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 +1,1160 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:33870508
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Physical Behavior Profiles Among Older Adults and Their Associations
+    With Physical Capacity and Life-Space Mobility.
+  sentences:
+  - Injectable hydrogel-based materials have emerged as promising alendronate (ALN)
+    delivery systems for the treatment of osteoporosis. However, their intrinsic permeability
+    limits the sustained delivery of small-molecule drugs. In response to this challenge,
+    we present the multifunctional hybrids composed of mesoporous silica particles
+    decorated with hydroxyapatite and loaded with alendronate (MSP-NH2-HAp-ALN), which
+    are immobilized in collagen/chitosan/hyaluronic acid-based hydrogel. We have mainly
+    focused on the biological in vitro/ex vivo evaluation of developed composites.
+    It was found that the extracts released from tested systems do not exhibit hemolytic
+    properties and are safe for blood elements and the human liver cell model. The
+    resulting materials create an environment conducive to differentiating human bone
+    marrow mesenchymal stem cells and reduce the viability of osteoclast precursors
+    (RAW 264.7). Importantly, even the system with the lowest concentration of ALN
+    caused a substantial cytotoxic effect on RAW 264.7 cells; their viability decreased
+    to 20 % and 10 % of control on 3 and 7 day of culture. Additionally, prolonged
+    ALN release (up to 20 days) with minimized burst release was observed, while material
+    features (wettability, swellability, degradation, mechanical properties) depended
+    on MSP-NH2-HAp-ALN content. The obtained data indicate that developed composites
+    establish a high-potential formulation for safe and effective osteoporosis therapy.
+  - 'We identified data-driven multidimensional physical activity (PA) profiles using
+    several novel accelerometer-derived metrics. Participants aged 75, 80, and 85
+    (n = 441) wore triaxial accelerometers for 3-7 days. PA profiles were formed with
+    k-means cluster analysis based on PA minutes, intensity, fragmentation, sit-to-stand
+    transitions, and gait bouts for men and women. Associations with physical capacity
+    and life-space mobility were examined using age-adjusted general linear models.
+    Three profiles emerged: "Exercisers" and "actives" accumulated relatively high
+    PA minutes, with actives engaging in lighter intensity PA. "Inactives" had the
+    highest activity fragmentation and lowest PA volume, intensity, and gait bouts.
+    Inactives showed lower scores in physical capacity and life-space mobility compared
+    with exercisers and actives. Exercisers and actives had similar physical capacity
+    and life-space mobility, except female exercisers had higher walking speed in
+    the 6-min walk test. Our findings demonstrate the importance of assessing PA as
+    multidimensional behavior rather than focusing on a single metric.'
+  - 'Existing exoskeletons for pediatric gait assistance have limitations in anthropometric
+    design, structure weight, cost, user safety features, and adaptability to diverse
+    users. Additionally, creating precise models for pediatric rehabilitation is difficult
+    because the rapid anthropometric changes in children result in unknown model parameters.
+    Furthermore, external disruptions, like unpredictable movements and involuntary
+    muscle contractions, add complexity to the control schemes that need to be managed.
+    To overcome these limitations, this study aims to develop an affordable stand-aided
+    lower-limb exoskeleton specifically for pediatric subjects (8-12 years, 25-40
+    kg, 128-132 cm) in passive-assist mode. The authors modified a previously developed
+    model (LLESv1) for improved rigidity, reduced mass, simplified motor arrangement,
+    variable waist size, and enhanced mobility. A computer-aided design of the new
+    exoskeleton system (LLESv2) is presented. The developed prototype of the exoskeleton
+    appended with a pediatric subject (age: 12 years old, body mass: 40 kg, body height:
+    132 cm) is presented with real-time hardware architecture. Thereafter, an improved
+    fast non-singular terminal sliding mode (IFNSTSM) control scheme is proposed,
+    incorporating a double exponential reaching law for expedited error convergence
+    and enhanced stability. The Lyapunov stability warrants the control system''s
+    performance despite uncertainties and disturbances. In contrast to fast non-singular
+    terminal sliding mode (FNSTSM) control and time-scaling sliding mode (TSSM) control,
+    experimental validation demonstrates the effectiveness of IFNSTSM control by a
+    respective average of 5.39% and 42.1% in tracking desired joint trajectories with
+    minimal and rapid finite time converging errors. Moreover, the exoskeleton with
+    the proposed IFNSTSM control requires significantly lesser control efforts than
+    the exoskeleton using contrast FNSTSM control. The Bland-Altman analysis indicates
+    that although there is a minimal mean difference in variables when employing FNSTSM
+    and IFNSTSM controllers, the latter exhibits significant performance variations
+    as the mean of variables changes. This research contributes to affordable and
+    effective pediatric gait assistance, improving rehabilitation outcomes and enhancing
+    mobility support.'
+- source_sentence: Anatomo-functional basis of emotional and motor resonance elicited
+    by facial expressions.
+  sentences:
+  - Simulation theories predict that the observation of other's expressions modulates
+    neural activity in the same centers controlling their production. This hypothesis
+    has been developed by two models, postulating that the visual input is directly
+    projected either to the motor system for action recognition (motor resonance)
+    or to emotional/interoceptive regions for emotional contagion and social synchronization
+    (emotional resonance). Here we investigated the role of frontal/insular regions
+    in the processing of observed emotional expressions by combining intracranial
+    recording, electrical stimulation and effective connectivity. First, we intracranially
+    recorded from prefrontal, premotor or anterior insular regions of 44 patients
+    during the passive observation of emotional expressions, finding widespread modulations
+    in prefrontal/insular regions (anterior cingulate cortex, anterior insula, orbitofrontal
+    cortex and inferior frontal gyrus) and motor territories (rolandic operculum and
+    inferior frontal junction). Subsequently, we electrically stimulated the activated
+    sites, finding that (a) in the anterior cingulate cortex and anterior insula,
+    the stimulation elicited emotional/interoceptive responses, as predicted by the
+    'emotional resonance model', (b) in the rolandic operculum it evoked face/mouth
+    sensorimotor responses, in line with the 'motor resonance' model, and (c) all
+    other regions were unresponsive or revealed functions unrelated to the processing
+    of facial expressions. Finally, we traced the effective connectivity to sketch
+    a network-level description of these regions, finding that the anterior cingulate
+    cortex and the anterior insula are reciprocally interconnected while the rolandic
+    operculum is part of the parieto-frontal circuits and poorly connected with the
+    formers. These results support the hypothesis that the pathways hypothesized by
+    the 'emotional resonance' and the 'motor resonance' models work in parallel, differing
+    in terms of spatio-temporal fingerprints, reactivity to electrical stimulation
+    and connectivity patterns.
+  - STAC3-related myopathy, or Native American myopathy, and myopathic facies. Since
+    the first description of NAM, more cases have been described worldwide, with three
+    cases reported from the Middle East. This study presents a cohort of seven Saudi
+    NAM patients belonging to three families. To our knowledge, this cohort is the
+    largest to be reported in the Arabian Peninsula and the Middle Eastern region.
+    We will also highlight the importance of considering this MH-causing disease preoperatively
+    in myopathic children with cleft palate in areas where NAM has been described.
