions. According
    to a descriptive concept, an assembly selected through the draw must be considered
    representative. The same can be said of a leader in whom a community identifies
    itself symbolically. The author claims that the adoption of exclusively substantial,
    descriptive or symbolic conceptions of representation involves many problems from
    the point of view of democratic theory, and therefore adopts a formal perspective.
    According to it, the unrepresented can be divided into three categories: a) who
    has not the right to elect representatives; b) who has this right, but fails to
    elect his or her own representative; c) who has this right but doesn''t exercise
    it. The first category includes foreign residents without citizenship in democratic
    countries. The author argues that discrimination against them is not rationally
    justifiable, because it cannot be based on any of the classic arguments developed
    to limit political rights (such as lack of capacity, independence, or interest).
    The second category includes those who vote, but don''t contribute to the election
    of anyone representing them. The existence of this category raises the problem
    of distorting electoral laws, and the issue of the size of representative assemblies.
    The third category includes those who don''t exercise their political rights.
    A worrying sign that the vote by many is no longer perceived as a vehicle for
    change.'
  sentences:
  - 'Background: Disability, societal, and health impact of chronic intractable pain
    secondary to various failed therapies is a major issue. As advanced therapy, implantable
    therapies, which include intrathecal devices and spinal cord stimulation systems,
    are frequently used in managing chronic intractable pain. Thus, continuous infusion
    of intrathecal medication is one of the methods used for the control of chronic,
    refractory, cancer, and non-cancer pain. However, despite the high costs of chronic
    non-cancer pain, it has been claimed that there is a lack of evidence for intrathecal
    infusion systems and the cost effectiveness of these systems has been questioned
    in improving pain and function. Study Design: A systematic review of intrathecal
    infusion devices for chronic non-cancer pain. Objective: To determine the efficacy,
    utilization, safety, and complications associated with the use of intrathecal
    infusion devices for long-term management of chronic non-cancer pain. Methods:
    Literature search was performed through EMBASE, Medline, Cochrane databases, and
    systematic reviews identified from to December . Studies were then reviewed and
    assessed using the Agency for Healthcare Research and Quality (AHRQ) criteria
    for observational studies and the Cochrane Musculoskeletal Review Group criteria
    for randomized trials. The level of evidence was determined using levels of evidence,
    ranging from Level I to III with subcategories in Level II, based on the quality
    of evidence developed by the U.S. Preventive Services Task Force (USPSTF). Outcome
    Measures: The primary outcome measure was pain relief (short-term relief one-year
    and long-term > one-year). Secondary outcome measures of improvement in functional
    status, psychological status, return to work, and reduction in opioid intake were
    also utilized. Results: The level of evidence for intrathecal infusion systems
    indicated either Level II- or Level III (limited) based on U.S. Preventive Services
    Task Force (USPSTF) criteria. Limitations: The limitations of this study include
    the paucity of literature, lack of quality evidence, and lack of randomized trials.
    Conclusion: This systematic review illustrates Level II- or Level III (limited)
    evidence for intrathecal infusion systems for long-term relief in chronic non-cancer
    pain. Key words: Intrathecal infusion, intraspinal infusion, programmable infusion
    systems, spinal infusion, intra-spinal infusion devices, baclofen infusion, intrathecal
    opiates'
  - 'Human attachment relationships are considered to be foundational to psychological
    well-being (Fonagy, ; Warren, Huston, Egeland, & Sroufe, ) and, by extension,
    attachment to God is often considered foundational to psychological well-being
    amongst Christian believers (Kirkpatrick, ; Miner, ). However, studies of psychological
    need satisfaction by different attachment figures (La Guardia, Ryan, Couchman,
    & Deci, ) suggest that experiences in which basic psychological needs are satisfied
    are conducive to more secure attachment relationships, and thus, to enhanced psychological
    well-being. This paper tests two contrasting models of attachment to God, need
    satisfaction, and well-being: the Attachment Security Primacy Model which holds
    that attachment security facilitates experiences of psychological need satisfaction
    and thence increased well-being; and the Need Satisfaction Primacy Model which
    holds that experiences of psychological need satisfaction facilitate attachment
    security and thence increased well-being. Using self-report data from Australian
    Christian participants, Structural Equation Modeling indicated that the Need Satisfaction
    Primacy Model fit the data better than competing models. Implications for augmenting
    theories of attachment to God and providing contexts in which people can experience
    God as meeting basic needs are discussed.'
