Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:26147930
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: '[YEAR_RANGE] 2020-2024 [TEXT] Vitamin B-6 Prevents Heart Failure
+    with Preserved Ejection Fraction Through Downstream of Kinase 3 in a Mouse Model.'
+  sentences:
+  - '[YEAR_RANGE] 2020-2024 [TEXT] Colorectal cancer (CRC) is a complex and genetically
+    heterogeneous disease presenting a specific metastatic pattern, with the liver
+    being the most common site of metastasis. Around 20%-25% of patients with CRC
+    will develop exclusively hepatic metastatic disease throughout their disease history.
+    With its specific characteristics and therapeutic options, liver-limited disease
+    (LLD) should be considered as a specific entity. The identification of these patients
+    is particularly relevant in view of the growing interest in liver transplantation
+    in selected patients with advanced CRC. Identifying why some patients will develop
+    only LLD remains a challenge, mainly because of a lack of a systemic understanding
+    of this complex and interlinked phenomenon given that cancer has traditionally
+    been investigated according to distinct physiological compartments. Recently,
+    multidisciplinary efforts and new diagnostic tools have made it possible to study
+    some of these complex issues in greater depth and may help identify targets and
+    specific treatment strategies to benefit these patients. In this review we analyze
+    the underlying biology and available tools to help clinicians better understand
+    this increasingly common and specific disease.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] PURPOSE: Secondary breast cancer is a frequent
+    late adverse event of mediastinal Hodgkin lymphoma radiotherapy. Secondary breast
+    cancers overwhelmingly correspond to ductal carcinoma and develop from the glandular
+    mammary tissue. In addition, during childhood, radiation overexposure of the glandular
+    tissue may lead to a late breast hypotrophy at adult age. The aim of this study
+    was to evaluate the radiation exposure to the glandular tissue in patients treated
+    for mediastinal Hodgkin lymphoma with intensity-modulated proton therapy, in order
+    to evaluate the potential dosimetric usefulness of its delineation for breast
+    sparing. MATERIALS AND METHODS: Sixteen consecutive intermediate-risk mediastinal
+    female patients with Hodgkin lymphoma treated with consolidation radiation with
+    deep inspiration breath hold intensity-modulated proton therapy to the total dose
+    of 30Gy were included. Breasts were delineated according to the European Society
+    for Radiotherapy and Oncology guidelines for treatment optimization ("clinical
+    organ at risk"). The glandular tissue ("glandular organ at risk") was retrospectively
+    contoured on the initial simulation CT scans based on Hounsfield unit (HU) values,
+    using a range between -80HU and 500HU. RESULTS: The mean and maximum doses delivered
+    to the glandular organ at risk were significantly lower than the mean and maximum
+    doses delivered to the clinical organ at risk, but were statistically correlated.
+    Glandular organ at risk volumes were significantly smaller. CONCLUSION: Optimizing
+    the treatment plans on the clinical breast contours will systematically lead to
+    overestimation of the dose received to the glandular tissue and, consequently,
+    to an indistinct and involuntary improved glandular tissue sparing. As such, our
+    findings do not support the consideration of the glandular tissue as an additional
+    organ at risk when planning intensity-modulated proton therapy for mediastinal
+    Hodgkin lymphoma in female patients.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] BACKGROUND: There is an urgent need to develop
+    an efficient therapeutic strategy for heart failure with preserved ejection fraction
+    (HFpEF), which is mediated by phenotypic changes in cardiac macrophages. We previously
+    reported that vitamin B-6 inhibits macrophage-mediated inflammasome activation.
+    OBJECTIVES: We sought to examine whether the prophylactic use of vitamin B-6 prevents
+    HFpEF. METHODS: HFpEF model was elicited by a combination of high-fat diet and
+    Nω-nitro-l-arginine methyl ester supplement in mice. Cardiac function was assessed
+    using conventional echocardiography and Doppler imaging. Immunohistochemistry
+    and immunoblotting were used to detect changes in the macrophage phenotype and
+    myocardial remodeling-related molecules. RESULTS: Co-administration of vitamin
+    B-6 with HFpEF mice mitigated HFpEF phenotypes, including diastolic dysfunction,
+    cardiac macrophage phenotypic shifts, fibrosis, and hypertrophy. Echocardiographic
+    improvements were observed, with the E/E'' ratio decreasing from 42.0 to 21.6
+    and the E/A ratio improving from 2.13 to 1.17. The exercise capacity also increased
+    from 295.3 to 657.7 min. However, these beneficial effects were negated in downstream
+    of kinase (DOK) 3-deficient mice. Mechanistically, vitamin B-6 increased DOK3
+    protein concentrations and inhibited macrophage phenotypic changes, which were
+    abrogated by an AMP-activated protein kinase inhibitor. CONCLUSIONS: Vitamin B-6
+    increases DOK3 signaling to lower risk of HFpEF by inhibiting phenotypic changes
+    in cardiac macrophages.'
+- source_sentence: '[YEAR_RANGE] 2020-2024 [TEXT] Resolving phylogenetic relationships
+    and taxonomic revision in the Pseudogastromyzon (Cypriniformes, Gastromyzonidae)
+    genus: molecular and morphological evidence for a new genus, Labigastromyzon.'
+  sentences:
+  - '[YEAR_RANGE] 2020-2024 [TEXT] Bats contain a diverse spectrum of viral species
+    in their bodies. The RNA virus family Paramyxoviridae tends to infect several
+    vertebrate species, which are accountable for a variety of devastating infections
+    in both humans and animals. Viruses of this kind include measles, mumps, and Hendra.
+    Some synonymous codons are favoured over others in mRNAs during gene-to-protein
+    synthesis process. Such phenomenon is termed as codon usage bias (CUB). Our research
+    emphasized many aspects that shape the CUB of genes in the Paramyxoviridae family
+    found in bats. Here, the nitrogenous base A occurred the most. AT was found to
+    be abundant in the coding sequences of the Paramyxoviridae family. RSCU data revealed
+    that A or T ending codons occurred more frequently than predicted. Furthermore,
+    3 overrepresented codons (CAT, AGA, and GCA) and 7 underrepresented codons (CCG,
+    TCG, CGC, CGG, CGT, GCG and ACG) were detected in the viral genomes. Correspondence
+    analysis, neutrality plot, and parity plots highlight the combined impact of mutational
+    pressure and natural selection on CUB. The neutrality plot of GC12 against GC3
+    yielded a regression coefficient value of 0.366, indicating that natural selection
+    had a significant (63.4 %) impact. Moreover, RNA editing analysis was done, which
+    revealed the highest frequency of C to T mutations. The results of our research
+    revealed the pattern of codon usage and RNA editing sites in Paramyxoviridae genomes.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] OBJECTIVE: The preoperative inclination angle of
+    mandibular incisors was crucial for surgical and postoperative stability while
+    the effect of proclined mandibular incisors on skeletal stability has not been
+    investigated. This study aimed to evaluate the effects of differences in presurgical
+    mandibular incisor inclination on skeletal stability after orthognathic surgery
+    in patients with skeletal Class III malocclusion. METHODS: A retrospective cohort
+    study of 80 consecutive patients with skeletal Class III malocclusion who underwent
+    bimaxillary orthognathic surgery was conducted. According to incisor mandibular
+    plane angle (IMPA), patients were divided into 3 groups: retroclined inclination
+    (IMPA < 87°), normal inclination (87° ≤ IMPA < 93°) and proclined inclination
+    (IMPA ≥ 93°). Preoperative characteristics, surgical changes and postoperative
+    stability were compared based on lateral cephalograms obtained 1 week before surgery
+    (T0), 1 week after surgery (T1), and at 6 to 12 months postoperatively (T2). RESULTS:
+    The mandible demonstrated a forward and upward relapse in all three groups. No
+    significant differences in skeletal relapse were observed in the 3 groups of patients.
