sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:33870508 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
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.'
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained on the parquet dataset. It maps sentences & paragraphs to a 512-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:** 512 tokens
- **Output Dimensionality:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 512, '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-8L-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, 512]

# 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: 282.68 tokens</li><li>max: 512 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: 279.56 tokens</li><li>max: 512 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
- `max_steps`: 754146
- `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`: 3
- `max_steps`: 754146
- `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      | 4.0337        | -               |
| 0.0040 | 1000   | 1.0731        | -               |
| 0.0080 | 2000   | 0.2694        | -               |
| 0.0119 | 3000   | 0.204         | -               |
| 0.0159 | 4000   | 0.2225        | -               |
| 0.0199 | 5000   | 0.1825        | -               |
| 0.0239 | 6000   | 0.1539        | -               |
| 0.0278 | 7000   | 0.1408        | -               |
| 0.0318 | 8000   | 0.1364        | -               |
| 0.0358 | 9000   | 0.1276        | -               |
| 0.0398 | 10000  | 0.1185        | -               |
| 0.0438 | 11000  | 0.1028        | -               |
| 0.0477 | 12000  | 0.1086        | -               |
| 0.0517 | 13000  | 0.1172        | -               |
| 0.0557 | 14000  | 0.1129        | -               |
| 0.0597 | 15000  | 0.1063        | -               |
| 0.0636 | 16000  | 0.1774        | -               |
| 0.0676 | 17000  | 0.0872        | -               |
| 0.0716 | 18000  | 0.1404        | -               |
| 0.0756 | 19000  | 0.0798        | -               |
| 0.0796 | 20000  | 0.1644        | -               |
| 0.0835 | 21000  | 0.0848        | -               |
| 0.0875 | 22000  | 0.1551        | -               |
| 0.0915 | 23000  | 0.0884        | -               |
| 0.0955 | 24000  | 0.0923        | -               |
| 0.0994 | 25000  | 0.0754        | -               |
| 0.1034 | 26000  | 0.1395        | -               |
| 0.1074 | 27000  | 0.0813        | -               |
| 0.1114 | 28000  | 0.1574        | -               |
| 0.1154 | 29000  | 0.09          | -               |
| 0.1193 | 30000  | 0.1319        | -               |
| 0.1233 | 31000  | 0.116         | -               |
| 0.1273 | 32000  | 0.0806        | -               |
| 0.1313 | 33000  | 0.2095        | -               |
| 0.1353 | 34000  | 0.0716        | -               |
| 0.1392 | 35000  | 0.0909        | -               |
| 0.1432 | 36000  | 0.1333        | -               |
| 0.1472 | 37000  | 0.067         | -               |
| 0.1512 | 38000  | 0.1183        | -               |
| 0.1551 | 39000  | 0.0739        | -               |
| 0.1591 | 40000  | 0.0662        | -               |
| 0.1631 | 41000  | 0.1371        | -               |
| 0.1671 | 42000  | 0.0913        | -               |
| 0.1711 | 43000  | 0.0867        | -               |
| 0.1750 | 44000  | 0.1184        | -               |
| 0.1790 | 45000  | 0.0913        | -               |
| 0.1830 | 46000  | 0.0857        | -               |
| 0.1870 | 47000  | 0.1223        | -               |
| 0.1909 | 48000  | 0.0731        | -               |
| 0.1949 | 49000  | 0.1028        | -               |
| 0.1989 | 50000  | 0.1107        | -               |
| 0.2029 | 51000  | 0.0726        | -               |
| 0.2069 | 52000  | 0.076         | -               |
| 0.2108 | 53000  | 0.0923        | -               |
| 0.2148 | 54000  | 0.0896        | -               |
| 0.2188 | 55000  | 0.0755        | -               |
| 0.2228 | 56000  | 0.0627        | -               |
| 0.2267 | 57000  | 0.0837        | -               |
| 0.2307 | 58000  | 0.0732        | -               |
| 0.2347 | 59000  | 0.0655        | -               |
| 0.2387 | 60000  | 0.0653        | -               |
| 0.2427 | 61000  | 0.0845        | -               |
| 0.2466 | 62000  | 0.0568        | -               |
| 0.2506 | 63000  | 0.0534        | -               |
| 0.2546 | 64000  | 0.0723        | -               |
| 0.2586 | 65000  | 0.0873        | -               |
| 0.2625 | 66000  | 0.0615        | -               |
| 0.2665 | 67000  | 0.0598        | -               |
| 0.2705 | 68000  | 0.0573        | -               |
| 0.2745 | 69000  | 0.062         | -               |
| 0.2785 | 70000  | 0.1152        | -               |
| 0.2824 | 71000  | 0.0945        | -               |
| 0.2864 | 72000  | 0.0853        | -               |
| 0.2904 | 73000  | 0.0457        | -               |
| 0.2944 | 74000  | 0.0604        | -               |
| 0.2983 | 75000  | 0.1008        | -               |
| 0.3023 | 76000  | 0.0564        | -               |
| 0.3063 | 77000  | 0.1009        | -               |
| 0.3103 | 78000  | 0.0531        | -               |
| 0.3143 | 79000  | 0.0966        | -               |