+  - The Tibetan Plateau supplies water to nearly 2 billion people in Asia, but climate
+    change poses threats to its aquatic microbial resources. Here, we construct the
+    Tibetan Plateau Microbial Catalog by sequencing 498 metagenomes from six water
+    ecosystems (saline lakes, freshwater lakes, rivers, hot springs, wetlands and
+    glaciers). Our catalog expands knowledge of regional genomic diversity by presenting
+    32,355 metagenome-assembled genomes that de-replicated into 10,723 representative
+    genome-based species, of which 88% were unannotated. The catalog contains nearly
+    300 million non-redundant gene clusters, of which 15% novel, and 73,864 biosynthetic
+    gene clusters, of which 50% novel, thus expanding known functional diversity.
+    Using these data, we investigate the Tibetan Plateau aquatic microbiome's biogeography
+    along a distance of 2,500 km and >5 km in altitude. Microbial compositional similarity
+    and the shared gene count with the Tibetan Plateau microbiome decline along with
+    distance and altitude difference, suggesting a dispersal pattern. The Tibetan
+    Plateau Microbial Catalog stands as a substantial repository for high-altitude
+    aquatic microbiome resources, providing potential for discovering novel lineages
+    and functions, and bridging knowledge gaps in microbiome biogeography.
+- source_sentence: Effect of verbal cues on the coupling and stability of anti-phase
+    bimanual coordination pattern in children with probable developmental coordination
+    disorder.
+  sentences:
+  - 'BACKGROUND: Tobacco smoking remains a key cause of preventable illness and death
+    globally. In response, many countries provide extensive services to help people
+    to stop smoking by offering a variety of effective behavioural and pharmacological
+    therapies. However, many people who wish to stop smoking do not have access to
+    or use stop smoking supports, and new modes of support, including the use of financial
+    incentives, are needed to address this issue. A realist review of published international
+    literature was undertaken to understand how, why, for whom, and in which circumstances
+    financial incentives contribute to success in stopping smoking for general population
+    groups and among pregnant women. METHODS: Systematic searches were undertaken
+    from inception to February 2022 of five academic databases: MEDLINE (ovid), Embase.com,
+    CIHAHL, Scopus and PsycINFO. Study selection was inclusive of all study designs.
+    Twenty-two studies were included. Using Pawson and Tilley''s iterative realist
+    review approach, data collected were screened, selected, coded, analysed, and
+    synthesised into a set of explanatory theoretical findings. RESULTS: Data were
+    synthesised into six Context-Mechanism-Outcome Configurations and one overarching
+    programme theory after iterative rounds of analysis, team discussion, and expert
+    panel feedback. Our programme theory shows that financial incentives are particularly
+    useful to help people stop smoking if they have a financial need, are pregnant
+    or recently post-partum, have a high threshold for behaviour change, and/or respond
+    well to external rewards. The incentives work through a number of mechanisms including
+    the role their direct monetary value can play in a person''s life and through
+    a process of reinforcement where they can help build confidence and self-esteem.
+    CONCLUSION: This is the first realist review to synthesise how, why, and for whom
+    financial incentives work among those attempting to stop smoking, adding to the
+    existing evidence demonstrating their efficacy. The findings will support the
+    implementation of current knowledge into effective programmes which can enhance
+    the impact of stop smoking care. PROSPERO REGISTRATION NUMBER: CRD42022298941.'
+  - We developed a synthetic method for obtaining 4,5-disubstituted 2-(pyridin-2-yl)oxazoles
+    from picolinamide and aldehydes by employing Pd(TFA)2 as the catalyst in n-octane.
+    This cascade reaction involves the condensation of picolinamide and two aldehyde
+    molecules promoted by trifluoroacetic acid (TFA) generated in situ from Pd(TFA)2.
+    This one-pot protocol provides rapid access to synthetically valuable triaryloxazoles
+    from readily available starting materials under mild conditions. An 18O labeling
+    study revealed that this tandem reaction proceeded via a different reaction mechanism
+    compared to the Robinson-Gabriel oxazole synthesis.
+  - 'The study of the emergence and stability of bimanual and interlimb coordination
+    patterns in children with Developmental Coordination Disorder (DCD) has shown
+    that they encounter greater difficulties in coupling their limbs compared to typically
+    developing (TD) children. Verbal cues have been identified as strategies to direct
+    children''s attention to more relevant task information, thus potentially improving
+    motor performance. Consequently, this study investigated the effect of providing
+    verbal cues on the execution of bimanual tasks in children with and without probable
+    DCD. Twenty-eight children aged 9-10, matched by age and gender, were divided
+    into two groups: pDCD and TD. The children performed bilateral trajectory movements
+    with both hands (horizontal back-and-forth), holding a pen on a tablet, in anti-phase
+    (180°) coordination pattern, in two conditions: No cues and Verbal cues. In the
+    last condition, children received verbal cues to maintain the anti-phase pattern
+    even with an increase in hand oscillation frequency. Relative phase and variability
+    of relative phase between the hands were calculated for analysis of pattern coupling
+    and stability. Hand cycles, movement amplitude, and tablet pressure force were
+    calculated to analyze pattern control parameters. All these variables were compared
+    between groups and conditions. The results indicated that despite the pDCD group
+    showing greater variability in the anti-phase coordination pattern compared to
+    the TD group, both groups performed better in the Verbal cues than the No cues
+    condition. Furthermore, the pDCD group exhibited more hand movement cycles and
+    applied greater pressure force compared to the TD group, suggesting different
+    motor control strategies during the bimanual task. It is suggested that the use
+    of verbal cues during bimanual task execution improves children''s performance,
+    potentially by promoting interaction between attention, as a cognitive function,
+    and intrinsic coordination dynamics, thereby reducing variability in the perceptual-motor
+    system.'
+- source_sentence: 'Frailty efficacy as a predictor of clinical and cognitive complications
+    in patients undergoing coronary artery bypass grafting: a prospective cohort study.'
+  sentences:
+  - 'BACKGROUND: Frailty is proposed as a predictor of outcomes in patients undergoing
+    major surgeries, although data on the association of frailty and coronary artery
+    bypass grafting, cognitive function by Montreal Cognitive Assessment (MoCA), and
+    depression by the Geriatric Depression Scale (GDS) were obtained. The incidence
+    of adverse outcomes was investigated at the three-month follow-up. Outcomes between
+    frail and non-frail groups were compared utilizing T-tests and Mann-Whitney U
+    tests, as appropriate. RESULTS: We included 170 patients with a median age of
+    66 ± 4 years (75.3% male). Of these, 58 cases were classified as frail, and 112
+    individuals were non-frail, preoperatively. Frail patients demonstrated significantly
+    worse baseline MOCA scores (21.08 versus 22.41, P = 0.045), GDS (2.00 versus 1.00,
+    P = 0.009), and Lawton IADL (8.00 versus 6.00, P < 0.001) compared to non-frail.
+    According to 3-month follow-up data, postoperative MOCA and GDS scores were comparable
+    between the two groups, while Lawton IADL (8.00 versus 6.00, P < 0.001) was significantly
+    lower in frail cases. A significantly higher rate of readmission (1.8% versus
+    12.1%), sepsis (7.1% versus 19.0%), as well as a higher Euroscore (1.5 versus
+    1.9), was observed in the frail group. A mildly significantly more extended ICU
+    stay (6.00 versus 5.00, p = 0.051) was shown in the frail patient. CONCLUSION:
+    Frailty showed a significant association with a worse preoperative independence
+    level, cognitive function, and depression status, as well as increased postoperative
+    complications.'