  - 'The constitutional State is under attack. If we separate Rule of Law and democratic
    sovereignty, civil rights and social rights, the holding of pluralist democracies
    is jeopardized. The sunset of the Rule of Law risks being one of the most dangerous
    consequences of neoliberal globalism and its crisis. The demolition of the welfare
    State and the technocratic depletion of politics have in fact generated a distortion
    of constitutional democracies, which can open the way for the questioning of the
    Rule of law. The opposing ideological narratives on the Rule of Law can be grouped
    according to two visions: an optimistic one, which sees in neo-liberal globalization
    the opportunity for its generalized diffusion; a radical-maximalist, which completely
    liquidates its regulatory framework and inheritance. The essay analyzes these
    two trends, to focus then on the emergency paradigm as a challenge to the "Rule
    of law".'
model-index:
- name: SentenceTransformer based on allenai/specter
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: specter og
      type: specter_og
    metrics:
    - type: cosine_accuracy
      value: 0.9840357598978289
      name: Cosine Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: modernBERT disciplines
      type: modernBERT_disciplines
    metrics:
    - type: cosine_accuracy
      value: 0.9846743295019157
      name: Cosine Accuracy
---

# SentenceTransformer based on allenai/specter

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [allenai/specter](https://huggingface.co/allenai/specter). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [allenai/specter](https://huggingface.co/allenai/specter) <!-- at revision 81cbfb43d4fc2e728d5b4201ce14987db8d0854c -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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-tuned_specter_1_001")
# Run inference
sentences = [
    'To deal with the theme of the "unrepresented" it is necessary to clarify what we mean by representation. Depending on whether a formal, substantial, descriptive or symbolic concept of representation is adopted, in fact, the answer to the question: "Who are the unrepresented?" changes. Based on a formal concept, the unrepresented are those formally excluded from political rights. On the basis of a substantial conception, instead, they too can be considered represented, if there is someone who pursues their interests in the institutions. According to a descriptive concept, an assembly selected through the draw must be considered representative. The same can be said of a leader in whom a community identifies itself symbolically. The author claims that the adoption of exclusively substantial, descriptive or symbolic conceptions of representation involves many problems from the point of view of democratic theory, and therefore adopts a formal perspective. According to it, the unrepresented can be divided into three categories: a) who has not the right to elect representatives; b) who has this right, but fails to elect his or her own representative; c) who has this right but doesn\'t exercise it. The first category includes foreign residents without citizenship in democratic countries. The author argues that discrimination against them is not rationally justifiable, because it cannot be based on any of the classic arguments developed to limit political rights (such as lack of capacity, independence, or interest). The second category includes those who vote, but don\'t contribute to the election of anyone representing them. The existence of this category raises the problem of distorting electoral laws, and the issue of the size of representative assemblies. The third category includes those who don\'t exercise their political rights. A worrying sign that the vote by many is no longer perceived as a vehicle for change.',
    'The constitutional State is under attack. If we separate Rule of Law and democratic sovereignty, civil rights and social rights, the holding of pluralist democracies is jeopardized. The sunset of the Rule of Law risks being one of the most dangerous consequences of neoliberal globalism and its crisis. The demolition of the welfare State and the technocratic depletion of politics have in fact generated a distortion of constitutional democracies, which can open the way for the questioning of the Rule of law. The opposing ideological narratives on the Rule of Law can be grouped according to two visions: an optimistic one, which sees in neo-liberal globalization the opportunity for its generalized diffusion; a radical-maximalist, which completely liquidates its regulatory framework and inheritance. The essay analyzes these two trends, to focus then on the emergency paradigm as a challenge to the "Rule of law".',