| 0.3182 | 80000  | 0.0991        | -               |
| 0.3222 | 81000  | 0.0617        | -               |
| 0.3262 | 82000  | 0.0685        | -               |
| 0.3302 | 83000  | 0.0895        | -               |
| 0.3342 | 84000  | 0.0424        | -               |
| 0.3381 | 85000  | 0.0646        | -               |
| 0.3421 | 86000  | 0.0796        | -               |
| 0.3461 | 87000  | 0.0599        | -               |
| 0.3501 | 88000  | 0.1033        | -               |
| 0.3540 | 89000  | 0.0475        | -               |
| 0.3580 | 90000  | 0.0366        | -               |
| 0.3620 | 91000  | 0.0402        | -               |
| 0.3660 | 92000  | 0.0587        | -               |
| 0.3700 | 93000  | 0.0661        | -               |
| 0.3739 | 94000  | 0.077         | -               |
| 0.3779 | 95000  | 0.0906        | -               |
| 0.3819 | 96000  | 0.05          | -               |
| 0.3859 | 97000  | 0.0505        | -               |
| 0.3898 | 98000  | 0.0413        | -               |
| 0.3938 | 99000  | 0.038         | -               |
| 0.3978 | 100000 | 0.0478        | -               |
| 0.4018 | 101000 | 0.073         | -               |
| 0.4058 | 102000 | 0.0527        | -               |
| 0.4097 | 103000 | 0.0351        | -               |
| 0.4137 | 104000 | 0.0377        | -               |
| 0.4177 | 105000 | 0.0347        | -               |
| 0.4217 | 106000 | 0.0431        | -               |
| 0.4256 | 107000 | 0.0613        | -               |
| 0.4296 | 108000 | 0.0825        | -               |
| 0.4336 | 109000 | 0.0546        | -               |
| 0.4376 | 110000 | 0.0335        | -               |
| 0.4416 | 111000 | 0.0232        | -               |
| 0.4455 | 112000 | 0.0525        | -               |
| 0.4495 | 113000 | 0.0473        | -               |
| 0.4535 | 114000 | 0.0342        | -               |
| 0.4575 | 115000 | 0.0346        | -               |
| 0.4614 | 116000 | 0.0279        | -               |
| 0.4654 | 117000 | 0.034         | -               |
| 0.4694 | 118000 | 0.0778        | -               |
| 0.4734 | 119000 | 0.0788        | -               |
| 0.4774 | 120000 | 0.0703        | -               |
| 0.4813 | 121000 | 0.0708        | -               |
| 0.4853 | 122000 | 0.0393        | -               |
| 0.4893 | 123000 | 0.037         | -               |
| 0.4933 | 124000 | 0.0426        | -               |
| 0.4972 | 125000 | 0.0335        | -               |
| 0.5012 | 126000 | 0.0317        | -               |
| 0.5052 | 127000 | 0.0406        | -               |
| 0.5092 | 128000 | 0.0302        | -               |
| 0.5132 | 129000 | 0.0284        | -               |
| 0.5171 | 130000 | 0.0416        | -               |
| 0.5211 | 131000 | 0.065         | -               |
| 0.5251 | 132000 | 0.0402        | -               |
| 0.5291 | 133000 | 0.0348        | -               |
| 0.5331 | 134000 | 0.033         | -               |
| 0.5370 | 135000 | 0.0485        | -               |
| 0.5410 | 136000 | 0.0364        | -               |
| 0.5450 | 137000 | 0.0686        | -               |
| 0.5490 | 138000 | 0.0648        | -               |
| 0.5529 | 139000 | 0.0652        | -               |
| 0.5569 | 140000 | 0.0626        | -               |
| 0.5609 | 141000 | 0.0684        | -               |
| 0.5649 | 142000 | 0.0482        | -               |
| 0.5689 | 143000 | 0.0517        | -               |
| 0.5728 | 144000 | 0.0389        | -               |
| 0.5768 | 145000 | 0.0435        | -               |
| 0.5808 | 146000 | 0.044         | -               |
| 0.5848 | 147000 | 0.03          | -               |
| 0.5887 | 148000 | 0.0254        | -               |
| 0.5927 | 149000 | 0.0268        | -               |
| 0.5967 | 150000 | 0.0409        | -               |
| 0.6007 | 151000 | 0.0401        | -               |
| 0.6047 | 152000 | 0.0317        | -               |
| 0.6086 | 153000 | 0.0309        | -               |
| 0.6126 | 154000 | 0.0389        | -               |
| 0.6166 | 155000 | 0.0368        | -               |
| 0.6206 | 156000 | 0.0434        | -               |
| 0.6245 | 157000 | 0.0469        | -               |
| 0.6285 | 158000 | 0.0734        | -               |
| 0.6325 | 159000 | 0.0544        | -               |
| 0.6365 | 160000 | 0.0498        | -               |
| 0.6405 | 161000 | 0.0256        | -               |
| 0.6444 | 162000 | 0.0302        | -               |
| 0.6484 | 163000 | 0.0398        | -               |
| 0.6524 | 164000 | 0.0657        | -               |
| 0.6564 | 165000 | 0.0548        | -               |
| 0.6603 | 166000 | 0.0638        | -               |
| 0.6643 | 167000 | 0.0288        | -               |
| 0.6683 | 168000 | 0.0273        | -               |
| 0.6723 | 169000 | 0.0323        | -               |
| 0.6763 | 170000 | 0.045         | -               |
| 0.6802 | 171000 | 0.0416        | -               |
| 0.6842 | 172000 | 0.0281        | -               |
| 0.6882 | 173000 | 0.0554        | -               |
| 0.6922 | 174000 | 0.0435        | -               |
| 0.6961 | 175000 | 0.0375        | -               |
| 0.7001 | 176000 | 0.0354        | -               |