+    However, the proclined inclination group showed a negative overbite tendency postoperatively
+    compared with the other two groups and a clinically significant mandibular relapse
+    pattern. Proclined IMPA both pre- and postoperatively was correlated with mandibular
+    relapse. CONCLUSION: Sufficient presurgical mandibular incisor decompensation
+    was of crucial importance for the maintenance of skeletal stability in patients
+    with skeletal Class III malocclusion who subsequently underwent orthognathic surgery.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] The Pseudogastromyzon genus, consisting of species
+    predominantly distributed throughout southeastern China, has garnered increasing
+    market attention in recent years due to its ornamental appeal. However, the overlapping
+    diagnostic attributes render the commonly accepted criteria for interspecific
+    identification unreliable, leaving the phylogenetic relationships among Pseudogastromyzon
+    species unexplored. In the present study, we undertake molecular phylogenetic
+    and morphological examinations of the Pseudogastromyzon genus. Our phylogenetic
+    analysis of mitochondrial genes distinctly segregated Pseudogastromyzon species
+    into two clades: the Pseudogastromyzon clade and the Labigastromyzon clade. A
+    subsequent morphological assessment revealed that the primary dermal ridge (specifically,
+    the second ridge) within the labial adhesive apparatus serves as an effective
+    and precise interspecific diagnostic characteristic. Moreover, the distributional
+    ranges of Pseudogastromyzon and Labigastromyzon are markedly distinct, exhibiting
+    only a narrow area of overlap. Considering the morphological heterogeneity of
+    the labial adhesive apparatus and the substantial division within the molecular
+    phylogeny, we advocate for the elevation of the Labigastromyzon subgenus to the
+    status of a separate genus. Consequently, we have ascertained the validity of
+    the Pseudogastromyzon and Labigastromyzon species, yielding a total of six valid
+    species. To facilitate future research, we present comprehensive descriptions
+    of the redefined species and introduce novel identification keys.'
+- source_sentence: '[YEAR_RANGE] 2020-2024 [TEXT] PCa-RadHop: A transparent and lightweight
+    feed-forward method for clinically significant prostate cancer segmentation.'
+  sentences:
+  - '[YEAR_RANGE] 2020-2024 [TEXT] According to the importance of time in treatment
+    of thrombosis disorders, faster than current treatments are required. For the
+    first time, this research discloses a novel strategy for rapid dissolution of
+    blood clots by encapsulation of a fibrinolytic (Reteplase) into a Thrombin sensitive
+    shell formed by polymerization of acrylamide monomers and bisacryloylated peptide
+    as crosslinker. Degradability of the peptide units in exposure to Thrombin, creates
+    the Thrombin-sensitive Reteplase nanocapsules (TSRNPs) as a triggered release
+    system. Accelerated thrombolysis was achieved by combining three approaches including:
+    deep penetration of TSRNPs into the blood clots, changing the clot dissolution
+    mechanism by altering the distribution pattern of TSRNPs to 3D intra-clot distribution
+    (based on the distributed intra-clot thrombolysis (DIT) model) instead of peripheral
+    and unidirectional distribution of unencapsulated fibrinolytics and, enzyme-stimulated
+    release of the fibrinolytic. Ex-vivo study was carried out by an occluded tube
+    model that mimics in-vivo brain stroke as an emergency situation where faster
+    treatment in short time is a golden key. In in vivo, efficacy of the developed
+    formulation was confirmed by PET scan and laser Doppler flowmetry (LDF). As the
+    most important achievements, 40.0 ± 0.7 (n = 3) % and 37.0 ± 0.4 (n = 3) % reduction
+    in the thrombolysis time (faster reperfusion) were observed by ex-vivo and in-vivo
+    experiments, respectively. Higher blood flow and larger digestion mass of clot
+    at similar times in comparison to non-encapsulated Reteplase were observed that
+    means more effective thrombolysis by the developed strategy.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] Prostate Cancer is one of the most frequently occurring
+    cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading
+    has a high false positive rate, thus increasing the diagnostic incurred costs
+    and patient discomfort. Deep learning (DL) models achieve a high segmentation
+    performance, although require a large model size and complexity. Also, DL models
+    lack of feature interpretability and are perceived as "black-boxes" in the medical
+    field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more
+    transparent feature extraction process using a linear model. It adopts the recently
+    introduced Green Learning (GL) paradigm, which offers a small model size and low
+    complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics
+    features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and
+    predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2
+    is introduced to refine the predictions by including more contextual information
+    and radiomics features from each already detected Region of Interest (ROI). Experiments
+    on the largest publicly available dataset, PI-CAI, show a competitive performance
+    standing of the proposed method among other deep DL models, achieving an area
+    under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop
+    maintains orders of magnitude smaller model size and complexity.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] OBJECTIVE: To evaluate rates of remission, recovery,
+    relapse, and recurrence in suicidal youth who participated in a clinical trial
+    comparing Dialectical Behavior Therapy (DBT) and Individual and Group Supportive
+    Therapy (IGST). METHOD: Participants were 173 youth, aged 12 to 18 years, with
+    repetitive self-harm (including at least 1 prior suicide attempt [SA]) and elevated
+    suicidal ideation (SI). Participants received 6 months of DBT or IGST and were
+    followed for 6 months post-treatment. The sample was 95% female, 56.4% White,
+    and 27.49% Latina. Remission was defined as absence of SA or nonsuicidal self-injury
+    (NSSI) across one 3-month interval; recovery was defined across 2 or more consecutive
+    intervals. Relapse and recurrence were defined as SA or NSSI following remission
+    or recovery. Cross-tabulation with χ2 was used for between-group contrasts. RESULTS:
+    Over 70% of the sample reported remission of SA at each treatment and follow-up
+    interval. There were significantly higher rates of remission and recovery and
+    lower rates of relapse and recurrence for SA in DBT than for IGST. Across treatments
+    and time points, SA had higher remission and recovery rates and lower relapse
+    and recurrence rates than NSSI. There were no significant differences in NSSI
+    remission between conditions; however, participants receiving DBT had significantly
+    higher NSSI recovery rates than those receiving IGST for the 3- to 9-month, 3-
+    to 12-month, and 6- to 12-month intervals. CONCLUSION: Results showed higher percentages
+    of SA remission and recovery for DBT as compared to IGST. NSSI was less likely
+    to remit than SA. PLAIN LANGUAGE SUMMARY: This study examined rates of remission,
+    recovery, relapse, and recurrence of suicide attempts (SA) and nonsuicidal self-injury
+    (NSSI) among the participants in the CARES Study, a randomized clinical trial
+    of 6 months of Dialectical Behavior Therapy or Individual and Group Supportive
+    Therapy. 173 youth aged 12 to 18 years participated in the study and were followed
+    for 6 months post treatment. Over 70% of the sample reported remission of SA at
+    each treatment and follow-up interval. There were significantly higher rates of
+    remission and recovery and lower rates of relapse and recurrence for SA among
+    participants who received Dialectical Behavioral Therapy. Across both treatments,
+    remission and recovery rates were lower and relapse and recurrence rates were
+    higher for NSSI than for SA. These results underscore the value of Dialectical
+    Behavioral Therapy as a first line treatment for youth at high risk for suicide.