+  - 'OBJECTIVE: To assess presentation of neurosyphilis with a focus on the psychiatric
+    aspects. METHOD: File review of the cases with a positive cerebrospinal fluid
+    venereal disease research laboratory test between 1999 to 2020. RESULTS: Medical
+    records of 143 neurosyphilis patients were analysed. Hallucinations, delusions,
+    and catatonia were the commonest psychiatric symptoms. Brain atrophy was the commonest
+    neuroimaging finding. The number of neurosyphilis patients and the proportion
+    with delirium or catatonia declined during the second decade. CONCLUSION: Atypical
+    presentation of psychiatric symptoms around the fifth decade, with associated
+    neurological symptoms or brain imaging changes, should prompt evaluation for neurosyphilis.'
+  - 'INTRODUCTION: Bibliometrics evaluates the quality of biomedical journals. The
+    aim of this study was to compare the main bibliometric indexes of the official
+    journals of scientific societies of Internal Medicine in Europe. MATERIAL AND
+    METHODS: Bibliometric information was obtained from the Web of Science European
+    Journal of Internal Medicine, which ranked in the first quartile (Q1) for JIF,
+    CiteScore and JCI metrics, exceeding values of 1 in Normalized Eigenfactor and
+    SNIP metrics; 2) Internal and Emergency Medicine, Q1 for CiteScore and JCI metrics,
+    and with values >1 in Normalized EigenFactor and SNIP metrics; 3) Polish Archives
+    of Internal Medicine, Q1 for JCI metrics; 4) Revista Clínica Española, Q2 for
+    JIF, CiteScore and JCI metrics; and 5) Acta Medica Belgica, Q2 for CiteScore and
+    JCI metrics. These journals increased their impact metrics in the last 3 years,
+    in parallel with the COVID pandemic. CONCLUSIONS: Five official journals of European
+    Internal Medicine societies, including Revista Clínica Española, meet high quality
+    standards.'
+- source_sentence: 'De Garengeot Hernia, an acute appendicitis in the right femoral
+    hernia canal, and successful management with transabdominal closure and appendectomy:
+    a case Report.'
+  sentences:
+  - With the increasing population worldwide more wastewater is created by human activities
+    and discharged into the waterbodies. This is causing the contamination of aquatic
+    bodies, thus disturbing the marine ecosystems. The rising population is also posing
+    a challenge to meet the demands of fresh drinking water in the water-scarce regions
+    of the world, where drinking water is made available to people by desalination
+    process. The fouling of composite membranes remains a major challenge in water
+    desalination. In this innovative study, we present a novel probabilistic approach
+    to analyse and anticipate the predominant fouling mechanisms in the filtration
+    process. Our establishment of a robust theoretical framework hinges upon the utilization
+    of both the geometric law and the Hermia model, elucidating the concept of resistance
+    in series (RIS). By manipulating the transmembrane pressure, we demonstrate effective
+    management of permeate flux rate and overall product quality. Our investigations
+    reveal a decrease in permeate flux in three distinct phases over time, with the
+    final stage marked by a significant reduction due to the accumulation of a denser
+    cake layer. Additionally, an increase in transmembrane pressure leads to a correlative
+    rise in permeate flux, while also exerting negative effects such as membrane ruptures.
+    Our study highlights the minimal immediate impact of the intermediate blocking
+    mechanism (n = 1) on permeate flux, necessitating continuous monitoring for potential
+    long-term effects. Additionally, we note a reduced membrane selectivity across
+    all three fouling types (n = 0, n = 1.5, n = 2). Ultimately, our findings indicate
+    that the membrane undergoes complete fouling with a probability of P = 0.9 in
+    the presence of all three fouling mechanisms. This situation renders the membrane
+    unable to produce water at its previous flow rate, resulting in a significant
+    reduction in the desalination plant's productivity. I have demonstrated that higher
+    pressure values notably correlate with increased permeate flux across all four
+    membrane types. This correlation highlights the significant role of TMP in enhancing
+    the production rate of purified water or desired substances through membrane filtration
+    systems. Our innovative approach opens new perspectives for water desalination
+    management and optimization, providing crucial insights into fouling mechanisms
+    and proposing potential strategies to address associated challenges.
+  - Incarceration of the appendix within a femoral hernia is a rare condition of abdominal
+    wall hernia about 0.1 to 0.5% in reported femoral hernia. We report a case of
+    a 56-year-old female whose appendix was trapped in the right femoral canal. There
+    are few reports in the literature on entrapment of the appendix within a femoral
+    hernia. The management of this condition includes antibiotics, drainage appendectomy,
+    hernioplasty and mesh repair.
+  - 'INTRODUCTION: Globally, the prevalence of obesity tripled from 1975 to 2016.
+    There is evidence that air pollution may contribute to the obesity epidemic through
+    an increase in oxidative stress and inflammation of adipose tissue. However, the
+    impact of air pollution on body weight at a population level remains inconclusive.
+    This systematic review and meta-analysis will estimate the association of ambient
+    air pollution with obesity, distribution of ectopic adipose tissue, and the incidence
+    and prevalence of non-alcoholic fatty liver disease among adults. METHODS AND
+    ANALYSIS: The study will follow the Preferred Reporting Items for Systematic Reviews
+    and Meta-Analyses guidelines for conduct and reporting. The search will include
+    the following databases: Ovid Medline, Embase, PubMed, Web of Science and Latin
+    America and the Caribbean Literature on Health Sciences, and will be supplemented
+    by a grey literature search. Each article will be independently screened by two
+    reviewers, and relevant data will be extracted independently and in duplicate.
+    Study-specific estimates of associations and their 95% Confidence Intervals will
+    be pooled using a DerSimonian and Laird random-effects model, implemented using
+    the RevMan software. The I2 statistic will be used to assess interstudy heterogeneity.