| 0.7041 | 177000 | 0.038         | -               |
| 0.7081 | 178000 | 0.0319        | -               |
| 0.7121 | 179000 | 0.0329        | -               |
| 0.7160 | 180000 | 0.0492        | -               |
| 0.7200 | 181000 | 0.0545        | -               |
| 0.7240 | 182000 | 0.051         | -               |
| 0.7280 | 183000 | 0.045         | -               |
| 0.7320 | 184000 | 0.0342        | -               |
| 0.7359 | 185000 | 0.0237        | -               |
| 0.7399 | 186000 | 0.0369        | -               |
| 0.7439 | 187000 | 0.0437        | -               |
| 0.7479 | 188000 | 0.0467        | -               |
| 0.7518 | 189000 | 0.0424        | -               |
| 0.7558 | 190000 | 0.0458        | -               |
| 0.7598 | 191000 | 0.0434        | -               |
| 0.7638 | 192000 | 0.0471        | -               |
| 0.7678 | 193000 | 0.0404        | -               |
| 0.7717 | 194000 | 0.0373        | -               |
| 0.7757 | 195000 | 0.0254        | -               |
| 0.7797 | 196000 | 0.0235        | -               |
| 0.7837 | 197000 | 0.0212        | -               |
| 0.7876 | 198000 | 0.0236        | -               |
| 0.7916 | 199000 | 0.0226        | -               |
| 0.7956 | 200000 | 0.0208        | -               |
| 0.7996 | 201000 | 0.0276        | -               |
| 0.8036 | 202000 | 0.0235        | -               |
| 0.8075 | 203000 | 0.0358        | -               |
| 0.8115 | 204000 | 0.0451        | -               |
| 0.8155 | 205000 | 0.0442        | -               |
| 0.8195 | 206000 | 0.0411        | -               |
| 0.8234 | 207000 | 0.0447        | -               |
| 0.8274 | 208000 | 0.0427        | -               |
| 0.8314 | 209000 | 0.0259        | -               |
| 0.8354 | 210000 | 0.0219        | -               |
| 0.8394 | 211000 | 0.0253        | -               |
| 0.8433 | 212000 | 0.0253        | -               |
| 0.8473 | 213000 | 0.0242        | -               |
| 0.8513 | 214000 | 0.0251        | -               |
| 0.8553 | 215000 | 0.0223        | -               |
| 0.8592 | 216000 | 0.0253        | -               |
| 0.8632 | 217000 | 0.024         | -               |
| 0.8672 | 218000 | 0.0249        | -               |
| 0.8712 | 219000 | 0.0234        | -               |
| 0.8752 | 220000 | 0.0232        | -               |
| 0.8791 | 221000 | 0.0231        | -               |
| 0.8831 | 222000 | 0.0264        | -               |
| 0.8871 | 223000 | 0.051         | -               |
| 0.8911 | 224000 | 0.0436        | -               |
| 0.8950 | 225000 | 0.0429        | -               |
| 0.8990 | 226000 | 0.0406        | -               |
| 0.9030 | 227000 | 0.0363        | -               |
| 0.9070 | 228000 | 0.0316        | -               |
| 0.9110 | 229000 | 0.023         | -               |
| 0.9149 | 230000 | 0.0239        | -               |
| 0.9189 | 231000 | 0.0225        | -               |
| 0.9229 | 232000 | 0.0223        | -               |
| 0.9269 | 233000 | 0.0225        | -               |
| 0.9309 | 234000 | 0.0223        | -               |
| 0.9348 | 235000 | 0.0191        | -               |
| 0.9388 | 236000 | 0.0202        | -               |
| 0.9428 | 237000 | 0.0149        | -               |
| 0.9468 | 238000 | 0.0215        | -               |
| 0.9507 | 239000 | 0.012         | -               |
| 0.9547 | 240000 | 0.0141        | -               |
| 0.9587 | 241000 | 0.0152        | -               |
| 0.9627 | 242000 | 0.0311        | -               |
| 0.9667 | 243000 | 0.0344        | -               |
| 0.9706 | 244000 | 0.0209        | -               |
| 0.9746 | 245000 | 0.0145        | -               |
| 0.9786 | 246000 | 0.0128        | -               |
| 0.9826 | 247000 | 0.0124        | -               |
| 0.9865 | 248000 | 0.0123        | -               |
| 0.9905 | 249000 | 0.0141        | -               |
| 0.9945 | 250000 | 0.0115        | -               |
| 0.9985 | 251000 | 0.0131        | -               |
| 1.0000 | 251382 | -             | 0.0022          |
| 1.0025 | 252000 | 0.3338        | -               |
| 1.0064 | 253000 | 0.1293        | -               |
| 1.0104 | 254000 | 0.0893        | -               |
| 1.0144 | 255000 | 0.0811        | -               |
| 1.0184 | 256000 | 0.0863        | -               |
| 1.0223 | 257000 | 0.0872        | -               |
| 1.0263 | 258000 | 0.0693        | -               |
| 1.0303 | 259000 | 0.0643        | -               |
| 1.0343 | 260000 | 0.0638        | -               |
| 1.0383 | 261000 | 0.0511        | -               |
| 1.0422 | 262000 | 0.0554        | -               |
| 1.0462 | 263000 | 0.0643        | -               |
| 1.0502 | 264000 | 0.0492        | -               |
| 1.0542 | 265000 | 0.0583        | -               |
| 1.0581 | 266000 | 0.0738        | -               |
| 1.0621 | 267000 | 0.079         | -               |
| 1.0661 | 268000 | 0.0823        | -               |
| 1.0701 | 269000 | 0.0673        | -               |
| 1.0741 | 270000 | 0.059         | -               |
| 1.0780 | 271000 | 0.0946        | -               |
| 1.0820 | 272000 | 0.0494        | -               |