    'Human attachment relationships are considered to be foundational to psychological well-being (Fonagy, ; Warren, Huston, Egeland, & Sroufe, ) and, by extension, attachment to God is often considered foundational to psychological well-being amongst Christian believers (Kirkpatrick, ; Miner, ). However, studies of psychological need satisfaction by different attachment figures (La Guardia, Ryan, Couchman, & Deci, ) suggest that experiences in which basic psychological needs are satisfied are conducive to more secure attachment relationships, and thus, to enhanced psychological well-being. This paper tests two contrasting models of attachment to God, need satisfaction, and well-being: the Attachment Security Primacy Model which holds that attachment security facilitates experiences of psychological need satisfaction and thence increased well-being; and the Need Satisfaction Primacy Model which holds that experiences of psychological need satisfaction facilitate attachment security and thence increased well-being. Using self-report data from Australian Christian participants, Structural Equation Modeling indicated that the Need Satisfaction Primacy Model fit the data better than competing models. Implications for augmenting theories of attachment to God and providing contexts in which people can experience God as meeting basic needs are discussed.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Triplet

* Datasets: `specter_og` and `modernBERT_disciplines`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | specter_og | modernBERT_disciplines |
|:--------------------|:-----------|:-----------------------|
| **cosine_accuracy** | **0.984**  | **0.9847**             |

<!--
## 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: 88 tokens</li><li>mean: 245.68 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 85 tokens</li><li>mean: 243.1 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 77 tokens</li><li>mean: 242.04 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>ChemInformVolume , Issue Reviews ChemInform Abstract: -Chloro- -aza- -propeniminium Units as Versatile Building Blocks in Organic Synthesis J. LIEBSCHER, J. LIEBSCHER Sekt. Chem., Humboldt-Univ., DDR- BerlinSearch for more papers by this author J. LIEBSCHER, J. LIEBSCHER Sekt. Chem., Humboldt-Univ., DDR- BerlinSearch for more papers by this author First published: January , 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 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. Volume00, Issue0January , RelatedInformation</code>               | <code>ChemInformVolume , Issue Heterocyclic Compounds ChemInform Abstract: Anhydrous CeCl0-Catalyzed C0-Selective Propargylation of Indoles with Tertiary Alcohols. Claudio C. Silveira, Claudio C. Silveira Dep. Quim., Univ. Fed. Santa Maria, Santa Maria, Rio Grande do Sul, BrazilSearch for more papers by this authorSamuel R. Mendes, Samuel R. Mendes Dep. Quim., Univ. Fed. Santa Maria, Santa Maria, Rio Grande do Sul, BrazilSearch for more papers by this authorLucas Wolf, Lucas Wolf Dep. Quim., Univ. Fed. Santa Maria, Santa Maria, Rio Grande do Sul, BrazilSearch for more papers by this authorGuilherme M. Martins, Guilherme M. Martins Dep. Quim., Univ. Fed. Santa Maria, Santa Maria, Rio Grande do Sul, BrazilSearch for more papers by this author Claudio C. Silveira, Claudio C. Silveira Dep. Quim., Univ. Fed. Santa Maria, Santa Maria, Rio Grande do Sul, BrazilSearch for more papers by this authorSamuel R. Mendes, Samuel R. Mendes Dep. Quim., Univ. Fed. Santa Maria, Santa Maria, Rio Grande do Sul, ...</code> | <code>INSIGHTVolume , Issue p. - Special Feature Highway Infrastructure Michael E. Krueger, Michael E. Krueger Search for more papers by this author Michael E. Krueger, Michael E. Krueger Search for more papers by this author First published: June 0AboutPDF 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 Citing Literature Volume0, Issue0April 0000Pages - RelatedInformation</code> |
  | <code>Background: When determining the duration of an acute bout of physical activity (PA) in an experiment, it is important for researchers to consider associations between duration and the target outcome, as well as how amenable participants will be to enrolling, and whether they will be capable of completing the study. Researchers must strike a suitable balance when working with populations that are commonly inactive, such as people with schizophrenia. Conceptually, a participant's task self-efficacy might indicate their willingness to participate in a study and their confidence in completing a PA protocol. To inform a future protocol, this study examined the self-efficacy of individuals with schizophrenia to complete PA bouts of differing durations. Methods: A secondary analysis on reliability data from a Health Action Process Approach inventory for PA in schizophrenia (n= ) was performed. Task self-efficacy was measured using -items. Participants rated how confident they were in their p...</code> | <code>Involvement opportunities (IOs) are perceived benefits that are only present through continued sport involvement (Weiss & Amorose, ). Knowing which IOs are poignant in different segments of a population may be important in explaining participants' sport commitment, their behaviours, and purchase intentions (Casper & Stellino, ; Young, Bennett & Seguin, ). This study examined how Masters swimmers judged IOs, as a function of age group ( - , - , - , +), sex, prior participation length (< , + yrs), and probability (low, high) of attending a World championship event. Participants reported information on demographics, sport involvement, intentions, and responded to a survey (Bennett & Young, ) assessing different IOs. A series of MANOVAs identified differences according to sample segments, all ps < . All age cohorts highly recognized opportunities for 'enjoyment', 'health and fitness', 'social', 'stress relief' and 'personal testing and assessment', though the youngest group viewed the latt...</code> | <code>The article analyzes the current state with anti-monopolistic regulation with regards to the transactions of mergers and acquisitions in Russia, describes the recent changes in legislation related to it, and analyzes the major trends in state regulation over merges and acquisitions in the post-crisis period. The acquisition of TNK-BP by Rosneft' is the central example discussed in the article. The author presents recommendation towards improving the mechanisms of evaluation of the effect produced by the transaction of merges and acquisitions upon Russian economy</code>                                                                                                                                                                                                                                                                |