+    The confidence in the body of evidence will be assessed using the Grading of Recommendations
+    Assessment, Development and Evaluation (GRADE) approach. ETHICS AND DISSEMINATION:
+    As per institutional policy, ethical approval is not required for secondary data
+    analysis. In addition to being published in a peer-reviewed journal and presented
+    at conferences, the results of the meta-analysis will be shared with key stakeholders,
+    health policymakers and healthcare professionals. PROSPERO REGISTRATION NUMBER:
+    CRD42023423955.'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# 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 dimensions
+- **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/Bioformer-16L-UMLS-Pubmed_PMC-Forward_TCE-Epoch-3")
+# Run inference
+sentences = [
+    'De Garengeot Hernia, an acute appendicitis in the right femoral hernia canal, and successful management with transabdominal closure and appendectomy: a case Report.',
+    'Incarceration of the appendix within a femoral hernia is a rare condition of abdominal wall hernia about 0.1 to 0.5% in reported femoral hernia. We report a case of a 56-year-old female whose appendix was trapped in the right femoral canal. There are few reports in the literature on entrapment of the appendix within a femoral hernia. The management of this condition includes antibiotics, drainage appendectomy, hernioplasty and mesh repair.',
+    "With the increasing population worldwide more wastewater is created by human activities and discharged into the waterbodies. This is causing the contamination of aquatic bodies, thus disturbing the marine ecosystems. The rising population is also posing a challenge to meet the demands of fresh drinking water in the water-scarce regions of the world, where drinking water is made available to people by desalination process. The fouling of composite membranes remains a major challenge in water desalination. In this innovative study, we present a novel probabilistic approach to analyse and anticipate the predominant fouling mechanisms in the filtration process. Our establishment of a robust theoretical framework hinges upon the utilization of both the geometric law and the Hermia model, elucidating the concept of resistance in series (RIS). By manipulating the transmembrane pressure, we demonstrate effective management of permeate flux rate and overall product quality. Our investigations reveal a decrease in permeate flux in three distinct phases over time, with the final stage marked by a significant reduction due to the accumulation of a denser cake layer. Additionally, an increase in transmembrane pressure leads to a correlative rise in permeate flux, while also exerting negative effects such as membrane ruptures. Our study highlights the minimal immediate impact of the intermediate blocking mechanism (n = 1) on permeate flux, necessitating continuous monitoring for potential long-term effects. Additionally, we note a reduced membrane selectivity across all three fouling types (n = 0, n = 1.5, n = 2). Ultimately, our findings indicate that the membrane undergoes complete fouling with a probability of P = 0.9 in the presence of all three fouling mechanisms. This situation renders the membrane unable to produce water at its previous flow rate, resulting in a significant reduction in the desalination plant's productivity. I have demonstrated that higher pressure values notably correlate with increased permeate flux across all four membrane types. This correlation highlights the significant role of TMP in enhancing the production rate of purified water or desired substances through membrane filtration systems. Our innovative approach opens new perspectives for water desalination management and optimization, providing crucial insights into fouling mechanisms and proposing potential strategies to address associated challenges.",
+]
+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: 33,870,508 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: 5 tokens</li><li>mean: 36.24 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 328.76 tokens</li><li>max: 1024 tokens</li></ul> |
+* Samples:
+  | anchor                                                         | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:---------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How TO OBTAIN THE BRAIN OF THE CAT.</code>               | <code>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>ADDRESS OF COL. GARRICK MALLERY, U. S. ARMY.</code>      | <code>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 unde...</code> |
+  | <code>DOLBEAR ON THE NATURE AND CONSTITUTION OF MATTER.</code> | <code>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: 33,870,508 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: 24.64 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 281.83 tokens</li><li>max: 894 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                   | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Noticing education campaigns or public health messages about vaping among youth in the United States, Canada and England from 2018 to 2022.</code> | <code>Public health campaigns have the potential to correct vaping misperceptions. However, campaigns highlighting vaping harms to youth may increase misperceptions that vaping is equally/more harmful than smoking. Vaping campaigns have been implemented in the United States and Canada since 2018 and in England since 2017 but with differing focus: youth vaping prevention. Over half of youth reported noticing vaping campaigns, and noticing increased from August 2018 to February 2020. Consistent with implementation of youth vaping prevention campaigns in the United States and Canada, most youth reported noticing vaping campaigns/messages, and most were perceived to negatively portray vaping.</code>                                                                                                                                                                                                                                                                                                                         |
+  | <code>Comprehensive performance evaluation of six bioaerosol samplers based on an aerosol wind tunnel.</code>                                            | <code>Choosing a suitable bioaerosol sampler for atmospheric microbial monitoring has been a challenge to researchers interested in environmental microbiology, especially during a pandemic. However, a comprehensive and integrated evaluation method to fully assess bioaerosol sampler performance is still lacking. Herein, we constructed a customized wind tunnel operated at 2-20 km/h wind speed to systematically and efficiently evaluate the performance of six frequently used samplers, where various aerosols, including Arizona test dust, bacterial spores, gram-positive and gram-negative bacteria, phages, and viruses, were generated. After 10 or 60 min of sampling, the physical and biological sampling efficiency and short or long-term sampling capabilities were determined by performing aerodynamic particle size analysis, live microbial culturing, and a qPCR assay. The results showed that AGI-30 and BioSampler impingers have good physical and biological sampling efficiencies for short-term sampling...</code> |