| 1.0860 | 273000 | 0.0958        | -               |
| 1.0900 | 274000 | 0.0561        | -               |
| 1.0939 | 275000 | 0.0517        | -               |
| 1.0979 | 276000 | 0.0502        | -               |
| 1.1019 | 277000 | 0.0874        | -               |
| 1.1059 | 278000 | 0.0463        | -               |
| 1.1099 | 279000 | 0.0472        | -               |
| 1.1138 | 280000 | 0.1038        | -               |
| 1.1178 | 281000 | 0.0542        | -               |
| 1.1218 | 282000 | 0.1036        | -               |
| 1.1258 | 283000 | 0.05          | -               |
| 1.1298 | 284000 | 0.1052        | -               |
| 1.1337 | 285000 | 0.073         | -               |
| 1.1377 | 286000 | 0.041         | -               |
| 1.1417 | 287000 | 0.0981        | -               |
| 1.1457 | 288000 | 0.0415        | -               |
| 1.1496 | 289000 | 0.0561        | -               |
| 1.1536 | 290000 | 0.0612        | -               |
| 1.1576 | 291000 | 0.0426        | -               |
| 1.1616 | 292000 | 0.0711        | -               |
| 1.1656 | 293000 | 0.0703        | -               |
| 1.1695 | 294000 | 0.0508        | -               |
| 1.1735 | 295000 | 0.0756        | -               |
| 1.1775 | 296000 | 0.053         | -               |
| 1.1815 | 297000 | 0.0581        | -               |
| 1.1854 | 298000 | 0.0793        | -               |
| 1.1894 | 299000 | 0.0476        | -               |
| 1.1934 | 300000 | 0.0601        | -               |
| 1.1974 | 301000 | 0.0713        | -               |
| 1.2014 | 302000 | 0.0501        | -               |
| 1.2053 | 303000 | 0.0457        | -               |
| 1.2093 | 304000 | 0.0471        | -               |
| 1.2133 | 305000 | 0.0682        | -               |
| 1.2173 | 306000 | 0.0544        | -               |
| 1.2212 | 307000 | 0.0359        | -               |
| 1.2252 | 308000 | 0.047         | -               |
| 1.2292 | 309000 | 0.0527        | -               |
| 1.2332 | 310000 | 0.0437        | -               |
| 1.2372 | 311000 | 0.0384        | -               |
| 1.2411 | 312000 | 0.0508        | -               |
| 1.2451 | 313000 | 0.0383        | -               |
| 1.2491 | 314000 | 0.0376        | -               |
| 1.2531 | 315000 | 0.0376        | -               |
| 1.2570 | 316000 | 0.0441        | -               |
| 1.2610 | 317000 | 0.0557        | -               |
| 1.2650 | 318000 | 0.0384        | -               |
| 1.2690 | 319000 | 0.0295        | -               |
| 1.2730 | 320000 | 0.0409        | -               |
| 1.2769 | 321000 | 0.0671        | -               |
| 1.2809 | 322000 | 0.0603        | -               |
| 1.2849 | 323000 | 0.0668        | -               |
| 1.2889 | 324000 | 0.026         | -               |
| 1.2928 | 325000 | 0.0264        | -               |
| 1.2968 | 326000 | 0.056         | -               |
| 1.3008 | 327000 | 0.0399        | -               |
| 1.3048 | 328000 | 0.074         | -               |
| 1.3088 | 329000 | 0.0364        | -               |
| 1.3127 | 330000 | 0.0306        | -               |
| 1.3167 | 331000 | 0.0727        | -               |
| 1.3207 | 332000 | 0.0325        | -               |
| 1.3247 | 333000 | 0.0439        | -               |
| 1.3286 | 334000 | 0.0563        | -               |
| 1.3326 | 335000 | 0.0274        | -               |
| 1.3366 | 336000 | 0.0274        | -               |
| 1.3406 | 337000 | 0.0721        | -               |
| 1.3446 | 338000 | 0.0286        | -               |
| 1.3485 | 339000 | 0.0558        | -               |
| 1.3525 | 340000 | 0.0452        | -               |
| 1.3565 | 341000 | 0.0201        | -               |
| 1.3605 | 342000 | 0.0275        | -               |
| 1.3645 | 343000 | 0.0189        | -               |
| 1.3684 | 344000 | 0.0543        | -               |
| 1.3724 | 345000 | 0.0469        | -               |
| 1.3764 | 346000 | 0.067         | -               |
| 1.3804 | 347000 | 0.0289        | -               |
| 1.3843 | 348000 | 0.0366        | -               |
| 1.3883 | 349000 | 0.026         | -               |
| 1.3923 | 350000 | 0.0286        | -               |
| 1.3963 | 351000 | 0.024         | -               |
| 1.4003 | 352000 | 0.0403        | -               |
| 1.4042 | 353000 | 0.0429        | -               |
| 1.4082 | 354000 | 0.0216        | -               |
| 1.4122 | 355000 | 0.027         | -               |
| 1.4162 | 356000 | 0.0184        | -               |
| 1.4201 | 357000 | 0.0266        | -               |
| 1.4241 | 358000 | 0.0332        | -               |
| 1.4281 | 359000 | 0.0427        | -               |
| 1.4321 | 360000 | 0.0456        | -               |
| 1.4361 | 361000 | 0.0229        | -               |
| 1.4400 | 362000 | 0.0161        | -               |
| 1.4440 | 363000 | 0.025         | -               |
| 1.4480 | 364000 | 0.031         | -               |
| 1.4520 | 365000 | 0.0256        | -               |
| 1.4559 | 366000 | 0.0195        | -               |
| 1.4599 | 367000 | 0.0224        | -               |
| 1.4639 | 368000 | 0.0163        | -               |
| 1.4679 | 369000 | 0.037         | -               |