+    DIVERSITY & INCLUSION STATEMENT: We worked to ensure race, ethnic, and/or other
+    types of diversity in the recruitment of human participants. CLINICAL TRIAL REGISTRATION
+    INFORMATION: Collaborative Adolescent Research on Emotions and Suicide (CARES);
+    https://www. CLINICALTRIALS: gov/; NCT01528020.'
+- source_sentence: '[YEAR_RANGE] 2020-2024 [TEXT] Predicting Recovery After Concussion
+    in Pediatric Patients: A Meta-Analysis.'
+  sentences:
+  - '[YEAR_RANGE] 2020-2024 [TEXT] OBJECTIVE: The authors examined licensing requirements
+    for select children''s behavioral health care providers. METHODS: Statutes and
+    regulations as of October 2021 were reviewed for licensed clinical social workers,
+    licensed professional counselors, and licensed marriage and family therapists
+    for all 50 U.S. states and the District of Columbia. RESULTS: All jurisdictions
+    had laws regarding postgraduate training and license portability. No jurisdiction
+    included language about specialized postgraduate training related to serving children
+    and families or cultural competence. Other policies that related to the structure,
+    composition, and authority of licensing boards varied across states and licensure
+    types. CONCLUSIONS: In their efforts to address barriers to licensure, expand
+    the workforce, and ensure that children have access to high-quality and culturally
+    responsive care, states could consider their statutes and regulations.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] Magnetic Resonance Imaging (MRI) plays a pivotal
+    role in the accurate measurement of brain subcortical structures in macaques,
+    which is crucial for unraveling the complexities of brain structure and function,
+    thereby enhancing our understanding of neurodegenerative diseases and brain development.
+    However, due to significant differences in brain size, structure, and imaging
+    characteristics between humans and macaques, computational tools developed for
+    human neuroimaging studies often encounter obstacles when applied to macaques.
+    In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which
+    integrates multimodal MRI data with anatomical constraints in a multi-scale framework
+    to address the challenges posed by the dynamic development, regional heterogeneity,
+    and age-related size variations of the juvenile macaque brain, thus achieving
+    precise subcortical segmentation. Specifically, we generate a Signed Distance
+    Map (SDM) based on the initial rough segmentation of the subcortical region by
+    a network as an anatomical constraint, providing comprehensive information on
+    positions, structures, and morphology. Then we construct AAF-Net to fully fuse
+    the SDM anatomical constraints and multimodal images for refined segmentation.
+    To thoroughly evaluate the performance of our proposed tool, over 700 macaque
+    MRIs from 19 datasets were used in this study. Specifically, we employed two manually
+    labeled longitudinal macaque datasets to develop the tool and complete four-fold
+    cross-validations. Furthermore, we incorporated various external datasets to demonstrate
+    the proposed tool''s generalization capabilities and promise in brain development
+    research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation
+    for direct application.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] CONTEXT: Prognostic prediction models (PPMs) can
+    help clinicians predict outcomes. OBJECTIVE: To critically examine peer-reviewed
+    PPMs predicting delayed recovery among pediatric patients with concussion. DATA
+    SOURCES: Ovid Medline, Embase, Ovid PsycInfo, Web of Science Core Collection,
+    Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Google
+    Scholar. STUDY SELECTION: The study had to report a PPM for pediatric patients
+    to be used within 28 days of injury to estimate risk of delayed recovery at 28
+    days to 1 year postinjury. Studies had to have at least 30 participants. DATA
+    EXTRACTION: The Critical Appraisal and Data Extraction for Systematic Reviews
+    of Prediction Modeling Studies checklist was completed. RESULTS: Six studies of
+    13 PPMs were included. These studies primarily reflected male patients in late
+    childhood or early adolescence presenting to an emergency department meeting the
+    Concussion in Sport Group concussion criteria. No study authors used the same
+    outcome definition nor evaluated the clinical utility of a model. All studies
+    demonstrated high risk of bias. Quality of evidence was best for the Predicting
+    and Preventing Postconcussive Problems in Pediatrics (5P) clinical risk score.
+    LIMITATIONS: No formal PPM Grading of Recommendations, Assessment, Development,
+    and Evaluations (GRADE) process exists. CONCLUSIONS: The 5P clinical risk score
+    may be considered for clinical use. Rigorous external validations, particularly
+    in other settings, are needed. The remaining PPMs require external validation.
+    Lack of consensus regarding delayed recovery criteria limits these PPMs.'
+- source_sentence: '[YEAR_RANGE] 2020-2024 [TEXT] Intraoperative Monitoring of the
+    External Urethral Sphincter Reflex: A Novel Adjunct to Bulbocavernosus Reflex
+    Neuromonitoring for Protecting the Sacral Neural Pathways Responsible for Urination,
+    Defecation and Sexual Function.'
+  sentences:
+  - '[YEAR_RANGE] 2020-2024 [TEXT] Early menarche has been associated with adverse
+    health outcomes, such as depressive symptoms. Discovering effect modifiers across
+    these conditions in the pediatric population is a constant challenge. We tested
+    whether movement behaviours modified the effect of the association between early
+    menarche and depression symptoms among adolescents. This cross-sectional study
+    included 2031 females aged 15-19 years across all Brazilian geographic regions.
+    Data were collected using a self-administered questionnaire; 30.5% (n = 620) reported
+    having experienced menarche before age 12 years (that is, early menarche). We
+    used the Patient Health Questionnaire (PHQ-9) to evaluate depressive symptoms.
+    Accruing any moderate-vigorous physical activity during leisure time, limited
+    recreational screen time, and having good sleep quality were the exposures investigated.
+    Adolescents who experienced early menarche and met one (B: -4.45, 95% CI: (-5.38,
+    -3.51)), two (B: -6.07 (-7.02, -5.12)), or three (B: -6.49 (-7.76, -5.21)), and
+    adolescents who experienced not early menarche and met one (B: -5.33 (-6.20; -4.46)),
+    two (B: -6.12 (-6.99; -5.24)), or three (B: -6.27 (-7.30; -5.24)) of the movement
+    behaviour targets had lower PHQ-9 scores for depression symptoms than adolescents
+    who experienced early menarche and did not meet any of the movement behaviours.
+    The disparities in depressive symptoms among the adolescents (early menarche versus
+    not early menarche) who adhered to all three target behaviours were not statistically
+    significant (B: 0.41 (-0.19; 1.01)). Adherence to movement behaviours modified
+    the effect of the association between early menarche and depression symptoms.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] PURPOSE: Intraoperative bulbocavernosus reflex
+    neuromonitoring has been utilized to protect bowel, bladder, and sexual function,
+    providing a continuous functional assessment of the somatic sacral nervous system
+    during surgeries where it is at risk. Bulbocavernosus reflex data may also provide
+    additional functional insight, including an evaluation for spinal shock, distinguishing
+    upper versus lower motor neuron injury (conus versus cauda syndromes) and prognosis
+    for postoperative bowel and bladder function. Continuous intraoperative bulbocavernosus
+    reflex monitoring has been utilized to provide the surgeon with an ongoing functional
+    assessment of the anatomical elements involved in the S2-S4 mediated reflex arc
+    including the conus, cauda equina and pudendal nerves. Intraoperative bulbocavernosus
+    reflex monitoring typically includes the electrical activation of the dorsal nerves
+    of the genitals to initiate the afferent component of the reflex, followed by
+    recording the resulting muscle response using needle electromyography recordings
+    from the external anal sphincter. METHODS: Herein we describe a complementary
+    and novel technique that includes recording electromyography responses from the
+    external urethral sphincter to monitor the external urethral sphincter reflex.