  | <code>In academe, there is a great bifurcation in the understanding of such things, i.e., the main thought of German ideology and its status in the history of Marxist philosophy. By exploring the historical background and the purpose of German ideology, and with the help of the direct explanation of Marx and Engels in this book, the author thinks that the main thought and basic content of German ideology is the theory on individuals. After that, the author elucidates the relation between the theory on individuals of Marxism and historical materialism, and the history of Marxist philosophy as well.</code>                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>After the Second Opium War, the government of the Qing Dynasty signed Treaty of Tianjing with America and British in succession, starting from which Shantou opened its seaport to the world, and the foreigners had enjoyed the rights to rent estate to build warehouse, church, hospital and graveyards. Being different with other cities such as Shanghai, Tianjin, and Hankou, where designed some special areas to rent, the expanding situation in Shantou is more complicated. Based on the analysis of British Public Archives Files, this paper focuses on the two recorded disputes on estate involving Chinese and foreigners in Shantou, in order to help us to gain a deep understanding of the complicate situation in the expanding progression of Chinese coastal ports, by presenting the transition of the strategies adopted by foreigners to rent or buy estate, and the reactions taken by Chinese officials during the formation of Shantou City in the end of the Qing Dynasty.</code>                                    | <code>We complete the determination of the \ell -block distribution of characters for quasi-simple exceptional groups of Lie type up to some minor ambiguities relating to non-uniqueness of Jordan decomposition. For this, we first determine the \ell -block distribution for finite reductive groups whose ambient algebraic group defined in characteristic different from \ell has connected centre. As a consequence we derive a compatibility between \ell -blocks, e -Harish-Chandra series and Jordan decomposition. Further we apply our results to complete the proof of Robinson's conjecture on defects of characters.</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": 0.3
  }
  ```

### 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: 88 tokens</li><li>mean: 241.2 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 91 tokens</li><li>mean: 242.12 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 92 tokens</li><li>mean: 244.54 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Some two component catalysts supported on inorganic oxides were prepared by wet impregnation and their catalytic performances for the direct synthesis of dimethyl carbonate from carbon dioxide, propylene oxide and methanol were studied. The influences of reaction temperature, amount of catalyst, reaction pressure and size of support on the synthetic reaction were investigated. The results showed that two component catalyst supported on ZnO had good catalytic activity and optimum reaction temperature was . The highest yield of DMC was obtained over a catalyst with % active component. The influence of reaction pressure was not obvious, and the decrease of the size of support favored the formation of DMC.</code>                                                                                                                                                                                                                                                                                                     | <code>-Dihydroxybenzoic acid was prepared by carboxylation of resorcinol in solvent under under atmospheric pressure was studied.The mechanism of Kolbe-Schmitt carboxylation was analyzed and the sutable solvent for the carboxylation reaction was selected.The optimum process parameters were determined by orthogonal test.Under the optimized parameters,i.e.,resorcinol to potassium carbonate molar ratio of ,reaction temperature of - ,reaction time of hours- hours,and dimethyl acetamide as solvent,the yield of -dihydroxybenzoic acid was up to %</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | <code>Background This paper uses a SEIR(D) model to analyse the time-varying transmission dynamics of the COVID- epidemic in Korea throughout its multiple stages of development. This multi-stage estimation of the model parameters offers a better model fit compared to the whole period analysis and shows how the COVID- 's infection patterns change over time, primarily depending on the effectiveness of the public health authority's non-pharmaceutical interventions (NPIs).Methods This paper uses the SEIR(D) compartment model to simulate and estimate the parameters for three distinctive stages of the COVID- epidemic in Korea, using a manually compiled COVID- epidemic dataset for the period between February and February . The paper identifies three major stages of the COVID- epidemic, conducts multi-stage estimations of the SEIR(D) model parameters, and carefully infers context-dependent meaning of the estimation results to help better understand the unique patterns of the transmission of the nove...