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

@@ -0,0 +1,697 @@
+---
+base_model: pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:137221
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: What are the underlying physical mechanisms that allow for the
+    detection of rotation velocity using orbital angular momentum light spots, even
+    when they are completely deviated from the rotation center?
+  sentences:
+  - 'Pneumothorax following ultrasound-guided jugular vein puncture for central venous
+    access in interventional radiology: 4 years of experience. PURPOSE: The purpose
+    of our study was to review the rate of pneumothorax following central venous access,
+    using real-time ultrasound guidance. MATERIALS AND METHODS: Data related to ultrasound-guided
+    venous puncture, for central venous access, performed between July 1, 2004 and
+    June 30, 2008 was retrospectively and prospectively collected. Access route, needle
+    gauge, catheter type, and diagnosis of pneumothorax on the intraprocedure spot
+    radiographs and or the postprocedure chest radiographs, were recorded. RESULTS:
+    A total of 1262 ultrasound-guided jugular venous puncture for central venous access
+    were performed on a total of 1066 patients between July 1, 2004 and June 30, 2008.
+    Access vessels included 983 right internal jugular veins, 275 left internal jugular
+    veins, and 4 right external jugular veins. No pneumothorax (0%) was identified.
+    CONCLUSION: Due to an extremely low rate of pneumothorax following ultrasound-guided
+    central venous access, 0% in our study and other published studies, we suggest
+    that routine postprocedure chest radiograph to exclude pneumothorax may be dispensed
+    unless it is suspected by the operator or if the patient becomes symptomatic.'
+  - 'Targeting CDK4/6 in patients with cancer. The cyclin D-cyclin dependent kinase
+    (CDK) 4/6-inhibitor of CDK4 (INK4)-retinoblastoma (Rb) pathway controls cell cycle
+    progression by regulating the G1-S checkpoint. Dysregulation of the cyclin D-CDK4/6-INK4-Rb
+    pathway results in increased proliferation, and is frequently observed in many
+    types of cancer. Pathway activation can occur through a variety of mechanisms,
+    including gene amplification or rearrangement, loss of negative regulators, epigenetic
+    alterations, and point mutations in key pathway components. Due to the importance
+    of CDK4/6 activity in cancer cells, CDK4/6 inhibitors have emerged as promising
+    candidates for cancer treatment. Moreover, combination of a CDK4/6 inhibitor with
+    other targeted therapies may help overcome acquired or de novo treatment resistance.
+    Ongoing studies include combinations of CDK4/6 inhibitors with endocrine therapy
+    and phosphatidylinositol 3-kinase (PI3K) pathway inhibitors for hormone receptor-positive
+    (HR+) breast cancers, and with selective RAF and MEK inhibitors for tumors with
+    alterations in the mitogen activated protein kinase (MAPK) pathway such as melanoma.
+    In particular, the combination of CDK4/6 inhibitors with endocrine therapy, such
+    as palbociclib''s recent first-line approval in combination with letrozole, is
+    expected to transform the treatment of HR+ breast cancer. Currently, three selective
+    CDK4/6 inhibitors have been approved or are in late-stage development: palbociclib
+    (PD-0332991), ribociclib (LEE011), and abemaciclib (LY2835219). Here we describe
+    the current preclinical and clinical data for these novel agents and discuss combination
+    strategies with other agents for the treatment of cancer.'
+  - Rotation velocity detection with orbital angular momentum light spot completely
+    deviated out of the rotation center. Based on the rotational Doppler effect, an
+    orbital angular momentum beam can measure the lateral rotation velocity of an
+    object, which has broad application prospects. However, all existing research
+    focus on the light spot center coinciding with the rotation center, or only with
+    small center offset. This is difficult to ensure in remote detection applications.
+    In this paper, the rotational Doppler frequency shifts under three cases, including
+    no center offset, small center offset and large center offset, are analyzed theoretically.
+    Through theoretical research results, a novel method of measuring rotation velocity
+    is proposed, with the light spot completely deviated out of the rotation center.
+    A laboratory verification experiment shows that this proposed method breaks the
+    limit of center offset of lateral rotation velocity measurement and is of great
+    significance to the remote detection of non-cooperative rotation object.
+- source_sentence: What are the implications of hydrogen bonding patterns on the supramolecular
+    assembly of molecules, and how can these interactions be manipulated or controlled?
+  sentences:
+  - M.V. Volkenstein, evolutionary thinking and the structure of fitness landscapes.
+    High dimensional fitness landscapes are robustly dominated by saddle points, not
+    isolated peaks. We present an argument to this effect that is reminiscent of May's
+    complexity stability analysis and trace out the significance for the dynamics
+    of speciation, the connection between the neutral and punctuated aspects of evolution
+    and evolution on moving landscapes. The paper is written in honor of M.V. Volkenstein,
+    who devoted his last papers to uniting dynamics with evolutionary thinking.