+    Specialized foley catheters embedded with recording electrodes have recently become
+    commercially available that provide the ability to perform intraoperative external
+    urethral sphincter muscle recordings. RESULTS: We describe technical details and
+    the potential utility of incorporating external urethral sphincter reflex recordings
+    into existing sacral neuromonitoring paradigms to provide redundant yet complementary
+    data streams. CONCLUSIONS: We present two illustrative neurosurgical oncology
+    cases to demonstrate the utility of the external urethral sphincter reflex technique
+    in the setting of the necessary surgical sacrifice of sacral nerve roots.'
+  - '[YEAR_RANGE] 2020-2024 [TEXT] BACKGROUND: Limited data are available on the appropriate
+    choice of blood pressure management strategy for patients with acute basilar artery
+    occlusion assessed by the standard deviation (SD). Multivariate logistic models
+    were used to investigate the association between BPV, the primary outcome (futile
+    recanalization, 90-day modified Rankin Scale score 3-6), and the secondary outcome
+    (30-day mortality). Subgroup analysis was performed as a sensitivity test. RESULTS:
+    Futile recanalization occurred in 60 (56 %) patients, while 26 (24 %) patients
+    died within 30 days. In the fully adjusted model, MAP SD was associated with a
+    higher risk of futile recanalization (OR adj=1.36, per 1 mmHg increase, 95 % CI:
+    1.09-1.69, P=0.006) and 30-day mortality (OR adj=1.56, per 1 mmHg increase, 95
+    % CI: 1.20-2.04, P=0.001). A significant interaction between MAP SD and the lack
+    of hypertension history on futile recanalization (P<0.05) was observed. CONCLUSIONS:
+    Among recanalized acute BAO ischemic patients, higher blood pressure variability
+    during the first 24 h after MT was associated with worse outcomes. This association
+    was stronger in patients without a history of hypertension.'
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained on the parquet dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - parquet
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("pankajrajdeo/UMLS-Pubmed-TCE-Epoch-2")
+# Run inference
+sentences = [
+    '[YEAR_RANGE] 2020-2024 [TEXT] Intraoperative Monitoring of the External Urethral Sphincter Reflex: A Novel Adjunct to Bulbocavernosus Reflex Neuromonitoring for Protecting the Sacral Neural Pathways Responsible for Urination, Defecation and Sexual Function.',
+    '[YEAR_RANGE] 2020-2024 [TEXT] PURPOSE: Intraoperative bulbocavernosus reflex neuromonitoring has been utilized to protect bowel, bladder, and sexual function, providing a continuous functional assessment of the somatic sacral nervous system during surgeries where it is at risk. Bulbocavernosus reflex data may also provide additional functional insight, including an evaluation for spinal shock, distinguishing upper versus lower motor neuron injury (conus versus cauda syndromes) and prognosis for postoperative bowel and bladder function. Continuous intraoperative bulbocavernosus reflex monitoring has been utilized to provide the surgeon with an ongoing functional assessment of the anatomical elements involved in the S2-S4 mediated reflex arc including the conus, cauda equina and pudendal nerves. Intraoperative bulbocavernosus reflex monitoring typically includes the electrical activation of the dorsal nerves of the genitals to initiate the afferent component of the reflex, followed by recording the resulting muscle response using needle electromyography recordings from the external anal sphincter. METHODS: Herein we describe a complementary and novel technique that includes recording electromyography responses from the external urethral sphincter to monitor the external urethral sphincter reflex. Specialized foley catheters embedded with recording electrodes have recently become commercially available that provide the ability to perform intraoperative external urethral sphincter muscle recordings. RESULTS: We describe technical details and the potential utility of incorporating external urethral sphincter reflex recordings into existing sacral neuromonitoring paradigms to provide redundant yet complementary data streams. CONCLUSIONS: We present two illustrative neurosurgical oncology cases to demonstrate the utility of the external urethral sphincter reflex technique in the setting of the necessary surgical sacrifice of sacral nerve roots.',
+    '[YEAR_RANGE] 2020-2024 [TEXT] Early menarche has been associated with adverse health outcomes, such as depressive symptoms. Discovering effect modifiers across these conditions in the pediatric population is a constant challenge. We tested whether movement behaviours modified the effect of the association between early menarche and depression symptoms among adolescents. This cross-sectional study included 2031 females aged 15-19 years across all Brazilian geographic regions. Data were collected using a self-administered questionnaire; 30.5% (n = 620) reported having experienced menarche before age 12 years (that is, early menarche). We used the Patient Health Questionnaire (PHQ-9) to evaluate depressive symptoms. Accruing any moderate-vigorous physical activity during leisure time, limited recreational screen time, and having good sleep quality were the exposures investigated. Adolescents who experienced early menarche and met one (B: -4.45, 95% CI: (-5.38, -3.51)), two (B: -6.07 (-7.02, -5.12)), or three (B: -6.49 (-7.76, -5.21)), and adolescents who experienced not early menarche and met one (B: -5.33 (-6.20; -4.46)), two (B: -6.12 (-6.99; -5.24)), or three (B: -6.27 (-7.30; -5.24)) of the movement behaviour targets had lower PHQ-9 scores for depression symptoms than adolescents who experienced early menarche and did not meet any of the movement behaviours. The disparities in depressive symptoms among the adolescents (early menarche versus not early menarche) who adhered to all three target behaviours were not statistically significant (B: 0.41 (-0.19; 1.01)). Adherence to movement behaviours modified the effect of the association between early menarche and depression symptoms.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### parquet
+
+* Dataset: parquet
+* Size: 26,147,930 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                              | positive                                                                              |
+  |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                                |