</code> |
  | <code>Clinical Pharmacology & TherapeuticsVolume , Issue p. - USAN Council List No. 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 onFacebookTwitterLinked InRedditWechat Abstract Clinical Pharmacology and Therapeutics ( ) , ; doi: /clpt.0000.000 Volume00, Issue0July 0000Pages - RelatedInformation</code>                                                                                                                                                                                                                                                                                  | <code>Clinical Pharmacology & TherapeuticsVolume , Issue p. - FDA papers FDA papers II Walter Modell M.D., Walter Modell M.D.Search for more papers by this authorC. E. Healy M.D., C. E. Healy M.D. Evansville, Ind.Search for more papers by this author Walter Modell M.D., Walter Modell M.D.Search for more papers by this authorC. E. Healy M.D., C. E. Healy M.D. Evansville, Ind.Search for more papers by this author First published: March 0AboutPDF 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 Citing Literature Volume0, Issue0March 0000Pages - Relat...</code> | <code>Preeclampsia is characterized by reduced placental perfusion with placental ischemia and hypertension during pregnancy. Preeclamptic women also exhibit a heightened inflammatory state and greater number of neutrophils in the vasculature compared to normal pregnancy. Since neutrophils are associated with tissue injury and inflammation, we hypothesized that neutrophils are critical to placental ischemia-induced hypertension and fetal demise. Using the reduced uteroplacental perfusion pressure (RUPP) model of placental ischemia-induced hypertension in the rat, we determined the effect of neutrophil depletion on blood pressure and fetal resorptions. Neutrophils were depleted with repeated injections of polyclonal rabbit anti-rat polymorphonuclear leukocyte (PMN) antibody (antiPMN). Rats received either antiPMN or normal rabbit serum (Control) on , , , and days post conception (dpc). On dpc, rats underwent either Sham surgery or clip placement on ovarian arteries and abdominal aorta to redu...</code> |
  | <code>Prior to the start of the LHC Run , the US ATLAS Software and Computing operations program established three shared Tier Analysis Facilities (AFs). The newest AF was established at the University of Chicago in the past year, joining the existing AFs at Brookhaven National Lab and SLAC National Accelerator Lab. In this paper, we will describe both the common and unique aspects of these three AFs, and the resulting distributed facility from the user's perspective, including how we monitor and measure the AFs. The common elements include enabling easy access via Federated ID, file sharing via EOS, provisioning of similar Jupyter environments using common Jupyter kernels and containerization, and efforts to centralize documentation and user support channels. The unique components we will cover are driven in turn by the requirements, expertise and resources at each individual site. Finally, we will highlight how the US AFs are collaborating with other ATLAS and LHC wide (IRIS-HEP and HSF) u...</code> | <code>Network traffic optimisation is difficult as the load is by nature dynamic and seemingly unpredictable. However, the increased usage of file transfer services may help the detection of future loads and the prediction of their expected duration. The NOTED project seeks to do exactly this and to dynamically adapt network topology to deliver improved bandwidth for users of such services. This article introduces, and explains the features of, the two main components of NOTED, the Transfer Broker and the Network Intelligence component. The Transfer Broker analyses all queued and on-going FTS transfers, producing a traffic report which can be used by network controllers. Based on this report and its knowledge of the network topology and routing, the Network Intelligence (NI) component makes decisions as to when a network reconfiguration could be beneficial. Any Software Defined Network controller can then apply these decision to the network, so optimising transfer execution time and reducing...</code> | <code>Human Geophagia, a phenomenon widely practised especially in Africa, is the craving and deliberate ingestion of clayey soils. It is frequently practised by women and children to relieve hunger, supply nutritional deficiencies or as folk medicine. Geophagic individuals are very selective in the type of clayey soil they consume, where it is obtained, and its physical state; as well as its colour, smell and texture. Though clayey soils are medicinal, they could equally be risky and hazardous to human health. Reports have associated geophagia with iron deficiency leading to anaemia, infestation with geohelminths, and abrasion of the gastro-intestines. This overview brings awareness on clayey soils consumed and throws light on the human health associated effects.</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": 0.3
