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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:95253
+- loss:MultipleNegativesRankingLoss
+base_model: thenlper/gte-base
+widget:
+- source_sentence: Molecular phylogenetic resolution of the mega-diverse clade Apoditrysia
+  sentences:
+  - In a previous study of higher-level arthropod phylogeny, analyses of nucleotide
+    sequences from 62 protein-coding nuclear genes for 80 panarthopod species yielded
+    significantly higher bootstrap support for selected nodes than did amino acids.
+    This study investigates the cause of that discrepancy. The hypothesis is tested
+    that failure to distinguish the serine residues encoded by two disjunct clusters
+    of codons (TCN, AGY) in amino acid analyses leads to this discrepancy. In one
+    test, the two clusters of serine codons (Ser1, Ser2) are conceptually translated
+    as separate amino acids. Analysis of the resulting 21-amino-acid data matrix shows
+    striking increases in bootstrap support, in some cases matching that in nucleotide
+    analyses. In a second approach, nucleotide and 20-amino-acid data sets are artificially
+    altered through targeted deletions, modifications, and replacements, revealing
+    the pivotal contributions of distinct Ser1 and Ser2 codons. We confirm that previous
+    methods of coding nonsynonymous nucleotide change are robust and computationally
+    efficient by introducing two new degeneracy coding methods. We demonstrate for
+    degeneracy coding that neither compositional heterogeneity at the level of nucleotides
+    nor codon usage bias between Ser1 and Ser2 clusters of codons (or their separately
+    coded amino acids) is a major source of non-phylogenetic signal. The incongruity
+    in support between amino-acid and nucleotide analyses of the forementioned arthropod
+    data set is resolved by showing that "standard" 20-amino-acid analyses yield lower
+    node support specifically when serine provides crucial signal. Separate coding
+    of Ser1 and Ser2 residues yields support commensurate with that found by degenerated
+    nucleotides, without introducing phylogenetic artifacts. While exclusion of all
+    serine data leads to reduced support for serine-sensitive nodes, these nodes are
+    still recovered in the ML topology, indicating that the enhanced signal from Ser1
+    and Ser2 is not qualitatively different from that of the other amino acids.
+  - 'Recent molecular phylogenetic studies of the insect order Lepidoptera have robustly
+    resolved family-level divergences within most superfamilies, and most divergences
+    among the relatively species-poor early-arising superfamilies. In sharp contrast,
+    relationships among the superfamilies of more advanced moths and butterflies that
+    comprise the mega-diverse clade Apoditrysia (ca. 145,000 spp.) remain mostly poorly
+    supported. This uncertainty, in turn, limits our ability to discern the origins,
+    ages and evolutionary consequences of traits hypothesized to promote the spectacular
+    diversification of Apoditrysia. Low support along the apoditrysian "backbone"
+    probably reflects rapid diversification. If so, it may be feasible to strengthen
+    resolution by radically increasing the gene sample, but case studies have been
+    few. We explored the potential of next-generation sequencing to conclusively resolve
+    apoditrysian relationships. We used transcriptome RNA-Seq to generate 1579 putatively
+    orthologous gene sequences across a broad sample of 40 apoditrysians plus four
+    outgroups, to which we added two taxa from previously published data. Phylogenetic
+    analysis of a 46-taxon, 741-gene matrix, resulting from a strict filter that eliminated
+    ortholog groups containing any apparent paralogs, yielded dramatic overall increase
+    in bootstrap support for deeper nodes within Apoditrysia as compared to results
+    from previous and concurrent 19-gene analyses. High support was restricted mainly
+    to the huge subclade Obtectomera broadly defined, in which 11 of 12 nodes subtending
+    multiple superfamilies had bootstrap support of 100%. The strongly supported nodes
+    showed little conflict with groupings from previous studies, and were little affected
+    by changes in taxon sampling, suggesting that they reflect true signal rather
+    than artifacts of massive gene sampling. In contrast, strong support was seen
+    at only 2 of 11 deeper nodes among the "lower", non-obtectomeran apoditrysians.