+  | details | <ul><li>min: 28 tokens</li><li>mean: 57.85 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 319.08 tokens</li><li>max: 1024 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                       | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:---------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>[YEAR_RANGE] 1880-1884 [TEXT] ADDRESS OF COL. GARRICK MALLERY, U. S. ARMY.</code>      | <code>[YEAR_RANGE] 1880-1884 [TEXT] It may be conceded that after man had all his present faculties, he did not choose between the adoption of voice and gesture, and never with those faculties, was in a state where the one was used, to the absolute exclusion of the other. The epoch, however, to which our speculations relate is that in which he had not reached the present symmetric development of his intellect and of his bodily organs, and the inquiry is: Which mode of communication was earliest adopted to his single wants and informed intelligence? With the voice he could imitate distinictively but few sounds of nature, while with gesture he could exhibit actions, motions, positions, forms, dimensions, directions and distances, with their derivations and analogues. It would seem from this unequal division of capacity that oral speech remained rudimentary long after gesture had become an efficient mode of communication. With due allowance for all purely imitative sounds, and for the spontaneous action of vocal organs under excitement, it appears that the connection between ideas and words is only to be explained by a compact between speaker and hearer which supposes the existence of a prior mode of communication. This was probably by gesture. At least we may accept it as a clew leading out of the labyrinth of philological confusion, and regulating the immemorial quest of man's primitive speech.</code> |
+  | <code>[YEAR_RANGE] 1880-1884 [TEXT] How TO OBTAIN THE BRAIN OF THE CAT.</code>               | <code>[YEAR_RANGE] 1880-1884 [TEXT] How to obtain the Brain of the Cat, (Wilder).-Correction: Page 158, second column, line 7, "grains," should be "grams;" page 159, near middle of 2nd column, "successily," should be "successively;" page 161, the number of Flower's paper is 3.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  | <code>[YEAR_RANGE] 1880-1884 [TEXT] DOLBEAR ON THE NATURE AND CONSTITUTION OF MATTER.</code> | <code>[YEAR_RANGE] 1880-1884 [TEXT] Mr. Dopp desires to make the following correction in his paper in the last issue: "In my article on page 200 of "Science", the expression and should have been and being the velocity of light.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### parquet
+
+* Dataset: parquet
+* Size: 26,147,930 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                             |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 27 tokens</li><li>mean: 43.78 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 315.03 tokens</li><li>max: 847 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                    | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>[YEAR_RANGE] 2020-2024 [TEXT] Solubility and thermodynamics of mesalazine in aqueous mixtures of poly ethylene glycol 200/600 at 293.2-313.2K.</code>               | <code>[YEAR_RANGE] 2020-2024 [TEXT] In this study, the solubility of mesalazine was investigated in binary solvent mixtures of poly ethylene glycols 200/600 and water at temperatures ranging from 293.2K to 313.2K. The solubility of mesalazine was determined using a shake-flask method, and its concentrations were measured using a UV-Vis spectrophotometer. The obtained solubility data were analyzed using mathematical models including the van't Hoff, Jouyban-Acree, Jouyban-Acree-van't Hoff, mixture response surface, and modified Wilson models. The experimental data obtained for mesalazine dissolution encompassed various thermodynamic properties, including ΔG°, ΔH°, ΔS°, and TΔS°. These properties offer valuable insights into the energetic aspects of the dissolution process and were calculated based on the van't Hoff equation.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  | <code>[YEAR_RANGE] 2020-2024 [TEXT] Safety and efficacy of remimazolam versus propofol during EUS: a multicenter randomized controlled study.</code>                      | <code>[YEAR_RANGE] 2020-2024 [TEXT] BACKGROUND AND AIMS: Propofol, a widely used sedative in GI endoscopic procedures, is associated with cardiorespiratory suppression. Remimazolam is a novel ultrashort-acting benzodiazepine sedative with rapid onset and minimal cardiorespiratory depression. This study compared the safety and efficacy of remimazolam and propofol during EUS procedures. METHODS: A multicenter randomized controlled study was conducted between October 2022 and March 2023 in patients who underwent EUS procedures. Patients were randomly assigned to receive either remimazolam or propofol as a sedative agent. The primary endpoint was cardiorespiratory adverse events.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+  | <code>[YEAR_RANGE] 2020-2024 [TEXT] Ultrasound-Guided Vs Non-Guided Prolotherapy for Internal Derangement of Temporomandibular Joint. A Randomized Clinical Trial.</code> | <code>[YEAR_RANGE] 2020-2024 [TEXT] OBJECTIVES: This randomized clinical trial study aims to compare ultrasound-guided versus non-guided Dextrose 10% injections in patients suffering from internal derangement in the temporomandibular joint (TMJ). MATERIAL AND METHODS: The study population included 22 patients and 43 TMJs suffering from unilateral or bilateral TMJ painful clicking, magnetic resonance imaging (MRI) proved disc displacement with reduction (DDWR), refractory to or failed conservative treatment. The patients were divided randomly into two groups (non-guided and ultrasound (US)-guided groups). The procedure involved injection of 2 mL solution of a mixture of 0.75 mL 0.9% normal saline solution, 0.3 mL 2% lidocaine and 0.75 mL dextrose 10% using a 25G needle in the joint and 1 mL intramuscular injection to the masseter muscle at the most tender point. The Visual Analogue Score (VAS) was used to compare joint pain intensity over four different periods, beginning with pre-injection, 1-, 2-, and 6-months postinjection. RESULTS: Twenty-two patients 5 males (n = 5/22, 22.7%) and 17 females (n = 17/22, 77.2%) were included in this study. The mean age was 27.3 ± 7.4 years (30.2 ± 7.0) for the non-guided group and 24.3 ± 6.9 for the US-guided group. The dextrose injection reduced intensity over time in both groups with statistically significant improvement (P value <.05) at 2 and 6 months in both groups. There was no statistically significant difference in VAS assessment between both groups. CONCLUSION: Intra-articular injection of dextrose 10% for patients with painful clicking and DDWR resulted in reduced pain intensity in both US-guided and non-guided groups with significant symptomatic improvement over time in both groups. US guidance allowed accurate anatomical localization and safe procedure with a single joint puncture.</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `max_steps`: 970330
+- `log_level`: info
+- `fp16`: True
+- `dataloader_num_workers`: 16
+- `load_best_model_at_end`: True
+- `resume_from_checkpoint`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: 970330
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: info
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 16
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: True
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss | Validation Loss |
+|:------:|:------:|:-------------:|:---------------:|
+| 0.0000 | 1      | 5.9163        | -               |
+| 0.0052 | 1000   | 0.6371        | -               |
+| 0.0103 | 2000   | 0.1741        | -               |
+| 0.0155 | 3000   | 0.1606        | -               |
+| 0.0206 | 4000   | 0.1496        | -               |
+| 0.0258 | 5000   | 0.111         | -               |
+| 0.0309 | 6000   | 0.1198        | -               |