+  - Differential impacts of smoke-free laws on indoor air quality. The authors assessed
+    the impacts of two different smoke-free laws on indoor air quality. They compared
+    the indoor air quality of 10 hospitality venues in Lexington and Louisville, Kentucky,
+    before and after the smoke-free laws went into effect. Real-time measurements
+    of particulate matter with aerodynamic diameter of 2.5 microm or smaller (PM2.5)
+    were made. One Lexington establishment was excluded from the analysis of results
+    because of apparent smoking violation after the law went into effect. The average
+    indoor PM2.5 concentrations in the nine Lexington venues decreased 91 percent,
+    from 199 to 18 microg/m3. The average indoor PM2.5 concentrations in the 10 Louisville
+    venues, however, increased slightly, from 304 to 338 microg/m3. PM2.5 levels in
+    the establishments decreased as numbers of burning cigarettes decreased. While
+    the Louisville partial smoke-free law with exemptions did not reduce indoor air
+    pollution in the selected venues, comprehensive and properly enforced smoke-free
+    laws can be an effective means of reducing indoor air pollution.
+  - 'Structures of three substituted arenesulfonamides from X-ray powder diffraction
+    data using the differential evolution technique. The structures of three substituted
+    arenesulfonamides have been solved from laboratory X-ray powder diffraction data,
+    using a new direct-space structure solution method based on a differential evolution
+    algorithm, and refined by the Rietveld method. In 2-toluenesulfonamide, C(7)H(9)NO(2)S
+    (I) (tetragonal I4(1)/a, Z = 16), the molecules are linked by N-H...O=S hydrogen
+    bonds into a three-dimensional framework. In 3-nitrobenzenesulfonamide, C(6)H(6)N(2)O(4)S
+    (II) (monoclinic P2(1), Z = 2), N-H...O=S hydrogen bonds produce molecular ladders,
+    which are linked into sheets by C-H...O=S hydrogen bonds: the nitro group does
+    not participate in the hydrogen bonding. Molecules of 4-nitrobenzenesulfonamide,
+    C(6)H(6)N(2)O(4)S (III) (monoclinic P2(1)/n, Z = 4), are linked into sheets by
+    four types of hydrogen bond, N-H...O=S, N-H...O(nitro), C-H...O=S and C-H...O(nitro),
+    and the sheets are weakly linked by aromatic pi...pi stacking interactions.'
+- source_sentence: When ceritinib used instead of crizotinib?
+  sentences:
+  - 'Pemedolac: a novel and long-acting non-narcotic analgesic. Pemedolacindole-1-acetic
+    acid; AY-30,715] exhibited potent analgesic effects against chemically induced
+    pain in rats and mice and against inflammatory pain in rats. In each of the animal
+    models used the analgesic potency of pemedolac was defined by an ED50 of 2.0 mg/kg
+    p.o. or less. Significant analgesic activity was detected in rats at 16 hr after
+    administration of 1 mg/kg p.o. (paw pressure test) and at 10 hr after administration
+    of 10 mg/kg p.o. to mice (p-phenylbenzoquinone writhing). Inasmuch as pemedolac
+    was inactive in the hot plate and tail-flick tests; and its analgesic activity
+    was not antagonized by naloxone (1 mg/kg s.c.), and tolerance did not develop
+    upon multiple administration; this drug does not exert its analgesic effects through
+    an opiate mechanism. Pemedolac differed from standard nonsteroidal anti-inflammatory
+    drugs (NSAIDs) in that the doses which produced analgesia were much lower than
+    those required for either anti-inflammatory or gastric irritant effects. In acute
+    anti-inflammatory tests, pemedolac exhibited only weak activity as evidenced by
+    an ED50 approximately 100 mg/kg p.o. in the carrageenan paw edema procedure. This
+    demonstrates for pemedolac a separation of at least 50-fold between the acute
+    analgesic and anti-inflammatory activities, which was greater than that observed
+    with reference NSAIDs. The compound also had a low ulcerogenic liability with
+    an acute UD50 = 107 mg/kg p.o. and a subacute UD50 estimated to be 140 mg/kg/day
+    p.o. In contrast, the reference NSAIDS (piroxicam, indomethacin, naproxen and
+    ibuprofen) exhibited similar dose-response relationships for the analgesic, anti-inflammatory
+    and gastric irritant effects.(ABSTRACT TRUNCATED AT 250 WORDS).'
+  - Genome-wide detection of CNVs associated with beak deformity in chickens using
+    high-density 600K SNP arrays. Beak deformity (crossed beaks) is found in several
+    indigenous chicken breeds including Beijing-You studied here. Birds with deformed
+    beaks have reduced feed intake and poor production performance. Recently, copy
+    number variation (CNV) has been examined in many species and is recognized as
+    a source of genetic variation, especially for disease phenotypes. In this study,
+    to unravel the genetic mechanisms underlying beak deformity, we performed genome-wide
+    CNV detection using Affymetrix chicken high-density 600K data on 48 deformed-beak
+    and 48 normal birds using penncnv. As a result, two and eight CNV regions (CNVRs)
+    covering 0.32 and 2.45 Mb respectively on autosomes were identified in deformed-beak
+    and normal birds respectively. Further RT-qPCR studies validated nine of the 10
+    CNVRs. The ratios of six CNVRs were significantly different between deformed-beak
+    and normal birds (P < 0.01). Within these six regions, three and 21 known genes
+    were identified in deformed-beak and normal birds respectively. Bioinformatics
+    analysis showed that these genes were enriched in six GO terms and one KEGG pathway.