| 1.4719 | 370000 | 0.0471        | -               |
| 1.4758 | 371000 | 0.0452        | -               |
| 1.4798 | 372000 | 0.0448        | -               |
| 1.4838 | 373000 | 0.034         | -               |
| 1.4878 | 374000 | 0.022         | -               |
| 1.4917 | 375000 | 0.0216        | -               |
| 1.4957 | 376000 | 0.0247        | -               |
| 1.4997 | 377000 | 0.0172        | -               |
| 1.5037 | 378000 | 0.0218        | -               |
| 1.5077 | 379000 | 0.023         | -               |
| 1.5116 | 380000 | 0.0138        | -               |
| 1.5156 | 381000 | 0.025         | -               |
| 1.5196 | 382000 | 0.0361        | -               |
| 1.5236 | 383000 | 0.0295        | -               |
| 1.5275 | 384000 | 0.0257        | -               |
| 1.5315 | 385000 | 0.0162        | -               |
| 1.5355 | 386000 | 0.0283        | -               |
| 1.5395 | 387000 | 0.0246        | -               |
| 1.5435 | 388000 | 0.0379        | -               |
| 1.5474 | 389000 | 0.0411        | -               |
| 1.5514 | 390000 | 0.0424        | -               |
| 1.5554 | 391000 | 0.0384        | -               |
| 1.5594 | 392000 | 0.0458        | -               |
| 1.5634 | 393000 | 0.039         | -               |
| 1.5673 | 394000 | 0.0273        | -               |
| 1.5713 | 395000 | 0.0338        | -               |
| 1.5753 | 396000 | 0.0276        | -               |
| 1.5793 | 397000 | 0.0277        | -               |
| 1.5832 | 398000 | 0.0231        | -               |
| 1.5872 | 399000 | 0.0168        | -               |
| 1.5912 | 400000 | 0.0135        | -               |
| 1.5952 | 401000 | 0.0262        | -               |
| 1.5992 | 402000 | 0.0253        | -               |
| 1.6031 | 403000 | 0.0235        | -               |
| 1.6071 | 404000 | 0.0189        | -               |
| 1.6111 | 405000 | 0.024         | -               |
| 1.6151 | 406000 | 0.0244        | -               |
| 1.6190 | 407000 | 0.0281        | -               |
| 1.6230 | 408000 | 0.0223        | -               |
| 1.6270 | 409000 | 0.0515        | -               |
| 1.6310 | 410000 | 0.0374        | -               |
| 1.6350 | 411000 | 0.0419        | -               |
| 1.6389 | 412000 | 0.0151        | -               |
| 1.6429 | 413000 | 0.0196        | -               |
| 1.6469 | 414000 | 0.0164        | -               |
| 1.6509 | 415000 | 0.0413        | -               |
| 1.6548 | 416000 | 0.0375        | -               |
| 1.6588 | 417000 | 0.0431        | -               |
| 1.6628 | 418000 | 0.0287        | -               |
| 1.6668 | 419000 | 0.0189        | -               |
| 1.6708 | 420000 | 0.0175        | -               |
| 1.6747 | 421000 | 0.0286        | -               |
| 1.6787 | 422000 | 0.0291        | -               |
| 1.6827 | 423000 | 0.0215        | -               |
| 1.6867 | 424000 | 0.027         | -               |
| 1.6906 | 425000 | 0.0366        | -               |
| 1.6946 | 426000 | 0.0285        | -               |
| 1.6986 | 427000 | 0.0216        | -               |
| 1.7026 | 428000 | 0.0268        | -               |
| 1.7066 | 429000 | 0.0255        | -               |
| 1.7105 | 430000 | 0.0209        | -               |
| 1.7145 | 431000 | 0.0257        | -               |
| 1.7185 | 432000 | 0.0407        | -               |
| 1.7225 | 433000 | 0.0349        | -               |
| 1.7264 | 434000 | 0.0342        | -               |
| 1.7304 | 435000 | 0.0235        | -               |
| 1.7344 | 436000 | 0.0216        | -               |
| 1.7384 | 437000 | 0.0201        | -               |
| 1.7424 | 438000 | 0.0257        | -               |
| 1.7463 | 439000 | 0.0269        | -               |
| 1.7503 | 440000 | 0.0305        | -               |
| 1.7543 | 441000 | 0.0319        | -               |
| 1.7583 | 442000 | 0.0292        | -               |
| 1.7623 | 443000 | 0.0311        | -               |
| 1.7662 | 444000 | 0.0304        | -               |
| 1.7702 | 445000 | 0.0263        | -               |
| 1.7742 | 446000 | 0.0195        | -               |
| 1.7782 | 447000 | 0.0162        | -               |
| 1.7821 | 448000 | 0.0122        | -               |
| 1.7861 | 449000 | 0.0156        | -               |
| 1.7901 | 450000 | 0.0148        | -               |
| 1.7941 | 451000 | 0.0126        | -               |
| 1.7981 | 452000 | 0.0162        | -               |
| 1.8020 | 453000 | 0.0154        | -               |
| 1.8060 | 454000 | 0.0157        | -               |
| 1.8100 | 455000 | 0.0321        | -               |
| 1.8140 | 456000 | 0.0296        | -               |
| 1.8179 | 457000 | 0.0296        | -               |
| 1.8219 | 458000 | 0.0305        | -               |
| 1.8259 | 459000 | 0.0285        | -               |
| 1.8299 | 460000 | 0.0263        | -               |
| 1.8339 | 461000 | 0.0116        | -               |
| 1.8378 | 462000 | 0.0156        | -               |
| 1.8418 | 463000 | 0.0172        | -               |
| 1.8458 | 464000 | 0.0154        | -               |
| 1.8498 | 465000 | 0.0162        | -               |
| 1.8537 | 466000 | 0.0151        | -               |
| 1.8577 | 467000 | 0.0162        | -               |