+| 0.0361 | 7000   | 0.1047        | -               |
+| 0.0412 | 8000   | 0.1002        | -               |
+| 0.0464 | 9000   | 0.0991        | -               |
+| 0.0515 | 10000  | 0.1142        | -               |
+| 0.0567 | 11000  | 0.1027        | -               |
+| 0.0618 | 12000  | 0.0752        | -               |
+| 0.0670 | 13000  | 0.1036        | -               |
+| 0.0721 | 14000  | 0.1114        | -               |
+| 0.0773 | 15000  | 0.0701        | -               |
+| 0.0824 | 16000  | 0.1623        | -               |
+| 0.0876 | 17000  | 0.0727        | -               |
+| 0.0928 | 18000  | 0.1121        | -               |
+| 0.0979 | 19000  | 0.0684        | -               |
+| 0.1031 | 20000  | 0.1327        | -               |
+| 0.1082 | 21000  | 0.0755        | -               |
+| 0.1134 | 22000  | 0.1293        | -               |
+| 0.1185 | 23000  | 0.0661        | -               |
+| 0.1237 | 24000  | 0.0797        | -               |
+| 0.1288 | 25000  | 0.063         | -               |
+| 0.1340 | 26000  | 0.1324        | -               |
+| 0.1391 | 27000  | 0.0708        | -               |
+| 0.1443 | 28000  | 0.1386        | -               |
+| 0.1494 | 29000  | 0.0691        | -               |
+| 0.1546 | 30000  | 0.143         | -               |
+| 0.1597 | 31000  | 0.0644        | -               |
+| 0.1649 | 32000  | 0.1065        | -               |
+| 0.1700 | 33000  | 0.1089        | -               |
+| 0.1752 | 34000  | 0.0737        | -               |
+| 0.1804 | 35000  | 0.1431        | -               |
+| 0.1855 | 36000  | 0.069         | -               |
+| 0.1907 | 37000  | 0.0756        | -               |
+| 0.1958 | 38000  | 0.1252        | -               |
+| 0.2010 | 39000  | 0.0768        | -               |
+| 0.2061 | 40000  | 0.1255        | -               |
+| 0.2113 | 41000  | 0.0849        | -               |
+| 0.2164 | 42000  | 0.072         | -               |
+| 0.2216 | 43000  | 0.1171        | -               |
+| 0.2267 | 44000  | 0.072         | -               |
+| 0.2319 | 45000  | 0.0818        | -               |
+| 0.2370 | 46000  | 0.0988        | -               |
+| 0.2422 | 47000  | 0.066         | -               |
+| 0.2473 | 48000  | 0.0829        | -               |
+| 0.2525 | 49000  | 0.0907        | -               |
+| 0.2576 | 50000  | 0.075         | -               |
+| 0.2628 | 51000  | 0.0653        | -               |
+| 0.2679 | 52000  | 0.0667        | -               |
+| 0.2731 | 53000  | 0.0906        | -               |
+| 0.2783 | 54000  | 0.0803        | -               |
+| 0.2834 | 55000  | 0.0568        | -               |
+| 0.2886 | 56000  | 0.0665        | -               |
+| 0.2937 | 57000  | 0.0629        | -               |
+| 0.2989 | 58000  | 0.0665        | -               |
+| 0.3040 | 59000  | 0.0601        | -               |
+| 0.3092 | 60000  | 0.0761        | -               |
+| 0.3143 | 61000  | 0.0472        | -               |
+| 0.3195 | 62000  | 0.0527        | -               |
+| 0.3246 | 63000  | 0.0585        | -               |
+| 0.3298 | 64000  | 0.0699        | -               |
+| 0.3349 | 65000  | 0.0687        | -               |
+| 0.3401 | 66000  | 0.0541        | -               |
+| 0.3452 | 67000  | 0.0413        | -               |
+| 0.3504 | 68000  | 0.055         | -               |
+| 0.3555 | 69000  | 0.0706        | -               |
+| 0.3607 | 70000  | 0.0805        | -               |
+| 0.3659 | 71000  | 0.0884        | -               |
+| 0.3710 | 72000  | 0.0359        | -               |
+| 0.3762 | 73000  | 0.0443        | -               |
+| 0.3813 | 74000  | 0.0543        | -               |
+| 0.3865 | 75000  | 0.045         | -               |
+| 0.3916 | 76000  | 0.1031        | -               |
+| 0.3968 | 77000  | 0.0535        | -               |
+| 0.4019 | 78000  | 0.0661        | -               |
+| 0.4071 | 79000  | 0.0353        | -               |
+| 0.4122 | 80000  | 0.06          | -               |
+| 0.4174 | 81000  | 0.0743        | -               |
+| 0.4225 | 82000  | 0.0439        | -               |
+| 0.4277 | 83000  | 0.0484        | -               |
+| 0.4328 | 84000  | 0.0643        | -               |
+| 0.4380 | 85000  | 0.0308        | -               |
+| 0.4431 | 86000  | 0.0412        | -               |
+| 0.4483 | 87000  | 0.0464        | -               |
+| 0.4535 | 88000  | 0.0713        | -               |
+| 0.4586 | 89000  | 0.0575        | -               |
+| 0.4638 | 90000  | 0.0461        | -               |
+| 0.4689 | 91000  | 0.0358        | -               |
+| 0.4741 | 92000  | 0.0413        | -               |
+| 0.4792 | 93000  | 0.0481        | -               |
+| 0.4844 | 94000  | 0.0473        | -               |
+| 0.4895 | 95000  | 0.082         | -               |
+| 0.4947 | 96000  | 0.0313        | -               |
+| 0.4998 | 97000  | 0.0365        | -               |
+| 0.5050 | 98000  | 0.0338        | -               |
+| 0.5101 | 99000  | 0.0364        | -               |
+| 0.5153 | 100000 | 0.0288        | -               |
+| 0.5204 | 101000 | 0.0421        | -               |
+| 0.5256 | 102000 | 0.0444        | -               |
+| 0.5307 | 103000 | 0.0242        | -               |
+| 0.5359 | 104000 | 0.0318        | -               |
+| 0.5411 | 105000 | 0.0285        | -               |
+| 0.5462 | 106000 | 0.0231        | -               |
+| 0.5514 | 107000 | 0.0534        | -               |
+| 0.5565 | 108000 | 0.0469        | -               |
+| 0.5617 | 109000 | 0.031         | -               |
+| 0.5668 | 110000 | 0.0376        | -               |
+| 0.5720 | 111000 | 0.0403        | -               |
+| 0.5771 | 112000 | 0.0408        | -               |
+| 0.5823 | 113000 | 0.0284        | -               |
+| 0.5874 | 114000 | 0.0344        | -               |
+| 0.5926 | 115000 | 0.0469        | -               |
+| 0.5977 | 116000 | 0.0304        | -               |
+| 0.6029 | 117000 | 0.0676        | -               |
+| 0.6080 | 118000 | 0.0396        | -               |
+| 0.6132 | 119000 | 0.0337        | -               |
+| 0.6183 | 120000 | 0.039         | -               |
+| 0.6235 | 121000 | 0.0286        | -               |
+| 0.6286 | 122000 | 0.0404        | -               |
+| 0.6338 | 123000 | 0.0383        | -               |
+| 0.6390 | 124000 | 0.046         | -               |
+| 0.6441 | 125000 | 0.0403        | -               |
+| 0.6493 | 126000 | 0.0502        | -               |
+| 0.6544 | 127000 | 0.0424        | -               |
+| 0.6596 | 128000 | 0.0424        | -               |
+| 0.6647 | 129000 | 0.0338        | -               |
+| 0.6699 | 130000 | 0.0262        | -               |
+| 0.6750 | 131000 | 0.0203        | -               |
+| 0.6802 | 132000 | 0.0405        | -               |
+| 0.6853 | 133000 | 0.0374        | -               |
+| 0.6905 | 134000 | 0.0329        | -               |
+| 0.6956 | 135000 | 0.0287        | -               |
+| 0.7008 | 136000 | 0.0366        | -               |
+| 0.7059 | 137000 | 0.0344        | -               |
+| 0.7111 | 138000 | 0.0402        | -               |
+| 0.7162 | 139000 | 0.0331        | -               |