+    Five candidate genes in the CNVRs were further validated using RT-qPCR. The expression
+    of LRIG2 (leucine rich repeats and immunoglobulin like domains 2) was lower in
+    birds with deformed beaks (P < 0.01). Therefore, the LRIG2 gene could be considered
+    a key factor in view of its known functions and its potential roles in beak deformity.
+    Overall, our results will be helpful for future investigations of the genomic
+    structural variations underlying beak deformity in chickens.
+  - 'Ceritinib: a new tyrosine kinase inhibitor for non-small-cell lung cancer. OBJECTIVE:
+    To review ceritinib for the treatment of anaplastic lymphoma kinase (ALK)-positive
+    metastatic non-small-cell lung cancer (NSCLC). DATA SOURCES: Literature searches
+    were conducted in PubMed, EMBASE (1974 to July week 5, 2014), and Google Scholar
+    using the terms ceritinib, LDK378, and non-small-cell lung cancer. STUDY SELECTION
+    AND DATA EXTRACTION: One phase 1 trial and 2 abstracts were identified. DATA SYNTHESIS:
+    Ceritinib is approved for the treatment of ALK-positive metastatic NSCLC in patients
+    who are intolerant to or have progressed despite therapy with crizotinib. In the
+    phase 1 clinical trial, the maximum tolerated dose was determined to be 750 mg
+    once daily. The overall response rate (ORR) was 58% (95% CI = 48-67) in patients
+    who received ≥400 mg daily (n = 114). In this group, the ORR was 56% (95% CI =
+    41-67) and 62% (95% CI = 44-78) among crizotinib-exposed and -naïve patients,
+    respectively. The ORR was 59% (95% CI = 47-70) in patients who received 750 mg
+    daily (n = 78). The ORR was 56% (95% CI = 41-70) in crizotinib-treated patients
+    and 64% (95% CI = 44-81) in crizotinib-naïve patients, respectively, in this subset.
+    The median duration of response was 8.2 months. Median progression-free survival
+    was 7.0 months. The most common adverse reactions included diarrhea, nausea, vomiting,
+    abdominal pain, anorexia, constipation, fatigue, and elevated transaminases. CONCLUSIONS:
+    Ceritinib has activity in crizotinib-resistant and crizotinib-naïve patients and
+    appears to be a viable alternative for ALK-positive NSCLC. Long-term data are
+    needed to further define the role of ceritinib in the treatment of NSCLC.'
+- source_sentence: How do underlying physiological mechanisms influence the relationship
+    between muscle tension and headache disorders?
+  sentences:
+  - 'The correlation of lncRNA SNHG16 with inflammatory cytokines, adhesion molecules,
+    disease severity, and prognosis in acute ischemic stroke patients. BACKGROUND:
+    Long non-coding RNA small nucleolar RNA host gene 16 (lncRNA SNHG16) is involved
+    in the pathogenesis of acute ischemic stroke (AIS) through the regulation of brain
+    endothelial cell viability, inflammation, atherosclerotic plaque formation, and
+    neural apoptosis. This study aimed to evaluate the prognostic value of lncRNA
+    SNHG16 in AIS patients. METHODS: Newly diagnosed AIS patients (N = 120) were serially
+    recruited. Their lncRNA SNHG16 expressions in peripheral blood mononuclear cells
+    (PBMCs) were detected by reverse transcription-quantitative polymerase chain reaction
+    (RT-qPCR); serum inflammatory cytokines and adhesion molecules were determined
+    using enzyme-linked immunosorbent assay (ELISA). The accumulating recurrence-free
+    survival (RFS) and overall survival (OS) were analyzed. Moreover, controls (N
+    = 60) were recruited and their lncRNA SNHG16 expressions in PBMCs were detected.
+    RESULTS: LncRNA SNHG16 was declined in AIS patients compared to controls (p <
+    0.001). Moreover, lncRNA SNHG16 was not related to any comorbidities in AIS patients
+    (all p > 0.05). Interestingly, lncRNA SNHG16 was negatively related to tumor necrosis
+    factor alpha (TNF-α) (p < 0.001), interleukin 6 (IL-6) (p = 0.013), and intracellular
+    cell adhesion molecule-1 (ICAM-1) (p = 0.024), while positively correlated with
+    interleukin 10 (IL-10) (p = 0.022) in AIS patients. Besides, lncRNA SNHG16 was
+    inversely associated with the National Institutes of Health Stroke Scale (NIHSS)
+    score in AIS patients (p = 0.003). During the follow-up period, in 14 (11.7%)
+    patients occurred recurrence and 5 (4.2%) patients died. Unexpectedly, lncRNA
+    SNHG16 was not associated with accumulating RFS (p = 0.103) or OS (p = 0.150)
+    in AIS patients. CONCLUSION: LncRNA SNHG16 relates to lower inflammatory cytokines,
+    adhesion molecules, and milder disease severity, but fails to predict prognosis
+    in AIS patients.'
+  - 'Burning mouth syndrome: a discussion of a complex pathology. Burning mouth syndrome
+    is a complex pathology for which there is very little information about the etiology
+    and pathogenesis. This lack of knowledge leaves patients with suboptimal treatments.