| 1.8617 | 468000 | 0.0149        | -               |
| 1.8657 | 469000 | 0.0157        | -               |
| 1.8697 | 470000 | 0.0161        | -               |
| 1.8736 | 471000 | 0.0159        | -               |
| 1.8776 | 472000 | 0.0152        | -               |
| 1.8816 | 473000 | 0.0148        | -               |
| 1.8856 | 474000 | 0.0315        | -               |
| 1.8895 | 475000 | 0.0282        | -               |
| 1.8935 | 476000 | 0.0331        | -               |
| 1.8975 | 477000 | 0.0284        | -               |
| 1.9015 | 478000 | 0.0272        | -               |
| 1.9055 | 479000 | 0.026         | -               |
| 1.9094 | 480000 | 0.0159        | -               |
| 1.9134 | 481000 | 0.0153        | -               |
| 1.9174 | 482000 | 0.0158        | -               |
| 1.9214 | 483000 | 0.014         | -               |
| 1.9253 | 484000 | 0.0153        | -               |
| 1.9293 | 485000 | 0.0146        | -               |
| 1.9333 | 486000 | 0.0137        | -               |
| 1.9373 | 487000 | 0.0129        | -               |
| 1.9413 | 488000 | 0.0112        | -               |
| 1.9452 | 489000 | 0.0139        | -               |
| 1.9492 | 490000 | 0.0076        | -               |
| 1.9532 | 491000 | 0.0074        | -               |
| 1.9572 | 492000 | 0.0097        | -               |
| 1.9612 | 493000 | 0.0136        | -               |
| 1.9651 | 494000 | 0.0269        | -               |
| 1.9691 | 495000 | 0.0192        | -               |
| 1.9731 | 496000 | 0.0092        | -               |
| 1.9771 | 497000 | 0.0086        | -               |
| 1.9810 | 498000 | 0.0078        | -               |
| 1.9850 | 499000 | 0.0073        | -               |
| 1.9890 | 500000 | 0.0093        | -               |
| 1.9930 | 501000 | 0.0071        | -               |
| 1.9970 | 502000 | 0.0075        | -               |
| 2.0000 | 502764 | -             | 0.0018          |
| 2.0009 | 503000 | 0.1807        | -               |
| 2.0049 | 504000 | 0.1676        | -               |
| 2.0089 | 505000 | 0.0704        | -               |
| 2.0129 | 506000 | 0.0733        | -               |
| 2.0168 | 507000 | 0.0713        | -               |
| 2.0208 | 508000 | 0.0633        | -               |
| 2.0248 | 509000 | 0.0562        | -               |
| 2.0288 | 510000 | 0.0521        | -               |
| 2.0328 | 511000 | 0.0517        | -               |
| 2.0367 | 512000 | 0.0516        | -               |
| 2.0407 | 513000 | 0.047         | -               |
| 2.0447 | 514000 | 0.0411        | -               |
| 2.0487 | 515000 | 0.0445        | -               |
| 2.0526 | 516000 | 0.0493        | -               |
| 2.0566 | 517000 | 0.0618        | -               |
| 2.0606 | 518000 | 0.0335        | -               |
| 2.0646 | 519000 | 0.1001        | -               |
| 2.0686 | 520000 | 0.0349        | -               |
| 2.0725 | 521000 | 0.0675        | -               |
| 2.0765 | 522000 | 0.0376        | -               |
| 2.0805 | 523000 | 0.0838        | -               |
| 2.0845 | 524000 | 0.0411        | -               |
| 2.0884 | 525000 | 0.0798        | -               |
| 2.0924 | 526000 | 0.0401        | -               |
| 2.0964 | 527000 | 0.0455        | -               |
| 2.1004 | 528000 | 0.0312        | -               |
| 2.1044 | 529000 | 0.0778        | -               |
| 2.1083 | 530000 | 0.0368        | -               |
| 2.1123 | 531000 | 0.0872        | -               |
| 2.1163 | 532000 | 0.0411        | -               |
| 2.1203 | 533000 | 0.0906        | -               |
| 2.1242 | 534000 | 0.0409        | -               |
| 2.1282 | 535000 | 0.0523        | -               |
| 2.1322 | 536000 | 0.0986        | -               |
| 2.1362 | 537000 | 0.0332        | -               |
| 2.1402 | 538000 | 0.0668        | -               |
| 2.1441 | 539000 | 0.0472        | -               |
| 2.1481 | 540000 | 0.035         | -               |
| 2.1521 | 541000 | 0.0588        | -               |
| 2.1561 | 542000 | 0.0349        | -               |
| 2.1601 | 543000 | 0.036         | -               |
| 2.1640 | 544000 | 0.0706        | -               |
| 2.1680 | 545000 | 0.0458        | -               |
| 2.1720 | 546000 | 0.0525        | -               |
| 2.1760 | 547000 | 0.0489        | -               |
| 2.1799 | 548000 | 0.0493        | -               |
| 2.1839 | 549000 | 0.044         | -               |
| 2.1879 | 550000 | 0.064         | -               |
| 2.1919 | 551000 | 0.0423        | -               |
| 2.1959 | 552000 | 0.0503        | -               |
| 2.1998 | 553000 | 0.0562        | -               |
| 2.2038 | 554000 | 0.0335        | -               |
| 2.2078 | 555000 | 0.0394        | -               |
| 2.2118 | 556000 | 0.0527        | -               |
| 2.2157 | 557000 | 0.0452        | -               |
| 2.2197 | 558000 | 0.0342        | -               |
| 2.2237 | 559000 | 0.0314        | -               |
| 2.2277 | 560000 | 0.0458        | -               |
| 2.2317 | 561000 | 0.0343        | -               |
| 2.2356 | 562000 | 0.0295        | -               |
| 2.2396 | 563000 | 0.0341        | -               |