+    These represent a much harder phylogenetic problem, for which one path to resolution
+    might include further increase in gene sampling, together with improved orthology
+    assignments. '
+  - 'One of the major challenges in cell implantation therapies is to promote integration
+    of the microcirculation between the implanted cells and the host. We used adipose-derived
+    stromal vascular fraction (SVF) cells to vascularize a human liver cell (HepG2)
+    implant. We hypothesized that the SVF cells would form a functional microcirculation
+    via vascular assembly and inosculation with the host vasculature. Initially, we
+    assessed the extent and character of neovasculatures formed by freshly isolated
+    and cultured SVF cells and found that freshly isolated cells have a higher vascularization
+    potential. Generation of a 3D implant containing fresh SVF and HepG2 cells formed
+    a tissue in which HepG2 cells were entwined with a network of microvessels. Implanted
+    HepG2 cells sequestered labeled LDL delivered by systemic intravascular injection
+    only in SVF-vascularized implants demonstrating that SVF cell-derived vasculatures
+    can effectively integrate with host vessels and interface with parenchymal cells
+    to form a functional tissue mimic. '
+- source_sentence: Exosomes as drug delivery systems for gastrointestinal cancers
+  sentences:
+  - Gastrointestinal cancer is one of the most common malignancies with relatively
+    high morbidity and mortality. Exosomes are nanosized extracellular vesicles derived
+    from most cells and widely distributed in body fluids. They are natural endogenous
+    nanocarriers with low immunogenicity, high biocompatibility, and natural targeting,
+    and can transport lipids, proteins, DNA, and RNA. Exosomes contain DNA, RNA, proteins,
+    lipids, and other bioactive components, which can play a role in information transmission
+    and regulation of cellular physiological and pathological processes during the
+    progression of gastrointestinal cancer. In this paper, the role of exosomes in
+    gastrointestinal cancers is briefly reviewed, with emphasis on the application
+    of exosomes as drug delivery systems for gastrointestinal cancers. Finally, the
+    challenges faced by exosome-based drug delivery systems are discussed.
+  - Background In the myocardium, pericytes are often confused with other interstitial
+    cell types, such as fibroblasts. The lack of well-characterized and specific tools
+    for identification, lineage tracing, and conditional targeting of myocardial pericytes
+    has hampered studies on their role in heart disease. In the current study, we
+    characterize and validate specific and reliable strategies for labeling and targeting
+    of cardiac pericytes. Methods and Results Using the neuron-glial antigen 2 (NG2)
+  - Exosomes are small extracellular vesicles with diameters of 30-150 nm. In both
+    physiological and pathological conditions, nearly all types of cells can release
+    exosomes, which play important roles in cell communication and epigenetic regulation
+    by transporting crucial protein and genetic materials such as miRNA, mRNA, and
+    DNA. Consequently, exosome-based disease diagnosis and therapeutic methods have
+    been intensively investigated. However, as in any natural science field, the in-depth
+    investigation of exosomes relies heavily on technological advances. Historically,
+    the two main technical hindrances that have restricted the basic and applied researches
+    of exosomes include, first, how to simplify the extraction and improve the yield
+    of exosomes and, second, how to effectively distinguish exosomes from other extracellular
+    vesicles, especially functional microvesicles. Over the past few decades, although
+    a standardized exosome isolation method has still not become available, a number
+    of techniques have been established through exploration of the biochemical and
+    physicochemical features of exosomes. In this work, by comprehensively analyzing
+    the progresses in exosome separation strategies, we provide a panoramic view of
+    current exosome isolation techniques, providing perspectives toward the development
+    of novel approaches for high-efficient exosome isolation from various types of
+    biological matrices. In addition, from the perspective of exosome-based diagnosis
+    and therapeutics, we emphasize the issue of quantitative exosome and microvesicle
+    separation.