+    This article discusses the existing scientific evidence about this disease. Since
+    topical oral use of clonazepam have been shown to be effective and safe to treat
+    some patients suffering with burning mouth syndrome, formulations including clonazepam
+    are included with this article. Compounding topical preparations of clonazepam
+    offers opportunities for compounding pharmacists to be more involved in improving
+    the quality of life of burning mouth syndrome patients.'
+  - 'Tension headaches and muscle tension: is there a role for magnesium? Although
+    many theories and hypotheses have been offered for the etiology of tension-type
+    headache (TH), no one previous hypothesis seems to adequately explain TH. This
+    may, in large measure, account for why it is often difficult to effectively treat
+    TH. Herein, we review current and old hypotheses of TH and offer a new hypothesis
+    which is consistent with what is known about TH. We show that magnesium (Mg) metabolism
+    may be pivotal in both the etiology and treatment of TH. Measurement of serum
+    ionized Mg2+ (IMg2+) levels and brain intracellular free Mg2+ (i) appear to offer
+    excellent methods for establishing the validity of our hypothesis. Since approximately
+    70% of patients who have a TH exhibit muscular tightness and tenderness, it is
+    distinctly possible that problems in Mg metabolism and dietary intake are the
+    links to concomitant muscle tension and TH. The significance of release of pain
+    mediators, muscle cramps, muscle strains (and damage) and muscle tension to TH,
+    and its relationship to Mg metabolism, are reviewed. These are all associated
+    with a Mg-deficient state. It seems clear from the available data that TH''s are
+    more associated with muscle tension or scalp tension than any other headache type.
+    From the data available, Mg supplementation appears to be of great benefit in
+    many of these situations. We believe there is a great need for clinicians to examine
+    Mg2+ metabolism, bioavailable Mg2+ in muscle tissues and blood, and the effectiveness
+    of Mg salts (in a double-blinded, placebo-controlled manner) in subjects with
+    TH and muscle tension.'
+- source_sentence: How do the structural properties of nanoporous materials influence
+    their efficiency in catalytic reactions?
+  sentences:
+  - 'Rationale and design of the Kanyini guidelines adherence with the polypill (Kanyini-GAP)
+    study: a randomised controlled trial of a polypill-based strategy amongst indigenous
+    and non indigenous people at high cardiovascular risk. BACKGROUND: The Kanyini
+    Guidelines Adherence with the Polypill (Kanyini-GAP) Study aims to examine whether
+    a polypill-based strategy (using a single capsule containing aspirin, a statin
+    and two blood pressure-lowering agents) amongst Indigenous and non-Indigenous
+    people at high risk of experiencing a cardiovascular event will improve adherence
+    to guideline-indicated therapies, and lower blood pressure and cholesterol levels.
+    METHODS/DESIGN: The study is an open, randomised, controlled, multi-centre trial
+    involving 1000 participants at high risk of cardiovascular events recruited from
+    mainstream general practices and Aboriginal Medical Services, followed for an
+    average of 18 months. The participants will be randomised to one of two versions
+    of the polypill, the version chosen by the treating health professional according
+    to clinical features of the patient, or to usual care. The primary study outcomes
+    will be changes, from baseline measures, in serum cholesterol and systolic blood
+    pressure and self-reported current use of aspirin, a statin and at least two blood
+    pressure lowering agents. Secondary study outcomes include cardiovascular events,
+    renal outcomes, self-reported barriers to indicated therapy, prescription of indicated
+    therapy, occurrence of serious adverse events and changes in quality-of-life.
+    The trial will be supplemented by formal economic and process evaluations. DISCUSSION:
+    The Kanyini-GAP trial will provide new evidence as to whether or not a polypill-based
+    strategy improves adherence to effective cardiovascular medications amongst individuals
+    in whom these treatments are indicated. TRIAL REGISTRATION: This trial is registered
+    with the Australian New Zealand Clinical Trial Registry ACTRN126080005833347.'
+  - A highly robust cluster-based indium(III)-organic framework with efficient catalytic
+    activity in cycloaddition of CO2 and Knoevenagel condensation. The efficient catalytic
+    performance displayed by MOFs is decided by an appropriate charge/radius ratio
+    of defect metal sites, large enough solvent-accessible channels and Lewis base
+    sites capable of polarizing substrate molecules. Herein, the solvothermal self-assembly
+    led to a highly robust nanochannel-based framework of {·2DMF·5H2O}n (NUC-66) with
+    a 56.8% void volume, which is a combination of a tetranuclear cluster (abbreviated
+    as {In4}) and a conjugated tetracyclic pentacarboxylic acid ligand of 4,4'-(4-(4-carboxyphenyl)pyridine-2,6-diyl)diisophthalic
+    acid (H5CPDD). To the best of our knowledge, NUC-66 is a rarely reported {In4}-based
+    3D framework with embedded hierarchical triangular-microporous (2.9 Å) and hexagonal-nanoporous
+    (12.0 Å) channels, which are shaped by six rows of {In4} clusters. After solvent
+    exchange and vacuum drying, the surface of nanochannels in desolvated NUC-66a
+    is modified by unsaturated In3+ ions, Npyridine atoms and μ3-OH groups, all of
+    which display polarization ability towards polar molecules due to their Lewis
+    acidity or basicity. The catalytic experiments performed showed that NUC-66a had
+    high catalytic activity in the cycloaddition reactions of epoxides with CO2 under
+    mild conditions, which should be ascribed to its structural advantages including
+    nanoscale channels, rich bifunctional active sites, large surface areas and chemical
+    stability. Moreover, NUC-66a, as a heterogeneous catalyst, could greatly accelerate
+    the Knoevenagel condensation reactions of aldehydes and malononitrile. Hence,
+    this work confirms that the development of rigid nanoporous cluster-based MOFs
+    built on metal ions with a high charge and large radius ratio will be more likely
+    to realize practical applications, such as catalysis, adsorption and separation
+    of gas, etc.