  }
  ```

### 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 | specter_og_cosine_accuracy | modernBERT_disciplines_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:--------------------------:|:--------------------------------------:|
| 0      | 0    | -             | -               | 0.9579                     | -                                      |
| 0.0511 | 100  | 0.1007        | 0.0612          | 0.9649                     | -                                      |
| 0.1022 | 200  | 0.0442        | 0.0423          | 0.9687                     | -                                      |
| 0.1533 | 300  | 0.0372        | 0.0342          | 0.9725                     | -                                      |
| 0.2044 | 400  | 0.0319        | 0.0274          | 0.9725                     | -                                      |
| 0.2555 | 500  | 0.0307        | 0.0282          | 0.9738                     | -                                      |
| 0.3066 | 600  | 0.0318        | 0.0268          | 0.9789                     | -                                      |
| 0.3577 | 700  | 0.0278        | 0.0251          | 0.9770                     | -                                      |
| 0.4088 | 800  | 0.0266        | 0.0282          | 0.9757                     | -                                      |
| 0.4599 | 900  | 0.0274        | 0.0252          | 0.9745                     | -                                      |
| 0.5110 | 1000 | 0.0317        | 0.0263          | 0.9770                     | -                                      |
| 0.5621 | 1100 | 0.024         | 0.0249          | 0.9770                     | -                                      |
| 0.6132 | 1200 | 0.0201        | 0.0236          | 0.9770                     | -                                      |
| 0.6643 | 1300 | 0.0202        | 0.0225          | 0.9757                     | -                                      |
| 0.7154 | 1400 | 0.0284        | 0.0228          | 0.9777                     | -                                      |
| 0.7665 | 1500 | 0.0229        | 0.0236          | 0.9777                     | -                                      |
| 0.8176 | 1600 | 0.0299        | 0.0219          | 0.9789                     | -                                      |
| 0.8687 | 1700 | 0.0315        | 0.0197          | 0.9808                     | -                                      |
| 0.9198 | 1800 | 0.0222        | 0.0193          | 0.9840                     | -                                      |
| 0.9709 | 1900 | 0.0251        | 0.0197          | 0.9821                     | -                                      |
| 1.0220 | 2000 | 0.0283        | 0.0190          | 0.9789                     | -                                      |
| 1.0731 | 2100 | 0.017         | 0.0198          | 0.9770                     | -                                      |
| 1.1242 | 2200 | 0.0154        | 0.0189          | 0.9821                     | -                                      |
| 1.1753 | 2300 | 0.0079        | 0.0192          | 0.9840                     | -                                      |
| 1.2264 | 2400 | 0.0042        | 0.0191          | 0.9834                     | -                                      |
| 1.2775 | 2500 | 0.0065        | 0.0197          | 0.9808                     | -                                      |
| 1.3286 | 2600 | 0.0066        | 0.0198          | 0.9796                     | -                                      |
| 1.3797 | 2700 | 0.0058        | 0.0196          | 0.9821                     | -                                      |
| 1.4308 | 2800 | 0.0084        | 0.0196          | 0.9828                     | -                                      |
| 1.4819 | 2900 | 0.009         | 0.0199          | 0.9847                     | -                                      |
| 1.5330 | 3000 | 0.0053        | 0.0193          | 0.9828                     | -                                      |
| 1.5841 | 3100 | 0.0075        | 0.0185          | 0.9821                     | -                                      |
| 1.6352 | 3200 | 0.0045        | 0.0188          | 0.9840                     | -                                      |
| 1.6863 | 3300 | 0.0051        | 0.0185          | 0.9821                     | -                                      |
| 1.7374 | 3400 | 0.008         | 0.0189          | 0.9821                     | -                                      |
| 1.7885 | 3500 | 0.0097        | 0.0187          | 0.9834                     | -                                      |
| 1.8396 | 3600 | 0.0083        | 0.0186          | 0.9840                     | -                                      |
| 1.8906 | 3700 | 0.007         | 0.0183          | 0.9847                     | -                                      |
| 1.9417 | 3800 | 0.0072        | 0.0180          | 0.9840                     | -                                      |
| 1.9673 | 3850 | -             | -               | -                          | 0.9847                                 |


### 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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