+  | <code>The occurrence, sources, and health risks of substituted polycyclic aromatic hydrocarbons (SPAHs) cannot be ignored.</code>                        | <code>Similar to parent polycyclic aromatic hydrocarbons (PPAHs), substituted PAHs (SPAHs) are prevalent in the environment and harmful to humans. However, they have not received much attention. This study investigated the occurrence, distribution, and sources of 10 PPAHs and 15 SPAHs in soil, water, and indoor and outdoor PM2.5 and dust in high-exposure areas (EAH) near industrial parks and low-exposure areas (EAL) far from industrial parks. PAH pollution in all media was more severe in the EAH than in the EAL. All SPAHs were detected in this study, with alkylated and oxygenated PAHs being predominant. Additionally, 3-OH-BaP and 1-OH-Pyr were detected in all dust samples in this study, and 6-N-Chr, a compound with carcinogenicity 10 times higher than that of BaP, was detected at high levels in all tap water samples. According to the indoor-outdoor ratio, PAHs in indoor PM2.5 in the EAH mainly originated from indoor pollution sources; however, those in the EAL were simultaneously affected by...</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`: 2
+- `max_steps`: 502764
+- `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`: 2
+- `max_steps`: 502764
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: 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      | 1.793         | -               |
+| 0.0040 | 1000   | 0.3695        | -               |
+| 0.0080 | 2000   | 0.0813        | -               |
+| 0.0119 | 3000   | 0.0666        | -               |
+| 0.0159 | 4000   | 0.0817        | -               |
+| 0.0199 | 5000   | 0.0694        | -               |
+| 0.0239 | 6000   | 0.0586        | -               |
+| 0.0278 | 7000   | 0.0539        | -               |
+| 0.0318 | 8000   | 0.0545        | -               |
+| 0.0358 | 9000   | 0.0515        | -               |
+| 0.0398 | 10000  | 0.0493        | -               |
+| 0.0438 | 11000  | 0.0419        | -               |
+| 0.0477 | 12000  | 0.0464        | -               |
+| 0.0517 | 13000  | 0.0494        | -               |
+| 0.0557 | 14000  | 0.0536        | -               |
+| 0.0597 | 15000  | 0.0472        | -               |
+| 0.0636 | 16000  | 0.0945        | -               |
+| 0.0676 | 17000  | 0.0385        | -               |
+| 0.0716 | 18000  | 0.068         | -               |
+| 0.0756 | 19000  | 0.0362        | -               |
+| 0.0796 | 20000  | 0.0865        | -               |
+| 0.0835 | 21000  | 0.0403        | -               |
+| 0.0875 | 22000  | 0.0798        | -               |
+| 0.0915 | 23000  | 0.0421        | -               |
+| 0.0955 | 24000  | 0.0428        | -               |
+| 0.0994 | 25000  | 0.035         | -               |
+| 0.1034 | 26000  | 0.0736        | -               |
+| 0.1074 | 27000  | 0.0395        | -               |
+| 0.1114 | 28000  | 0.0837        | -               |
+| 0.1154 | 29000  | 0.0432        | -               |
+| 0.1193 | 30000  | 0.0695        | -               |
+| 0.1233 | 31000  | 0.0584        | -               |
+| 0.1273 | 32000  | 0.0394        | -               |
+| 0.1313 | 33000  | 0.113         | -               |
+| 0.1353 | 34000  | 0.0349        | -               |
+| 0.1392 | 35000  | 0.044         | -               |
+| 0.1432 | 36000  | 0.0712        | -               |
+| 0.1472 | 37000  | 0.0322        | -               |
+| 0.1512 | 38000  | 0.0628        | -               |
+| 0.1551 | 39000  | 0.035         | -               |
+| 0.1591 | 40000  | 0.0305        | -               |
+| 0.1631 | 41000  | 0.0733        | -               |
+| 0.1671 | 42000  | 0.0449        | -               |
+| 0.1711 | 43000  | 0.0434        | -               |
+| 0.1750 | 44000  | 0.0597        | -               |
+| 0.1790 | 45000  | 0.0464        | -               |
+| 0.1830 | 46000  | 0.0428        | -               |
+| 0.1870 | 47000  | 0.0657        | -               |
+| 0.1909 | 48000  | 0.0346        | -               |
+| 0.1949 | 49000  | 0.0537        | -               |
+| 0.1989 | 50000  | 0.0577        | -               |
+| 0.2029 | 51000  | 0.0349        | -               |
+| 0.2069 | 52000  | 0.0376        | -               |
+| 0.2108 | 53000  | 0.0476        | -               |
+| 0.2148 | 54000  | 0.0453        | -               |
+| 0.2188 | 55000  | 0.0366        | -               |
+| 0.2228 | 56000  | 0.0295        | -               |
+| 0.2267 | 57000  | 0.0427        | -               |
+| 0.2307 | 58000  | 0.0352        | -               |
+| 0.2347 | 59000  | 0.0319        | -               |
+| 0.2387 | 60000  | 0.0316        | -               |
+| 0.2427 | 61000  | 0.0433        | -               |
+| 0.2466 | 62000  | 0.0272        | -               |
+| 0.2506 | 63000  | 0.0253        | -               |
+| 0.2546 | 64000  | 0.0356        | -               |
+| 0.2586 | 65000  | 0.0429        | -               |
+| 0.2625 | 66000  | 0.0301        | -               |
+| 0.2665 | 67000  | 0.0293        | -               |
+| 0.2705 | 68000  | 0.0269        | -               |
+| 0.2745 | 69000  | 0.03          | -               |
+| 0.2785 | 70000  | 0.0585        | -               |
+| 0.2824 | 71000  | 0.05          | -               |
+| 0.2864 | 72000  | 0.0455        | -               |
+| 0.2904 | 73000  | 0.0212        | -               |
+| 0.2944 | 74000  | 0.0296        | -               |
+| 0.2983 | 75000  | 0.043         | -               |
+| 0.3023 | 76000  | 0.0277        | -               |
+| 0.3063 | 77000  | 0.0592        | -               |
+| 0.3103 | 78000  | 0.0247        | -               |
+| 0.3143 | 79000  | 0.046         | -               |
+| 0.3182 | 80000  | 0.0429        | -               |
+| 0.3222 | 81000  | 0.0306        | -               |
+| 0.3262 | 82000  | 0.0313        | -               |
+| 0.3302 | 83000  | 0.0386        | -               |
+| 0.3342 | 84000  | 0.0196        | -               |
+| 0.3381 | 85000  | 0.0353        | -               |
+| 0.3421 | 86000  | 0.0462        | -               |
+| 0.3461 | 87000  | 0.0277        | -               |
+| 0.3501 | 88000  | 0.0461        | -               |
+| 0.3540 | 89000  | 0.0265        | -               |
+| 0.3580 | 90000  | 0.0159        | -               |
+| 0.3620 | 91000  | 0.0201        | -               |
+| 0.3660 | 92000  | 0.031         | -               |
+| 0.3700 | 93000  | 0.0337        | -               |
+| 0.3739 | 94000  | 0.0369        | -               |
+| 0.3779 | 95000  | 0.0504        | -               |
+| 0.3819 | 96000  | 0.0254        | -               |
+| 0.3859 | 97000  | 0.0265        | -               |
+| 0.3898 | 98000  | 0.0205        | -               |
+| 0.3938 | 99000  | 0.0181        | -               |
+| 0.3978 | 100000 | 0.0242        | -               |
+| 0.4018 | 101000 | 0.0317        | -               |
+| 0.4058 | 102000 | 0.0248        | -               |
+| 0.4097 | 103000 | 0.0171        | -               |
+| 0.4137 | 104000 | 0.0183        | -               |
+| 0.4177 | 105000 | 0.0156        | -               |
+| 0.4217 | 106000 | 0.0217        | -               |
+| 0.4256 | 107000 | 0.0282        | -               |
+| 0.4296 | 108000 | 0.0381        | -               |
+| 0.4336 | 109000 | 0.0271        | -               |
+| 0.4376 | 110000 | 0.0165        | -               |