+  - Absolute quantification of dehydroacetic acid in processed foods using quantitative
+    1H NMR. An absolute quantification method for the determination of dehydroacetic
+    acid in processed foods using quantitative (1)H NMR was developed and validated.
+    The level of dehydroacetic acid was determined using the proton signals of dehydroacetic
+    acid referenced to 1,4-bis (trimethylsilyl) benzene-d4 after simple solvent extraction
+    from processed foods. All the recoveries from three processed foods spiked at
+    two different concentrations were larger than 85%. The proposed method also proved
+    to be precise, with inter-day precision and excellent linearity. The limit of
+    quantification was confirmed as 0.13g/kg in processed foods, which is sufficiently
+    low for the purposes of monitoring dehydroacetic acid. Furthermore, the method
+    is rapid and easy to apply, and provides International System of Units traceability
+    without the need for authentic analyte reference materials. Therefore, the proposed
+    method is a useful and practical tool for determining the level of dehydroacetic
+    acid in processed foods.
+---
+
+# SentenceTransformer based on pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1](https://huggingface.co/pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1](https://huggingface.co/pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1) <!-- at revision 0c787217b5a51d93286052fb773dea46ff9b1e57 -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1-QA_100K-BioASQ-Epoch_5")
+# Run inference
+sentences = [
+    'How do the structural properties of nanoporous materials influence their efficiency in catalytic reactions?',
+    "A highly robust cluster-based indium(III)-organic framework with efficient catalytic activity in cycloaddition of CO2 and Knoevenagel condensation. The efficient catalytic performance displayed by MOFs is decided by an appropriate charge/radius ratio of defect metal sites, large enough solvent-accessible channels and Lewis base sites capable of polarizing substrate molecules. Herein, the solvothermal self-assembly led to a highly robust nanochannel-based framework of {·2DMF·5H2O}n (NUC-66) with a 56.8% void volume, which is a combination of a tetranuclear cluster (abbreviated as {In4}) and a conjugated tetracyclic pentacarboxylic acid ligand of 4,4'-(4-(4-carboxyphenyl)pyridine-2,6-diyl)diisophthalic acid (H5CPDD). To the best of our knowledge, NUC-66 is a rarely reported {In4}-based 3D framework with embedded hierarchical triangular-microporous (2.9 Å) and hexagonal-nanoporous (12.0 Å) channels, which are shaped by six rows of {In4} clusters. After solvent exchange and vacuum drying, the surface of nanochannels in desolvated NUC-66a is modified by unsaturated In3+ ions, Npyridine atoms and μ3-OH groups, all of which display polarization ability towards polar molecules due to their Lewis acidity or basicity. The catalytic experiments performed showed that NUC-66a had high catalytic activity in the cycloaddition reactions of epoxides with CO2 under mild conditions, which should be ascribed to its structural advantages including nanoscale channels, rich bifunctional active sites, large surface areas and chemical stability. Moreover, NUC-66a, as a heterogeneous catalyst, could greatly accelerate the Knoevenagel condensation reactions of aldehydes and malononitrile. Hence, this work confirms that the development of rigid nanoporous cluster-based MOFs built on metal ions with a high charge and large radius ratio will be more likely to realize practical applications, such as catalysis, adsorption and separation of gas, etc.",
+    'Absolute quantification of dehydroacetic acid in processed foods using quantitative 1H NMR. An absolute quantification method for the determination of dehydroacetic acid in processed foods using quantitative (1)H NMR was developed and validated. The level of dehydroacetic acid was determined using the proton signals of dehydroacetic acid referenced to 1,4-bis (trimethylsilyl) benzene-d4 after simple solvent extraction from processed foods. All the recoveries from three processed foods spiked at two different concentrations were larger than 85%. The proposed method also proved to be precise, with inter-day precision and excellent linearity. The limit of quantification was confirmed as 0.13g/kg in processed foods, which is sufficiently low for the purposes of monitoring dehydroacetic acid. Furthermore, the method is rapid and easy to apply, and provides International System of Units traceability without the need for authentic analyte reference materials. Therefore, the proposed method is a useful and practical tool for determining the level of dehydroacetic acid in processed foods.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 137,221 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 23.82 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 281.43 tokens</li><li>max: 915 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How do menstrual-related factors, such as pain and cycle irregularity, impact the mental health and well-being of young women in educational settings?