+| 0.7214 | 140000 | 0.0404        | -               |
+| 0.7266 | 141000 | 0.0433        | -               |
+| 0.7317 | 142000 | 0.032         | -               |
+| 0.7369 | 143000 | 0.0281        | -               |
+| 0.7420 | 144000 | 0.0265        | -               |
+| 0.7472 | 145000 | 0.0282        | -               |
+| 0.7523 | 146000 | 0.0233        | -               |
+| 0.7575 | 147000 | 0.0291        | -               |
+| 0.7626 | 148000 | 0.0358        | -               |
+| 0.7678 | 149000 | 0.0343        | -               |
+| 0.7729 | 150000 | 0.0292        | -               |
+| 0.7781 | 151000 | 0.0359        | -               |
+| 0.7832 | 152000 | 0.0361        | -               |
+| 0.7884 | 153000 | 0.0289        | -               |
+| 0.7935 | 154000 | 0.0374        | -               |
+| 0.7987 | 155000 | 0.0341        | -               |
+| 0.8038 | 156000 | 0.0353        | -               |
+| 0.8090 | 157000 | 0.033         | -               |
+| 0.8142 | 158000 | 0.0291        | -               |
+| 0.8193 | 159000 | 0.0362        | -               |
+| 0.8245 | 160000 | 0.0355        | -               |
+| 0.8296 | 161000 | 0.026         | -               |
+| 0.8348 | 162000 | 0.0237        | -               |
+| 0.8399 | 163000 | 0.0175        | -               |
+| 0.8451 | 164000 | 0.0219        | -               |
+| 0.8502 | 165000 | 0.0227        | -               |
+| 0.8554 | 166000 | 0.0177        | -               |
+| 0.8605 | 167000 | 0.0239        | -               |
+| 0.8657 | 168000 | 0.0223        | -               |
+| 0.8708 | 169000 | 0.0219        | -               |
+| 0.8760 | 170000 | 0.0248        | -               |
+| 0.8811 | 171000 | 0.0237        | -               |
+| 0.8863 | 172000 | 0.0262        | -               |
+| 0.8914 | 173000 | 0.026         | -               |
+| 0.8966 | 174000 | 0.0228        | -               |
+| 0.9018 | 175000 | 0.0259        | -               |
+| 0.9069 | 176000 | 0.0232        | -               |
+| 0.9121 | 177000 | 0.0268        | -               |
+| 0.9172 | 178000 | 0.0228        | -               |
+| 0.9224 | 179000 | 0.0198        | -               |
+| 0.9275 | 180000 | 0.0183        | -               |
+| 0.9327 | 181000 | 0.022         | -               |
+| 0.9378 | 182000 | 0.0227        | -               |
+| 0.9430 | 183000 | 0.021         | -               |
+| 0.9481 | 184000 | 0.0211        | -               |
+| 0.9533 | 185000 | 0.0216        | -               |
+| 0.9584 | 186000 | 0.0209        | -               |
+| 0.9636 | 187000 | 0.0191        | -               |
+| 0.9687 | 188000 | 0.0188        | -               |
+| 0.9739 | 189000 | 0.0203        | -               |
+| 0.9790 | 190000 | 0.0203        | -               |
+| 0.9842 | 191000 | 0.0313        | -               |
+| 0.9893 | 192000 | 0.0213        | -               |
+| 0.9945 | 193000 | 0.0164        | -               |
+| 0.9997 | 194000 | 0.0181        | -               |
+| 1.0000 | 194066 | -             | 0.0006          |
+| 1.0048 | 195000 | 0.2333        | -               |
+| 1.0100 | 196000 | 0.0787        | -               |
+| 1.0151 | 197000 | 0.0848        | -               |
+| 1.0203 | 198000 | 0.0813        | -               |
+| 1.0254 | 199000 | 0.0624        | -               |
+| 1.0306 | 200000 | 0.0691        | -               |
+| 1.0357 | 201000 | 0.0596        | -               |
+| 1.0409 | 202000 | 0.0575        | -               |
+| 1.0460 | 203000 | 0.0573        | -               |
+| 1.0512 | 204000 | 0.0684        | -               |
+| 1.0563 | 205000 | 0.0507        | -               |
+| 1.0615 | 206000 | 0.0548        | -               |
+| 1.0666 | 207000 | 0.0607        | -               |
+| 1.0718 | 208000 | 0.0713        | -               |
+| 1.0769 | 209000 | 0.0425        | -               |
+| 1.0821 | 210000 | 0.113         | -               |
+| 1.0873 | 211000 | 0.0432        | -               |
+| 1.0924 | 212000 | 0.0727        | -               |
+| 1.0976 | 213000 | 0.0431        | -               |
+| 1.1027 | 214000 | 0.0909        | -               |
+| 1.1079 | 215000 | 0.0453        | -               |
+| 1.1130 | 216000 | 0.087         | -               |
+| 1.1182 | 217000 | 0.0442        | -               |
+| 1.1233 | 218000 | 0.0503        | -               |
+| 1.1285 | 219000 | 0.0413        | -               |
+| 1.1336 | 220000 | 0.0899        | -               |
+| 1.1388 | 221000 | 0.0463        | -               |
+| 1.1439 | 222000 | 0.0956        | -               |
+| 1.1491 | 223000 | 0.0452        | -               |
+| 1.1542 | 224000 | 0.098         | -               |
+| 1.1594 | 225000 | 0.0426        | -               |
+| 1.1645 | 226000 | 0.0597        | -               |
+| 1.1697 | 227000 | 0.085         | -               |
+| 1.1749 | 228000 | 0.0487        | -               |
+| 1.1800 | 229000 | 0.0984        | -               |
+| 1.1852 | 230000 | 0.0465        | -               |
+| 1.1903 | 231000 | 0.0492        | -               |
+| 1.1955 | 232000 | 0.0864        | -               |
+| 1.2006 | 233000 | 0.0489        | -               |
+| 1.2058 | 234000 | 0.0855        | -               |
+| 1.2109 | 235000 | 0.0579        | -               |
+| 1.2161 | 236000 | 0.0455        | -               |
+| 1.2212 | 237000 | 0.0811        | -               |
+| 1.2264 | 238000 | 0.0488        | -               |
+| 1.2315 | 239000 | 0.0547        | -               |
+| 1.2367 | 240000 | 0.0691        | -               |
+| 1.2418 | 241000 | 0.0426        | -               |
+| 1.2470 | 242000 | 0.0528        | -               |
+| 1.2521 | 243000 | 0.0552        | -               |
+| 1.2573 | 244000 | 0.0607        | -               |
+| 1.2625 | 245000 | 0.0421        | -               |
+| 1.2676 | 246000 | 0.0434        | -               |
+| 1.2728 | 247000 | 0.0632        | -               |
+| 1.2779 | 248000 | 0.0546        | -               |
+| 1.2831 | 249000 | 0.0375        | -               |
+| 1.2882 | 250000 | 0.038         | -               |
+| 1.2934 | 251000 | 0.0471        | -               |
+| 1.2985 | 252000 | 0.0441        | -               |
+| 1.3037 | 253000 | 0.0383        | -               |
+| 1.3088 | 254000 | 0.0521        | -               |
+| 1.3140 | 255000 | 0.033         | -               |
+| 1.3191 | 256000 | 0.0339        | -               |
+| 1.3243 | 257000 | 0.0363        | -               |
+| 1.3294 | 258000 | 0.0429        | -               |
+| 1.3346 | 259000 | 0.0523        | -               |
+| 1.3397 | 260000 | 0.0353        | -               |
+| 1.3449 | 261000 | 0.0271        | -               |
+| 1.3500 | 262000 | 0.0364        | -               |
+| 1.3552 | 263000 | 0.0477        | -               |
+| 1.3604 | 264000 | 0.0532        | -               |
+| 1.3655 | 265000 | 0.0595        | -               |
+| 1.3707 | 266000 | 0.0237        | -               |
+| 1.3758 | 267000 | 0.0239        | -               |
+| 1.3810 | 268000 | 0.0389        | -               |
+| 1.3861 | 269000 | 0.0288        | -               |
+| 1.3913 | 270000 | 0.0728        | -               |
+| 1.3964 | 271000 | 0.0365        | -               |