+| 0.4416 | 111000 | 0.01          | -               |
+| 0.4455 | 112000 | 0.0241        | -               |
+| 0.4495 | 113000 | 0.0226        | -               |
+| 0.4535 | 114000 | 0.0161        | -               |
+| 0.4575 | 115000 | 0.0172        | -               |
+| 0.4614 | 116000 | 0.0129        | -               |
+| 0.4654 | 117000 | 0.0147        | -               |
+| 0.4694 | 118000 | 0.0346        | -               |
+| 0.4734 | 119000 | 0.039         | -               |
+| 0.4774 | 120000 | 0.0348        | -               |
+| 0.4813 | 121000 | 0.0353        | -               |
+| 0.4853 | 122000 | 0.0178        | -               |
+| 0.4893 | 123000 | 0.0173        | -               |
+| 0.4933 | 124000 | 0.0197        | -               |
+| 0.4972 | 125000 | 0.0148        | -               |
+| 0.5012 | 126000 | 0.014         | -               |
+| 0.5052 | 127000 | 0.0186        | -               |
+| 0.5092 | 128000 | 0.0129        | -               |
+| 0.5132 | 129000 | 0.0116        | -               |
+| 0.5171 | 130000 | 0.0186        | -               |
+| 0.5211 | 131000 | 0.0332        | -               |
+| 0.5251 | 132000 | 0.0195        | -               |
+| 0.5291 | 133000 | 0.0163        | -               |
+| 0.5331 | 134000 | 0.0145        | -               |
+| 0.5370 | 135000 | 0.0236        | -               |
+| 0.5410 | 136000 | 0.0169        | -               |
+| 0.5450 | 137000 | 0.0327        | -               |
+| 0.5490 | 138000 | 0.0332        | -               |
+| 0.5529 | 139000 | 0.034         | -               |
+| 0.5569 | 140000 | 0.0317        | -               |
+| 0.5609 | 141000 | 0.0372        | -               |
+| 0.5649 | 142000 | 0.0246        | -               |
+| 0.5689 | 143000 | 0.0278        | -               |
+| 0.5728 | 144000 | 0.0196        | -               |
+| 0.5768 | 145000 | 0.0217        | -               |
+| 0.5808 | 146000 | 0.0223        | -               |
+| 0.5848 | 147000 | 0.0138        | -               |
+| 0.5887 | 148000 | 0.0114        | -               |
+| 0.5927 | 149000 | 0.0122        | -               |
+| 0.5967 | 150000 | 0.0199        | -               |
+| 0.6007 | 151000 | 0.0204        | -               |
+| 0.6047 | 152000 | 0.0155        | -               |
+| 0.6086 | 153000 | 0.015         | -               |
+| 0.6126 | 154000 | 0.0196        | -               |
+| 0.6166 | 155000 | 0.0183        | -               |
+| 0.6206 | 156000 | 0.0225        | -               |
+| 0.6245 | 157000 | 0.0232        | -               |
+| 0.6285 | 158000 | 0.0389        | -               |
+| 0.6325 | 159000 | 0.0267        | -               |
+| 0.6365 | 160000 | 0.0264        | -               |
+| 0.6405 | 161000 | 0.0123        | -               |
+| 0.6444 | 162000 | 0.0144        | -               |
+| 0.6484 | 163000 | 0.018         | -               |
+| 0.6524 | 164000 | 0.0327        | -               |
+| 0.6564 | 165000 | 0.0283        | -               |
+| 0.6603 | 166000 | 0.0357        | -               |
+| 0.6643 | 167000 | 0.0148        | -               |
+| 0.6683 | 168000 | 0.0137        | -               |
+| 0.6723 | 169000 | 0.0165        | -               |
+| 0.6763 | 170000 | 0.0237        | -               |
+| 0.6802 | 171000 | 0.0218        | -               |
+| 0.6842 | 172000 | 0.0143        | -               |
+| 0.6882 | 173000 | 0.027         | -               |
+| 0.6922 | 174000 | 0.025         | -               |
+| 0.6961 | 175000 | 0.0211        | -               |
+| 0.7001 | 176000 | 0.0191        | -               |
+| 0.7041 | 177000 | 0.0213        | -               |
+| 0.7081 | 178000 | 0.0177        | -               |
+| 0.7121 | 179000 | 0.0178        | -               |
+| 0.7160 | 180000 | 0.0263        | -               |
+| 0.7200 | 181000 | 0.0263        | -               |
+| 0.7240 | 182000 | 0.0265        | -               |
+| 0.7280 | 183000 | 0.0236        | -               |
+| 0.7320 | 184000 | 0.0183        | -               |
+| 0.7359 | 185000 | 0.012         | -               |
+| 0.7399 | 186000 | 0.0192        | -               |
+| 0.7439 | 187000 | 0.0221        | -               |
+| 0.7479 | 188000 | 0.0223        | -               |
+| 0.7518 | 189000 | 0.021         | -               |
+| 0.7558 | 190000 | 0.0234        | -               |
+| 0.7598 | 191000 | 0.0221        | -               |
+| 0.7638 | 192000 | 0.0246        | -               |
+| 0.7678 | 193000 | 0.0212        | -               |
+| 0.7717 | 194000 | 0.0191        | -               |
+| 0.7757 | 195000 | 0.0122        | -               |
+| 0.7797 | 196000 | 0.0111        | -               |
+| 0.7837 | 197000 | 0.0094        | -               |
+| 0.7876 | 198000 | 0.0107        | -               |
+| 0.7916 | 199000 | 0.0103        | -               |
+| 0.7956 | 200000 | 0.0093        | -               |
+| 0.7996 | 201000 | 0.0128        | -               |
+| 0.8036 | 202000 | 0.0104        | -               |
+| 0.8075 | 203000 | 0.0161        | -               |
+| 0.8115 | 204000 | 0.0221        | -               |
+| 0.8155 | 205000 | 0.0243        | -               |
+| 0.8195 | 206000 | 0.0209        | -               |
+| 0.8234 | 207000 | 0.0241        | -               |
+| 0.8274 | 208000 | 0.0224        | -               |
+| 0.8314 | 209000 | 0.0131        | -               |
+| 0.8354 | 210000 | 0.0105        | -               |
+| 0.8394 | 211000 | 0.0118        | -               |
+| 0.8433 | 212000 | 0.0122        | -               |
+| 0.8473 | 213000 | 0.0112        | -               |
+| 0.8513 | 214000 | 0.0113        | -               |
+| 0.8553 | 215000 | 0.0108        | -               |
+| 0.8592 | 216000 | 0.0117        | -               |
+| 0.8632 | 217000 | 0.0111        | -               |
+| 0.8672 | 218000 | 0.0123        | -               |
+| 0.8712 | 219000 | 0.0112        | -               |
+| 0.8752 | 220000 | 0.0109        | -               |
+| 0.8791 | 221000 | 0.011         | -               |
+| 0.8831 | 222000 | 0.0122        | -               |
+| 0.8871 | 223000 | 0.0287        | -               |
+| 0.8911 | 224000 | 0.0234        | -               |
+| 0.8950 | 225000 | 0.0234        | -               |
+| 0.8990 | 226000 | 0.0222        | -               |
+| 0.9030 | 227000 | 0.0193        | -               |
+| 0.9070 | 228000 | 0.0166        | -               |
+| 0.9110 | 229000 | 0.0113        | -               |
+| 0.9149 | 230000 | 0.012         | -               |
+| 0.9189 | 231000 | 0.0108        | -               |
+| 0.9229 | 232000 | 0.0106        | -               |
+| 0.9269 | 233000 | 0.0107        | -               |
+| 0.9309 | 234000 | 0.0105        | -               |
+| 0.9348 | 235000 | 0.0091        | -               |
+| 0.9388 | 236000 | 0.0095        | -               |
+| 0.9428 | 237000 | 0.0066        | -               |
+| 0.9468 | 238000 | 0.0093        | -               |
+| 0.9507 | 239000 | 0.0049        | -               |
+| 0.9547 | 240000 | 0.0058        | -               |
+| 0.9587 | 241000 | 0.0065        | -               |
+| 0.9627 | 242000 | 0.0144        | -               |
+| 0.9667 | 243000 | 0.0181        | -               |
+| 0.9706 | 244000 | 0.0105        | -               |
+| 0.9746 | 245000 | 0.0066        | -               |