</code>                                        | <code>Determinants of premenstrual dysphoric disorder and associated factors among regular undergraduate students at Hawassa University Southern, Ethiopia, 2023: institution-based cross-sectional study. BACKGROUND: Premenstrual dysphoric disorder (PMDD) is a condition causing severe emotional, physical, and behavioral symptoms before menstruation. It greatly hinders daily activities, affecting academic and interpersonal relationships. Attention is not given to premenstrual disorders among female students in higher education. As a result, students are susceptible to stress, and their academic success is influenced by various factors, including their menstrual cycle, and the long-term outcomes and consequences are poorly researched. Even though PMDD has a significant negative impact on student's academic achievement and success limited research has been conducted in low- and middle-income countries including Ethiopia, especially in the study setting. Therefore, a study is needed to assess premenstrual dysphoric disorder and associated factors among regular undergraduate students at Hawassa University. METHODS: An institutional-based cross-sectional study was conducted among 374 regular undergraduate female students at Hawassa University, College of Medicine and Health Sciences. A self-administered structured premenstrual symptoms screening tool for adolescents was used to assess premenstrual dysphoric disorder. The collected data were loaded into a statistical package for the social science version 25 and analyzed using it. Both bivariate and multivariate logistic regression were used to identify factors associated with premenstrual dysphoric disorder. Each independent variable was entered separately into bivariate analysis, and a variable with a p-value less than 0.25 were included in the multivariate analysis to adjust the possible confounders. Statistically significant was declared at a 95% confidence interval when variable with a p-value less than 0.05 in the multivariate analysis with premenstrual dysphoric disorder. RESULTS: The magnitude of premenstrual dysphoric disorder in this study was 62.6% (95% CI 57.4-67.5). Having severe premenstrual pain (AOR = 6.44;95%CI 1.02-40.73), having irregular menstrual cycle (AOR = 2.21; 95% CI 1.32-3.70), students who had poor social support (AOR = 5.10;95%CI, (2.76-12.92) and moderate social support (AOR = 4.93;95%CI (2.18-11.18), and students who used contraception (AOR = 3.76;95%CI, 2.21-6,40) were statistically significant factors with the outcome variable. CONCLUSION: The prevalence of premenstrual dysphoric disorder was high as compared to other studies. There was a strong link between irregular menstrual cycle, severe menstrual pain (severe dysmenorrhea), poor social support, and contraception use with premenstrual dysphoric disorder. This needs early screening and intervention to prevent the complications and worsening of the symptoms that affect students' academic performance by the institution.</code> |
+  | <code>How do sleep patterns influence cognitive function and learning in humans, and what are the broader implications for understanding neurological disorders?</code>                                    | <code>Neurochemical mechanisms for memory processing during sleep: basic findings in humans and neuropsychiatric implications. Sleep is essential for memory formation. Active systems consolidation maintains that memory traces that are initially stored in a transient store such as the hippocampus are gradually redistributed towards more permanent storage sites such as the cortex during sleep replay. The complementary synaptic homeostasis theory posits that weak memory traces are erased during sleep through a competitive down-selection mechanism, ensuring the brain's capability to learn new information. We discuss evidence from neuropharmacological experiments in humans to show how major neurotransmitters and neuromodulators are implicated in these memory processes. As to the major excitatory neurotransmitter glutamate that plays a prominent role in inducing synaptic consolidation, we show that these processes, while strengthening cortical memory traces during sleep, are insufficient to explain the consolidation of hippocampus-dependent declarative memories. In the inhibitory GABAergic system, we will offer insights how drugs may alter the intricate interplay of sleep oscillations that have been identified to be crucial for strengthening memories during sleep. Regarding the dopaminergic reward system, we will show how it is engaged during sleep replay, but that dopaminergic neuromodulation likely plays a side role for enhancing relevant memories during sleep. Also, we briefly go into basic evidence on acetylcholine and cortisol whose low tone during slow wave sleep (SWS) is crucial in supporting hippocampal-to-neocortical memory transmission. Finally, we will outline how these insights can be used to improve treatment of neuropsychiatric disorders focusing mainly on anxiety disorders, depression, and addiction that are strongly related to memory processing.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>What are the underlying physiological mechanisms by which elevated brain natriuretic peptide levels interact with heart rate variability to increase the likelihood of cardiovascular events?</code> | <code>The Combination of Non-dipper Heart Rate and High Brain Natriuretic Peptide Predicts Cardiovascular Events: The Japan Morning Surge-Home Blood Pressure (J-HOP) Study. BACKGROUND: We hypothesized that the association between the dipping heart rate (HR) pattern and cardiovascular (CV) events differs according to the brain natriuretic peptide (BNP) level. METHODS: We examined a subgroup of 1,369 patients from the Japan Morning Surge Home Blood Pressure study; these were patients who had CV risk factors and had undergone ambulatory blood pressure (BP) monitoring. HR non-dipping status was defined as (awake HR - sleep HR)/awake HR <0.1, and high BNP was defined as ≥35 pg/ml. We divided the patients into four groups according to their HR dipper status (dipping or non-dipping) and BNP level (normal or high). RESULTS: The mean follow-up period was 60 ± 30 months. The primary endpoints were fatal/nonfatal CV events (myocardial infarction, angina pectoris, stroke, hospitalization for heart failure, and aortic dissection). During the follow-up period, 23 patients (2.8%) in the dipper HR with normal BNP group, 8 patients (4.4%) in the non-dipper HR with normal BNP group, 24 patients (9.5%) in the dipper HR with high-BNP group, and 25 patients (21.0%) in the non-dipper HR with high-BNP group suffered primary endpoints (log rank 78.8, P < 0.001). Non-dipper HR was revealed as an independent predictor of CV events (hazard ratio, 2.13; 95% confidence interval, 1.35-3.36; P = 0.001) after adjusting for age, gender and smoking, dyslipidemia, diabetes mellitus, chronic kidney disease, BNP, non-dipper BP, 24-h HR, and 24-h systolic blood pressure. CONCLUSIONS: The combination of non-dipper HR and higher BNP was associated with a higher incidence of CV events.