| 2.2436 | 564000 | 0.04          | -               |
| 2.2476 | 565000 | 0.0302        | -               |
| 2.2515 | 566000 | 0.0266        | -               |
| 2.2555 | 567000 | 0.0373        | -               |
| 2.2595 | 568000 | 0.0447        | -               |
| 2.2635 | 569000 | 0.03          | -               |
| 2.2675 | 570000 | 0.0252        | -               |
| 2.2714 | 571000 | 0.0313        | -               |
| 2.2754 | 572000 | 0.0472        | -               |
| 2.2794 | 573000 | 0.0495        | -               |
| 2.2834 | 574000 | 0.0588        | -               |
| 2.2873 | 575000 | 0.0249        | -               |
| 2.2913 | 576000 | 0.0221        | -               |
| 2.2953 | 577000 | 0.0303        | -               |
| 2.2993 | 578000 | 0.0416        | -               |
| 2.3033 | 579000 | 0.064         | -               |
| 2.3072 | 580000 | 0.0227        | -               |
| 2.3112 | 581000 | 0.0263        | -               |
| 2.3152 | 582000 | 0.051         | -               |
| 2.3192 | 583000 | 0.0344        | -               |
| 2.3231 | 584000 | 0.0342        | -               |
| 2.3271 | 585000 | 0.0368        | -               |
| 2.3311 | 586000 | 0.0303        | -               |
| 2.3351 | 587000 | 0.0206        | -               |
| 2.3391 | 588000 | 0.0597        | -               |
| 2.3430 | 589000 | 0.0232        | -               |
| 2.3470 | 590000 | 0.0342        | -               |
| 2.3510 | 591000 | 0.0477        | -               |
| 2.3550 | 592000 | 0.015         | -               |
| 2.3590 | 593000 | 0.0169        | -               |
| 2.3629 | 594000 | 0.0209        | -               |
| 2.3669 | 595000 | 0.0392        | -               |
| 2.3709 | 596000 | 0.0299        | -               |
| 2.3749 | 597000 | 0.0362        | -               |
| 2.3788 | 598000 | 0.0503        | -               |
| 2.3828 | 599000 | 0.0262        | -               |
| 2.3868 | 600000 | 0.0275        | -               |
| 2.3908 | 601000 | 0.0217        | -               |
| 2.3948 | 602000 | 0.0185        | -               |
| 2.3987 | 603000 | 0.0251        | -               |
| 2.4027 | 604000 | 0.036         | -               |
| 2.4067 | 605000 | 0.0202        | -               |
| 2.4107 | 606000 | 0.018         | -               |
| 2.4146 | 607000 | 0.0178        | -               |
| 2.4186 | 608000 | 0.0201        | -               |
| 2.4226 | 609000 | 0.0183        | -               |
| 2.4266 | 610000 | 0.0325        | -               |
| 2.4306 | 611000 | 0.0391        | -               |
| 2.4345 | 612000 | 0.0229        | -               |
| 2.4385 | 613000 | 0.0165        | -               |
| 2.4425 | 614000 | 0.0123        | -               |
| 2.4465 | 615000 | 0.0257        | -               |
| 2.4504 | 616000 | 0.022         | -               |
| 2.4544 | 617000 | 0.0167        | -               |
| 2.4584 | 618000 | 0.0167        | -               |
| 2.4624 | 619000 | 0.0127        | -               |
| 2.4664 | 620000 | 0.0201        | -               |
| 2.4703 | 621000 | 0.0364        | -               |
| 2.4743 | 622000 | 0.0368        | -               |
| 2.4783 | 623000 | 0.0352        | -               |
| 2.4823 | 624000 | 0.0319        | -               |
| 2.4862 | 625000 | 0.0171        | -               |
| 2.4902 | 626000 | 0.0185        | -               |
| 2.4942 | 627000 | 0.0206        | -               |
| 2.4982 | 628000 | 0.0138        | -               |
| 2.5022 | 629000 | 0.0151        | -               |
| 2.5061 | 630000 | 0.019         | -               |
| 2.5101 | 631000 | 0.0122        | -               |
| 2.5141 | 632000 | 0.0139        | -               |
| 2.5181 | 633000 | 0.0203        | -               |
| 2.5220 | 634000 | 0.0295        | -               |
| 2.5260 | 635000 | 0.0217        | -               |
| 2.5300 | 636000 | 0.0132        | -               |
| 2.5340 | 637000 | 0.0183        | -               |
| 2.5380 | 638000 | 0.0208        | -               |
| 2.5419 | 639000 | 0.0217        | -               |
| 2.5459 | 640000 | 0.0333        | -               |
| 2.5499 | 641000 | 0.036         | -               |
| 2.5539 | 642000 | 0.0317        | -               |
| 2.5578 | 643000 | 0.034         | -               |
| 2.5618 | 644000 | 0.0365        | -               |
| 2.5658 | 645000 | 0.023         | -               |
| 2.5698 | 646000 | 0.0288        | -               |
| 2.5738 | 647000 | 0.0211        | -               |
| 2.5777 | 648000 | 0.0211        | -               |
| 2.5817 | 649000 | 0.023         | -               |
| 2.5857 | 650000 | 0.0137        | -               |
| 2.5897 | 651000 | 0.0115        | -               |
| 2.5937 | 652000 | 0.0141        | -               |
| 2.5976 | 653000 | 0.0209        | -               |
| 2.6016 | 654000 | 0.0207        | -               |
| 2.6056 | 655000 | 0.0149        | -               |
| 2.6096 | 656000 | 0.0173        | -               |
| 2.6135 | 657000 | 0.0196        | -               |
| 2.6175 | 658000 | 0.0198        | -               |
| 2.6215 | 659000 | 0.0217        | -               |
| 2.6255 | 660000 | 0.0306        | -               |