+| 1.4016 | 272000 | 0.038         | -               |
+| 1.4067 | 273000 | 0.0285        | -               |
+| 1.4119 | 274000 | 0.0246        | -               |
+| 1.4170 | 275000 | 0.0692        | -               |
+| 1.4222 | 276000 | 0.0281        | -               |
+| 1.4273 | 277000 | 0.0322        | -               |
+| 1.4325 | 278000 | 0.0451        | -               |
+| 1.4376 | 279000 | 0.0202        | -               |
+| 1.4428 | 280000 | 0.0274        | -               |
+| 1.4480 | 281000 | 0.0254        | -               |
+| 1.4531 | 282000 | 0.0539        | -               |
+| 1.4583 | 283000 | 0.0407        | -               |
+| 1.4634 | 284000 | 0.0286        | -               |
+| 1.4686 | 285000 | 0.0241        | -               |
+| 1.4737 | 286000 | 0.029         | -               |
+| 1.4789 | 287000 | 0.0337        | -               |
+| 1.4840 | 288000 | 0.0317        | -               |
+| 1.4892 | 289000 | 0.0406        | -               |
+| 1.4943 | 290000 | 0.0377        | -               |
+| 1.4995 | 291000 | 0.0238        | -               |
+| 1.5046 | 292000 | 0.0226        | -               |
+| 1.5098 | 293000 | 0.0233        | -               |
+| 1.5149 | 294000 | 0.0186        | -               |
+| 1.5201 | 295000 | 0.0255        | -               |
+| 1.5252 | 296000 | 0.0305        | -               |
+| 1.5304 | 297000 | 0.0154        | -               |
+| 1.5356 | 298000 | 0.0214        | -               |
+| 1.5407 | 299000 | 0.0187        | -               |
+| 1.5459 | 300000 | 0.0143        | -               |
+| 1.5510 | 301000 | 0.03          | -               |
+| 1.5562 | 302000 | 0.0346        | -               |
+| 1.5613 | 303000 | 0.0209        | -               |
+| 1.5665 | 304000 | 0.0206        | -               |
+| 1.5716 | 305000 | 0.0239        | -               |
+| 1.5768 | 306000 | 0.0262        | -               |
+| 1.5819 | 307000 | 0.0179        | -               |
+| 1.5871 | 308000 | 0.0198        | -               |
+| 1.5922 | 309000 | 0.0288        | -               |
+| 1.5974 | 310000 | 0.0192        | -               |
+| 1.6025 | 311000 | 0.0435        | -               |
+| 1.6077 | 312000 | 0.0251        | -               |
+| 1.6128 | 313000 | 0.0205        | -               |
+| 1.6180 | 314000 | 0.0246        | -               |
+| 1.6232 | 315000 | 0.0176        | -               |
+| 1.6283 | 316000 | 0.026         | -               |
+| 1.6335 | 317000 | 0.025         | -               |
+| 1.6386 | 318000 | 0.029         | -               |
+| 1.6438 | 319000 | 0.0274        | -               |
+| 1.6489 | 320000 | 0.0343        | -               |
+| 1.6541 | 321000 | 0.028         | -               |
+| 1.6592 | 322000 | 0.0282        | -               |
+| 1.6644 | 323000 | 0.0239        | -               |
+| 1.6695 | 324000 | 0.017         | -               |
+| 1.6747 | 325000 | 0.0132        | -               |
+| 1.6798 | 326000 | 0.0252        | -               |
+| 1.6850 | 327000 | 0.0243        | -               |
+| 1.6901 | 328000 | 0.0232        | -               |
+| 1.6953 | 329000 | 0.0183        | -               |
+| 1.7004 | 330000 | 0.0244        | -               |
+| 1.7056 | 331000 | 0.0239        | -               |
+| 1.7107 | 332000 | 0.0277        | -               |
+| 1.7159 | 333000 | 0.0223        | -               |
+| 1.7211 | 334000 | 0.0252        | -               |
+| 1.7262 | 335000 | 0.0302        | -               |
+| 1.7314 | 336000 | 0.0224        | -               |
+| 1.7365 | 337000 | 0.0188        | -               |
+| 1.7417 | 338000 | 0.0174        | -               |
+| 1.7468 | 339000 | 0.0189        | -               |
+| 1.7520 | 340000 | 0.0152        | -               |
+| 1.7571 | 341000 | 0.0185        | -               |
+| 1.7623 | 342000 | 0.024         | -               |
+| 1.7674 | 343000 | 0.0249        | -               |
+| 1.7726 | 344000 | 0.0202        | -               |
+| 1.7777 | 345000 | 0.0248        | -               |
+| 1.7829 | 346000 | 0.0256        | -               |
+| 1.7880 | 347000 | 0.022         | -               |
+| 1.7932 | 348000 | 0.0271        | -               |
+| 1.7983 | 349000 | 0.024         | -               |
+| 1.8035 | 350000 | 0.0241        | -               |
+| 1.8087 | 351000 | 0.0243        | -               |
+| 1.8138 | 352000 | 0.018         | -               |
+| 1.8190 | 353000 | 0.0236        | -               |
+| 1.8241 | 354000 | 0.0237        | -               |
+| 1.8293 | 355000 | 0.0175        | -               |
+| 1.8344 | 356000 | 0.015         | -               |
+| 1.8396 | 357000 | 0.0111        | -               |
+| 1.8447 | 358000 | 0.014         | -               |
+| 1.8499 | 359000 | 0.0146        | -               |
+| 1.8550 | 360000 | 0.0108        | -               |
+| 1.8602 | 361000 | 0.0157        | -               |
+| 1.8653 | 362000 | 0.0142        | -               |
+| 1.8705 | 363000 | 0.0129        | -               |
+| 1.8756 | 364000 | 0.0168        | -               |
+| 1.8808 | 365000 | 0.0155        | -               |
+| 1.8859 | 366000 | 0.017         | -               |
+| 1.8911 | 367000 | 0.0164        | -               |
+| 1.8963 | 368000 | 0.0156        | -               |
+| 1.9014 | 369000 | 0.0168        | -               |
+| 1.9066 | 370000 | 0.015         | -               |
+| 1.9117 | 371000 | 0.018         | -               |
+| 1.9169 | 372000 | 0.0151        | -               |
+| 1.9220 | 373000 | 0.0132        | -               |
+| 1.9272 | 374000 | 0.0117        | -               |
+| 1.9323 | 375000 | 0.0142        | -               |
+| 1.9375 | 376000 | 0.0151        | -               |
+| 1.9426 | 377000 | 0.0142        | -               |
+| 1.9478 | 378000 | 0.0139        | -               |
+| 1.9529 | 379000 | 0.014         | -               |
+| 1.9581 | 380000 | 0.0141        | -               |
+| 1.9632 | 381000 | 0.012         | -               |
+| 1.9684 | 382000 | 0.0127        | -               |
+| 1.9735 | 383000 | 0.0131        | -               |
+| 1.9787 | 384000 | 0.0131        | -               |
+| 1.9839 | 385000 | 0.0158        | -               |
+| 1.9890 | 386000 | 0.0211        | -               |
+| 1.9942 | 387000 | 0.011         | -               |
+| 1.9993 | 388000 | 0.0118        | -               |
+| 2.0000 | 388132 | -             | 0.0005          |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.2
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0
+- Accelerate: 1.0.1
+- Datasets: 3.0.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "/data/aronow/pankaj/Embeddings/Bioformer-MNRL-finetuned/checkpoint-388132",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 1024,
+  "model_type": "bert",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 16,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.5.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c58dce77d24f995b71ad50cd1fb6a0818148c882e960c8363360d478d0ab4c4d
+size 166097144

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 1024,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_length": 1024,
+  "model_max_length": 1024,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}