+| 0.9786 | 246000 | 0.0057        | -               |
+| 0.9826 | 247000 | 0.0053        | -               |
+| 0.9865 | 248000 | 0.005         | -               |
+| 0.9905 | 249000 | 0.006         | -               |
+| 0.9945 | 250000 | 0.0047        | -               |
+| 0.9985 | 251000 | 0.0055        | -               |
+| 1.0000 | 251382 | -             | 0.0021          |
+| 1.0025 | 252000 | 0.2602        | -               |
+| 1.0064 | 253000 | 0.0967        | -               |
+| 1.0104 | 254000 | 0.0643        | -               |
+| 1.0144 | 255000 | 0.057         | -               |
+| 1.0184 | 256000 | 0.0614        | -               |
+| 1.0223 | 257000 | 0.062         | -               |
+| 1.0263 | 258000 | 0.0471        | -               |
+| 1.0303 | 259000 | 0.0445        | -               |
+| 1.0343 | 260000 | 0.0439        | -               |
+| 1.0383 | 261000 | 0.0339        | -               |
+| 1.0422 | 262000 | 0.0376        | -               |
+| 1.0462 | 263000 | 0.0445        | -               |
+| 1.0502 | 264000 | 0.0331        | -               |
+| 1.0542 | 265000 | 0.0392        | -               |
+| 1.0581 | 266000 | 0.0539        | -               |
+| 1.0621 | 267000 | 0.0595        | -               |
+| 1.0661 | 268000 | 0.0595        | -               |
+| 1.0701 | 269000 | 0.0472        | -               |
+| 1.0741 | 270000 | 0.0421        | -               |
+| 1.0780 | 271000 | 0.0705        | -               |
+| 1.0820 | 272000 | 0.0343        | -               |
+| 1.0860 | 273000 | 0.0702        | -               |
+| 1.0900 | 274000 | 0.0385        | -               |
+| 1.0939 | 275000 | 0.0348        | -               |
+| 1.0979 | 276000 | 0.0338        | -               |
+| 1.1019 | 277000 | 0.065         | -               |
+| 1.1059 | 278000 | 0.032         | -               |
+| 1.1099 | 279000 | 0.0318        | -               |
+| 1.1138 | 280000 | 0.0768        | -               |
+| 1.1178 | 281000 | 0.0372        | -               |
+| 1.1218 | 282000 | 0.0771        | -               |
+| 1.1258 | 283000 | 0.0346        | -               |
+| 1.1298 | 284000 | 0.0781        | -               |
+| 1.1337 | 285000 | 0.0528        | -               |
+| 1.1377 | 286000 | 0.0282        | -               |
+| 1.1417 | 287000 | 0.0723        | -               |
+| 1.1457 | 288000 | 0.0286        | -               |
+| 1.1496 | 289000 | 0.0403        | -               |
+| 1.1536 | 290000 | 0.0439        | -               |
+| 1.1576 | 291000 | 0.0286        | -               |
+| 1.1616 | 292000 | 0.0517        | -               |
+| 1.1656 | 293000 | 0.0504        | -               |
+| 1.1695 | 294000 | 0.0348        | -               |
+| 1.1735 | 295000 | 0.0537        | -               |
+| 1.1775 | 296000 | 0.0364        | -               |
+| 1.1815 | 297000 | 0.04          | -               |
+| 1.1854 | 298000 | 0.0587        | -               |
+| 1.1894 | 299000 | 0.0332        | -               |
+| 1.1934 | 300000 | 0.0429        | -               |
+| 1.1974 | 301000 | 0.0522        | -               |
+| 1.2014 | 302000 | 0.0348        | -               |
+| 1.2053 | 303000 | 0.0305        | -               |
+| 1.2093 | 304000 | 0.0319        | -               |
+| 1.2133 | 305000 | 0.0493        | -               |
+| 1.2173 | 306000 | 0.0375        | -               |
+| 1.2212 | 307000 | 0.024         | -               |
+| 1.2252 | 308000 | 0.0327        | -               |
+| 1.2292 | 309000 | 0.0356        | -               |
+| 1.2332 | 310000 | 0.0296        | -               |
+| 1.2372 | 311000 | 0.0259        | -               |
+| 1.2411 | 312000 | 0.0358        | -               |
+| 1.2451 | 313000 | 0.0263        | -               |
+| 1.2491 | 314000 | 0.0252        | -               |
+| 1.2531 | 315000 | 0.0251        | -               |
+| 1.2570 | 316000 | 0.0298        | -               |
+| 1.2610 | 317000 | 0.0393        | -               |
+| 1.2650 | 318000 | 0.0261        | -               |
+| 1.2690 | 319000 | 0.0198        | -               |
+| 1.2730 | 320000 | 0.0271        | -               |
+| 1.2769 | 321000 | 0.048         | -               |
+| 1.2809 | 322000 | 0.0421        | -               |
+| 1.2849 | 323000 | 0.0483        | -               |
+| 1.2889 | 324000 | 0.0173        | -               |
+| 1.2928 | 325000 | 0.0174        | -               |
+| 1.2968 | 326000 | 0.0375        | -               |
+| 1.3008 | 327000 | 0.0261        | -               |
+| 1.3048 | 328000 | 0.0563        | -               |
+| 1.3088 | 329000 | 0.0238        | -               |
+| 1.3127 | 330000 | 0.02          | -               |
+| 1.3167 | 331000 | 0.0495        | -               |
+| 1.3207 | 332000 | 0.0218        | -               |
+| 1.3247 | 333000 | 0.031         | -               |
+| 1.3286 | 334000 | 0.0366        | -               |
+| 1.3326 | 335000 | 0.0188        | -               |
+| 1.3366 | 336000 | 0.0179        | -               |
+| 1.3406 | 337000 | 0.0547        | -               |
+| 1.3446 | 338000 | 0.0197        | -               |
+| 1.3485 | 339000 | 0.0372        | -               |
+| 1.3525 | 340000 | 0.0327        | -               |
+| 1.3565 | 341000 | 0.0131        | -               |
+| 1.3605 | 342000 | 0.019         | -               |
+| 1.3645 | 343000 | 0.0119        | -               |
+| 1.3684 | 344000 | 0.038         | -               |
+| 1.3724 | 345000 | 0.0324        | -               |
+| 1.3764 | 346000 | 0.0495        | -               |
+| 1.3804 | 347000 | 0.0196        | -               |
+| 1.3843 | 348000 | 0.0256        | -               |
+| 1.3883 | 349000 | 0.0176        | -               |
+| 1.3923 | 350000 | 0.0195        | -               |
+| 1.3963 | 351000 | 0.0157        | -               |
+| 1.4003 | 352000 | 0.0267        | -               |
+| 1.4042 | 353000 | 0.0285        | -               |
+| 1.4082 | 354000 | 0.0145        | -               |
+| 1.4122 | 355000 | 0.0183        | -               |
+| 1.4162 | 356000 | 0.012         | -               |
+| 1.4201 | 357000 | 0.0175        | -               |
+| 1.4241 | 358000 | 0.022         | -               |
+| 1.4281 | 359000 | 0.028         | -               |
+| 1.4321 | 360000 | 0.0319        | -               |
+| 1.4361 | 361000 | 0.0157        | -               |
+| 1.4400 | 362000 | 0.0107        | -               |
+| 1.4440 | 363000 | 0.0158        | -               |
+| 1.4480 | 364000 | 0.0209        | -               |
+| 1.4520 | 365000 | 0.0168        | -               |
+| 1.4559 | 366000 | 0.0125        | -               |
+| 1.4599 | 367000 | 0.0151        | -               |
+| 1.4639 | 368000 | 0.0106        | -               |
+| 1.4679 | 369000 | 0.0232        | -               |
+| 1.4719 | 370000 | 0.0318        | -               |
+| 1.4758 | 371000 | 0.031         | -               |
+| 1.4798 | 372000 | 0.0314        | -               |
+| 1.4838 | 373000 | 0.023         | -               |
+| 1.4878 | 374000 | 0.0151        | -               |
+| 1.4917 | 375000 | 0.0144        | -               |
+| 1.4957 | 376000 | 0.0165        | -               |
+| 1.4997 | 377000 | 0.011         | -               |
+| 1.5037 | 378000 | 0.0138        | -               |
+| 1.5077 | 379000 | 0.0149        | -               |
+| 1.5116 | 380000 | 0.0087        | -               |