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 15,247 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 24.38 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 280.0 tokens</li><li>max: 866 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                               | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What are the underlying mechanisms by which electroporation enhances the immunogenicity of low-dose DNA vaccines, and what implications does this have for vaccine design and efficacy?</code> | <code>Immunotherapeutic Effects of Different Doses of Mycobacterium tuberculosis ag85a/b DNA Vaccine Delivered by Electroporation. Background: Tuberculosis (TB) is a major global public health problem. New treatment methods on TB are urgently demanded. Methods: Ninety-six female BALB/c mice were challenged with 2×104 colony-forming units (CFUs) of MTB H37Rv through tail vein injection, then was treated with 10μg, 50μg, 100μg, and 200μg of Mycobacterium tuberculosis (MTB) ag85a/b chimeric DNA vaccine delivered by intramuscular injection (IM) and electroporation (EP), respectively. The immunotherapeutic effects were evaluated immunologically, bacteriologically, and pathologically. Results: Compared with the phosphate-buffered saline (PBS) group, the CD4+IFN-γ+ T cells% in whole blood from 200 μg DNA IM group and four DNA EP groups increased significantly (P<0.05), CD8+IFN-γ+ T cells% (in 200 μg DNA EP group), CD4+IL-4+ T cells% (50 μg DNA IM group) and CD8+IL-4+ T cells% (50 μg and 100 μg DNA IM group, 100 μg and 200 μg DNA EP group) increased significantly only in a few DNA groups (P< 0.05). The CD4+CD25+ Treg cells% decreased significantly in all DNA vaccine groups (P<0.01). Except for the 10 μg DNA IM group, the lung and spleen colony-forming units (CFUs) of the other seven DNA immunization groups decreased significantly (P<0.001, P<0.01), especially the 100 μg DNA IM group and 50 μg DNA EP group significantly reduced the pulmonary bacterial loads and lung lesions than the other DNA groups. Conclusions: An MTB ag85a/b chimeric DNA vaccine could induce Th1-type cellular immune reactions. DNA immunization by EP could improve the immunogenicity of the low-dose DNA vaccine, reduce DNA dose, and produce good immunotherapeutic effects on the mouse TB model, to provide the basis for the future human clinical trial of MTB ag85a/b chimeric DNA vaccine.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+  | <code>What is known about prostate cancer screening in the UK</code>                                                                                                                                 | <code>Supporting informed decision making online in 20 minutes: an observational web-log study of a PSA test decision aid. BACKGROUND: Web-based decision aids are known to have an effect on knowledge, attitude, and behavior; important components of informed decision making. We know what decision aids achieve in randomized controlled trials (RCTs), but we still know very little about how they are used and how this relates to the informed decision making outcome measures. OBJECTIVE: To examine men's use of an online decision aid for prostate cancer screening using website transaction log files (web-logs), and to examine associations between usage and components of informed decision making. METHODS: We conducted an observational web-log analysis of users of an online decision aid, Prosdex. Men between 50 and 75 years of age were recruited for an associated RCT from 26 general practices across South Wales, United Kingdom. Men allocated to one arm of the RCT were included in the current study. Time and usage data were derived from website log files. Components of informed decision making were measured by an online questionnaire. RESULTS: Available for analysis were 82 web-logs. Overall, there was large variation in the use of Prosdex. The mean total time spent on the site was 20 minutes. The mean number of pages accessed was 32 (SD 21) out of a possible 60 pages. Significant associations were found between increased usage and increased knowledge (Spearman rank correlation [rho] = 0.69, P < .01), between increased usage and less favorable attitude towards PSA testing (rho = -0.52, P < .01), and between increased usage and reduced intention to undergo PSA testing (rho = -0.44, P < .01). A bimodal distribution identified two types of user: low access and high access users. CONCLUSIONS: Increased usage of Prosdex leads to more informed decision making, the key aim of the UK Prostate Cancer Risk Management Programme. However, developers realistically have roughly 20 minutes to provide useful information that will support informed decision making when the patient uses a web-based interface. Future decision aids need to be developed with this limitation in mind. We recommend that web-log analysis should be an integral part of online decision aid development and analysis. TRIAL REGISTRATION: ISRCTN48473735; http://www.controlled-trials.com/ISRCTN48473735 (Archived by WebCite at http://www.webcitation.org/5pqeF89tS).</code> |