| 2.6295 | 661000 | 0.0383        | -               |
| 2.6334 | 662000 | 0.0286        | -               |
| 2.6374 | 663000 | 0.0228        | -               |
| 2.6414 | 664000 | 0.0135        | -               |
| 2.6454 | 665000 | 0.0148        | -               |
| 2.6493 | 666000 | 0.0221        | -               |
| 2.6533 | 667000 | 0.0347        | -               |
| 2.6573 | 668000 | 0.0293        | -               |
| 2.6613 | 669000 | 0.0333        | -               |
| 2.6653 | 670000 | 0.015         | -               |
| 2.6692 | 671000 | 0.0143        | -               |
| 2.6732 | 672000 | 0.0195        | -               |
| 2.6772 | 673000 | 0.0251        | -               |
| 2.6812 | 674000 | 0.0212        | -               |
| 2.6851 | 675000 | 0.0155        | -               |
| 2.6891 | 676000 | 0.0306        | -               |
| 2.6931 | 677000 | 0.0231        | -               |
| 2.6971 | 678000 | 0.0202        | -               |
| 2.7011 | 679000 | 0.0215        | -               |
| 2.7050 | 680000 | 0.0212        | -               |
| 2.7090 | 681000 | 0.0192        | -               |
| 2.7130 | 682000 | 0.0187        | -               |
| 2.7170 | 683000 | 0.0305        | -               |
| 2.7209 | 684000 | 0.0286        | -               |
| 2.7249 | 685000 | 0.0282        | -               |
| 2.7289 | 686000 | 0.0229        | -               |
| 2.7329 | 687000 | 0.0211        | -               |
| 2.7369 | 688000 | 0.0124        | -               |
| 2.7408 | 689000 | 0.0227        | -               |
| 2.7448 | 690000 | 0.0205        | -               |
| 2.7488 | 691000 | 0.0246        | -               |
| 2.7528 | 692000 | 0.0234        | -               |
| 2.7567 | 693000 | 0.0245        | -               |
| 2.7607 | 694000 | 0.0239        | -               |
| 2.7647 | 695000 | 0.0261        | -               |
| 2.7687 | 696000 | 0.0227        | -               |
| 2.7727 | 697000 | 0.0181        | -               |
| 2.7766 | 698000 | 0.014         | -               |
| 2.7806 | 699000 | 0.0103        | -               |
| 2.7846 | 700000 | 0.0119        | -               |
| 2.7886 | 701000 | 0.0115        | -               |
| 2.7926 | 702000 | 0.0102        | -               |
| 2.7965 | 703000 | 0.0113        | -               |
| 2.8005 | 704000 | 0.0124        | -               |
| 2.8045 | 705000 | 0.0119        | -               |
| 2.8085 | 706000 | 0.0218        | -               |
| 2.8124 | 707000 | 0.0239        | -               |
| 2.8164 | 708000 | 0.024         | -               |
| 2.8204 | 709000 | 0.0237        | -               |
| 2.8244 | 710000 | 0.0242        | -               |
| 2.8284 | 711000 | 0.0252        | -               |
| 2.8323 | 712000 | 0.011         | -               |
| 2.8363 | 713000 | 0.0129        | -               |
| 2.8403 | 714000 | 0.0129        | -               |
| 2.8443 | 715000 | 0.0133        | -               |
| 2.8482 | 716000 | 0.0117        | -               |
| 2.8522 | 717000 | 0.013         | -               |
| 2.8562 | 718000 | 0.0133        | -               |
| 2.8602 | 719000 | 0.0116        | -               |
| 2.8642 | 720000 | 0.0126        | -               |
| 2.8681 | 721000 | 0.0131        | -               |
| 2.8721 | 722000 | 0.0124        | -               |
| 2.8761 | 723000 | 0.0122        | -               |
| 2.8801 | 724000 | 0.0109        | -               |
| 2.8840 | 725000 | 0.0233        | -               |
| 2.8880 | 726000 | 0.0241        | -               |
| 2.8920 | 727000 | 0.0266        | -               |
| 2.8960 | 728000 | 0.0244        | -               |
| 2.9000 | 729000 | 0.0234        | -               |
| 2.9039 | 730000 | 0.0215        | -               |
| 2.9079 | 731000 | 0.0166        | -               |
| 2.9119 | 732000 | 0.0135        | -               |
| 2.9159 | 733000 | 0.013         | -               |
| 2.9198 | 734000 | 0.0127        | -               |
| 2.9238 | 735000 | 0.0118        | -               |
| 2.9278 | 736000 | 0.0131        | -               |
| 2.9318 | 737000 | 0.0108        | -               |
| 2.9358 | 738000 | 0.0099        | -               |
| 2.9397 | 739000 | 0.011         | -               |
| 2.9437 | 740000 | 0.0068        | -               |
| 2.9477 | 741000 | 0.0103        | -               |
| 2.9517 | 742000 | 0.0054        | -               |
| 2.9556 | 743000 | 0.0071        | -               |
| 2.9596 | 744000 | 0.0076        | -               |
| 2.9636 | 745000 | 0.0206        | -               |
| 2.9676 | 746000 | 0.0212        | -               |
| 2.9716 | 747000 | 0.0075        | -               |
| 2.9755 | 748000 | 0.0078        | -               |
| 2.9795 | 749000 | 0.0067        | -               |
| 2.9835 | 750000 | 0.0057        | -               |
| 2.9875 | 751000 | 0.0062        | -               |
| 2.9915 | 752000 | 0.0075        | -               |
| 2.9954 | 753000 | 0.006         | -               |
| 2.9994 | 754000 | 0.0073        | -               |
| 3.0000 | 754146 | -             | 0.0013          |

</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}
}
```

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