+| 1.5156 | 381000 | 0.0154        | -               |
+| 1.5196 | 382000 | 0.0245        | -               |
+| 1.5236 | 383000 | 0.0199        | -               |
+| 1.5275 | 384000 | 0.0174        | -               |
+| 1.5315 | 385000 | 0.0103        | -               |
+| 1.5355 | 386000 | 0.018         | -               |
+| 1.5395 | 387000 | 0.0166        | -               |
+| 1.5435 | 388000 | 0.0249        | -               |
+| 1.5474 | 389000 | 0.028         | -               |
+| 1.5514 | 390000 | 0.0306        | -               |
+| 1.5554 | 391000 | 0.0264        | -               |
+| 1.5594 | 392000 | 0.0325        | -               |
+| 1.5634 | 393000 | 0.0282        | -               |
+| 1.5673 | 394000 | 0.0189        | -               |
+| 1.5713 | 395000 | 0.0246        | -               |
+| 1.5753 | 396000 | 0.0189        | -               |
+| 1.5793 | 397000 | 0.0192        | -               |
+| 1.5832 | 398000 | 0.0155        | -               |
+| 1.5872 | 399000 | 0.0108        | -               |
+| 1.5912 | 400000 | 0.0085        | -               |
+| 1.5952 | 401000 | 0.0171        | -               |
+| 1.5992 | 402000 | 0.0176        | -               |
+| 1.6031 | 403000 | 0.0159        | -               |
+| 1.6071 | 404000 | 0.0127        | -               |
+| 1.6111 | 405000 | 0.016         | -               |
+| 1.6151 | 406000 | 0.0169        | -               |
+| 1.6190 | 407000 | 0.0199        | -               |
+| 1.6230 | 408000 | 0.0149        | -               |
+| 1.6270 | 409000 | 0.0364        | -               |
+| 1.6310 | 410000 | 0.0259        | -               |
+| 1.6350 | 411000 | 0.0294        | -               |
+| 1.6389 | 412000 | 0.0109        | -               |
+| 1.6429 | 413000 | 0.0132        | -               |
+| 1.6469 | 414000 | 0.0109        | -               |
+| 1.6509 | 415000 | 0.0269        | -               |
+| 1.6548 | 416000 | 0.0259        | -               |
+| 1.6588 | 417000 | 0.0304        | -               |
+| 1.6628 | 418000 | 0.0216        | -               |
+| 1.6668 | 419000 | 0.0133        | -               |
+| 1.6708 | 420000 | 0.0125        | -               |
+| 1.6747 | 421000 | 0.0197        | -               |
+| 1.6787 | 422000 | 0.0211        | -               |
+| 1.6827 | 423000 | 0.015         | -               |
+| 1.6867 | 424000 | 0.0183        | -               |
+| 1.6906 | 425000 | 0.0262        | -               |
+| 1.6946 | 426000 | 0.0217        | -               |
+| 1.6986 | 427000 | 0.0163        | -               |
+| 1.7026 | 428000 | 0.0201        | -               |
+| 1.7066 | 429000 | 0.0188        | -               |
+| 1.7105 | 430000 | 0.015         | -               |
+| 1.7145 | 431000 | 0.019         | -               |
+| 1.7185 | 432000 | 0.0271        | -               |
+| 1.7225 | 433000 | 0.0236        | -               |
+| 1.7264 | 434000 | 0.0239        | -               |
+| 1.7304 | 435000 | 0.0173        | -               |
+| 1.7344 | 436000 | 0.0159        | -               |
+| 1.7384 | 437000 | 0.0143        | -               |
+| 1.7424 | 438000 | 0.0176        | -               |
+| 1.7463 | 439000 | 0.0183        | -               |
+| 1.7503 | 440000 | 0.0204        | -               |
+| 1.7543 | 441000 | 0.0216        | -               |
+| 1.7583 | 442000 | 0.0196        | -               |
+| 1.7623 | 443000 | 0.0215        | -               |
+| 1.7662 | 444000 | 0.021         | -               |
+| 1.7702 | 445000 | 0.0197        | -               |
+| 1.7742 | 446000 | 0.0131        | -               |
+| 1.7782 | 447000 | 0.0107        | -               |
+| 1.7821 | 448000 | 0.0079        | -               |
+| 1.7861 | 449000 | 0.01          | -               |
+| 1.7901 | 450000 | 0.0097        | -               |
+| 1.7941 | 451000 | 0.0079        | -               |
+| 1.7981 | 452000 | 0.0105        | -               |
+| 1.8020 | 453000 | 0.01          | -               |
+| 1.8060 | 454000 | 0.0103        | -               |
+| 1.8100 | 455000 | 0.0217        | -               |
+| 1.8140 | 456000 | 0.0204        | -               |
+| 1.8179 | 457000 | 0.0206        | -               |
+| 1.8219 | 458000 | 0.0218        | -               |
+| 1.8259 | 459000 | 0.0207        | -               |
+| 1.8299 | 460000 | 0.0187        | -               |
+| 1.8339 | 461000 | 0.0083        | -               |
+| 1.8378 | 462000 | 0.0104        | -               |
+| 1.8418 | 463000 | 0.0119        | -               |
+| 1.8458 | 464000 | 0.01          | -               |
+| 1.8498 | 465000 | 0.0108        | -               |
+| 1.8537 | 466000 | 0.0101        | -               |
+| 1.8577 | 467000 | 0.0106        | -               |
+| 1.8617 | 468000 | 0.0098        | -               |
+| 1.8657 | 469000 | 0.0108        | -               |
+| 1.8697 | 470000 | 0.0109        | -               |
+| 1.8736 | 471000 | 0.0104        | -               |
+| 1.8776 | 472000 | 0.0098        | -               |
+| 1.8816 | 473000 | 0.0097        | -               |
+| 1.8856 | 474000 | 0.0244        | -               |
+| 1.8895 | 475000 | 0.019         | -               |
+| 1.8935 | 476000 | 0.0238        | -               |
+| 1.8975 | 477000 | 0.0207        | -               |
+| 1.9015 | 478000 | 0.0198        | -               |
+| 1.9055 | 479000 | 0.0184        | -               |
+| 1.9094 | 480000 | 0.0124        | -               |
+| 1.9134 | 481000 | 0.0106        | -               |
+| 1.9174 | 482000 | 0.0113        | -               |
+| 1.9214 | 483000 | 0.0095        | -               |
+| 1.9253 | 484000 | 0.0106        | -               |
+| 1.9293 | 485000 | 0.0097        | -               |
+| 1.9333 | 486000 | 0.0094        | -               |
+| 1.9373 | 487000 | 0.0088        | -               |
+| 1.9413 | 488000 | 0.0076        | -               |
+| 1.9452 | 489000 | 0.0095        | -               |
+| 1.9492 | 490000 | 0.005         | -               |
+| 1.9532 | 491000 | 0.0048        | -               |
+| 1.9572 | 492000 | 0.0063        | -               |
+| 1.9612 | 493000 | 0.0088        | -               |
+| 1.9651 | 494000 | 0.0191        | -               |
+| 1.9691 | 495000 | 0.0137        | -               |
+| 1.9731 | 496000 | 0.0067        | -               |
+| 1.9771 | 497000 | 0.0062        | -               |
+| 1.9810 | 498000 | 0.0056        | -               |
+| 1.9850 | 499000 | 0.0049        | -               |
+| 1.9890 | 500000 | 0.0064        | -               |
+| 1.9930 | 501000 | 0.0047        | -               |
+| 1.9970 | 502000 | 0.0051        | -               |
+| 2.0000 | 502764 | -             | 0.0012          |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### 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/scripts/data/Bioformer-16L-UMLS-Pubmed_PMC-Forward_TCE/Bioformer-16L-UMLS-Pubmed_PMC-Forward_TCE-Epoch-3",
+  "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": 32768
+}

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": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5070870b97247ca36b70358be47f7b77fe02430767c9b059fbed94cffaf0beb
+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 +1,65 @@
+{
+  "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,
+  "extra_special_tokens": {},
+  "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]"
+}