+  | <code>How does early life adiposity influence long-term cardiovascular health, and what are the implications for prevention and intervention strategies?</code>                                      | <code>Adiposity is associated with endothelial activation in healthy 2-3 year-old children. Adiposity is associated with C-reactive protein level in healthy 2-3 year-old children and with other markers of endothelial activation in adults, but data are lacking in very young children. Data from 491 healthy Hispanic children were analyzed. Mean age was 2.7 years (SD 0.5, range 2-3 years); mean body mass index (BMI) was 17.2 kg/m2 (SD 1.9) among boys and 17.1 kg/m2 (SD 2.1) among girls. E-selectin level was associated with BMI (R = 0.11; p < 0.02), ponderal index (p < 0.02), waist circumference (p = 0.02), fasting insulin (p < 0.02), and insulin resistance (p < or = 0.05); these associations remained significant after adjustment for age, sex and fasting glucose. sVCAM was also associated with BMI (R = 0.12; p < 0.05). These observations indicate that adiposity is associated with inflammation and endothelial activation in very early childhood.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `resume_from_checkpoint`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: True
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0932 | 100  | 0.3536        | -               |
+| 0.1864 | 200  | 0.227         | -               |
+| 0.2796 | 300  | 0.1599        | -               |
+| 0.3728 | 400  | 0.1448        | -               |
+| 0.4660 | 500  | 0.1276        | -               |
+| 0.5592 | 600  | 0.1187        | -               |
+| 0.6524 | 700  | 0.1191        | -               |
+| 0.7456 | 800  | 0.1082        | -               |
+| 0.8388 | 900  | 0.1026        | -               |
+| 0.9320 | 1000 | 0.0991        | -               |
+| 1.0    | 1073 | -             | 0.0138          |
+| 1.0252 | 1100 | 0.089         | -               |
+| 1.1184 | 1200 | 0.0759        | -               |
+| 1.2116 | 1300 | 0.0726        | -               |
+| 1.3048 | 1400 | 0.075         | -               |
+| 1.3979 | 1500 | 0.0732        | -               |
+| 1.4911 | 1600 | 0.07          | -               |
+| 1.5843 | 1700 | 0.0706        | -               |
+| 1.6775 | 1800 | 0.0708        | -               |
+| 1.7707 | 1900 | 0.0691        | -               |
+| 1.8639 | 2000 | 0.0713        | -               |
+| 1.9571 | 2100 | 0.0626        | -               |
+| 2.0    | 2146 | -             | 0.0115          |
+| 2.0503 | 2200 | 0.0564        | -               |
+| 2.1435 | 2300 | 0.0547        | -               |
+| 2.2367 | 2400 | 0.052         | -               |
+| 2.3299 | 2500 | 0.0491        | -               |
+| 2.4231 | 2600 | 0.0542        | -               |
+| 2.5163 | 2700 | 0.0506        | -               |
+| 2.6095 | 2800 | 0.0508        | -               |
+| 2.7027 | 2900 | 0.0493        | -               |
+| 2.7959 | 3000 | 0.0537        | -               |
+| 2.8891 | 3100 | 0.0499        | -               |
+| 2.9823 | 3200 | 0.0488        | -               |
+| 3.0    | 3219 | -             | 0.0101          |
+| 3.0755 | 3300 | 0.0444        | -               |
+| 3.1687 | 3400 | 0.0433        | -               |
+| 3.2619 | 3500 | 0.0425        | -               |
+| 3.3551 | 3600 | 0.0412        | -               |
+| 3.4483 | 3700 | 0.0451        | -               |
+| 3.5415 | 3800 | 0.0433        | -               |
+| 3.6347 | 3900 | 0.0429        | -               |
+| 3.7279 | 4000 | 0.0423        | -               |
+| 3.8211 | 4100 | 0.0445        | -               |
+| 3.9143 | 4200 | 0.0407        | -               |
+| 4.0    | 4292 | -             | 0.0099          |
+| 4.0075 | 4300 | 0.0415        | -               |
+| 4.1007 | 4400 | 0.0371        | -               |
+| 4.1938 | 4500 | 0.0376        | -               |
+| 4.2870 | 4600 | 0.037         | -               |
+| 4.3802 | 4700 | 0.0388        | -               |
+| 4.4734 | 4800 | 0.0352        | -               |
+| 4.5666 | 4900 | 0.0367        | -               |
+| 4.6598 | 5000 | 0.0377        | -               |
+| 4.7530 | 5100 | 0.0384        | -               |
+| 4.8462 | 5200 | 0.0355        | -               |
+| 4.9394 | 5300 | 0.0415        | -               |
+| 5.0    | 5365 | -             | 0.0098          |
+
+
+### Framework Versions
+- Python: 3.12.2
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0
+- Accelerate: 1.0.1
+- Datasets: 3.0.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

@@ -0,0 +1,4 @@
+{
+  "[TEXT]": 32768,
+  "[YEAR_RANGE]": 32769
+}

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "/data/aronow/pankaj/Embeddings/Bioformer-MNRL-finetuned/checkpoint-5365",
+  "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": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32770
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.4.1+cu121"
+  },
+  "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:a55f3a88611239d6d860ea7241e6c95705177bea3750b7dfc50f05d12df57201
+size 166100216

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 +1,41 @@
+{
+  "additional_special_tokens": [
+    "[TEXT]",
+    "[YEAR_RANGE]"
+  ],
+  "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 +1,84 @@
+{
+  "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
+    },
+    "32768": {
+      "content": "[TEXT]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32769": {
+      "content": "[YEAR_RANGE]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "[TEXT]",
+    "[YEAR_RANGE]"
+  ],
+  "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]"
+}