+- source_sentence: Comparison of pesticide active substances in conventional agriculture
+    and organic agriculture in Europe
+  sentences:
+  - Total concentrations of metals in soil are poor predictors of toxicity. In the
+    last decade, considerable effort has been made to demonstrate how metal toxicity
+    is affected by the abiotic properties of soil. Here this information is collated
+    and shows how these data have been used in the European Union for defining predicted-no-effect
+    concentrations (PNECs) of Cd, Cu, Co, Ni, Pb, and Zn in soil. Bioavailability
+    models have been calibrated using data from more than 500 new chronic toxicity
+    tests in soils amended with soluble metal salts, in experimentally aged soils,
+    and in field-contaminated soils. In general, soil pH was a good predictor of metal
+    solubility but a poor predictor of metal toxicity across soils. Toxicity thresholds
+    based on the free metal ion activity were generally more variable than those expressed
+    on total soil metal, which can be explained, but not predicted, using the concept
+    of the biotic ligand model. The toxicity thresholds based on total soil metal
+    concentrations rise almost proportionally to the effective cation exchange capacity
+    of soil. Total soil metal concentrations yielding 10% inhibition in freshly amended
+    soils were up to 100-fold smaller (median 3.4-fold, n = 110 comparative tests)
+    than those in corresponding aged soils or field-contaminated soils. The change
+    in isotopically exchangeable metal in soil proved to be a conservative estimate
+    of the change in toxicity upon aging. The PNEC values for specific soil types
+    were calculated using this information. The corrections for aging and for modifying
+    effects of soil properties in metal-salt-amended soils are shown to be the main
+    factors by which PNEC values rise above the natural background range.
+  - There is much debate about whether the (mostly synthetic) pesticide active substances
+    (AS) in conventional agriculture have different non-target effects than the natural
+    AS in organic agriculture. We evaluated the official EU pesticide database to
+    compare 256 AS that may only be used on conventional farmland with 134 AS that
+    are permitted on organic farmland. As a benchmark, we used (i) the hazard classifications
+    of the Globally Harmonized System (GHS), and (ii) the dietary and occupational
+    health-based guidance values, which were established in the authorization procedure.
+    Our comparison showed that 55% of the AS used only in conventional agriculture
+    contained health or environmental hazard statements, but only 3% did of the AS
+    authorized for organic agriculture. Warnings about possible harm to the unborn
+    child, suspected carcinogenicity, or acute lethal effects were found in 16% of
+    the AS used in conventional agriculture, but none were found in organic agriculture.
+    Furthermore, the establishment of health-based guidance values for dietary and
+    non-dietary exposures were relevant by the European authorities for 93% of conventional
+    AS, but only for 7% of organic AS. We, therefore, encourage policies and strategies
+    to reduce the use and risk of pesticides, and to strengthen organic farming in
+    order to protect biodiversity and maintain food security.
+  - Herpes simplex virus 1 (HSV-1) encodes Us3 protein kinase, which is critical for
+    viral pathogenicity in both mouse peripheral sites (e.g., eyes and vaginas) and
+    in the central nervous systems (CNS) of mice after intracranial and peripheral
+    inoculations, respectively. Whereas some Us3 substrates involved in Us3 pathogenicity
+    in peripheral sites have been reported, those involved in Us3 pathogenicity in
+    the CNS remain to be identified. We recently reported that Us3 phosphorylated
+    HSV-1 dUTPase (vdUTPase) at serine 187 (Ser-187) in infected cells, and this phosphorylation
+    promoted viral replication by regulating optimal enzymatic activity of vdUTPase.
+    In the present study, we show that the replacement of vdUTPase Ser-187 by alanine
+    (S187A) significantly reduced viral replication and virulence in the CNS of mice
+    following intracranial inoculation and that the phosphomimetic substitution at
+    vdUTPase Ser-187 in part restored the wild-type viral replication and virulence.
+    Interestingly, the S187A mutation in vdUTPase had no effect on viral replication
+    and pathogenic effects in the eyes and vaginas of mice after ocular and vaginal
+    inoculation, respectively. Similarly, the enzyme-dead mutation in vdUTPase significantly
+    reduced viral replication and virulence in the CNS of mice after intracranial
+    inoculation, whereas the mutation had no effect on viral replication and pathogenic
+    effects in the eyes and vaginas of mice after ocular and vaginal inoculation,
+    respectively. These observations suggested that vdUTPase was one of the Us3 substrates
+    responsible for Us3 pathogenicity in the CNS and that the CNS-specific virulence
+    of HSV-1 involved strict regulation of vdUTPase activity by Us3 phosphorylation.
+- source_sentence: Load-dependent detachment and reattachment kinetics of kinesin-1,
+    -2 and 3 motors
+  sentences:
+  - Bidirectional cargo transport by kinesin and dynein is essential for cell viability
+    and defects are linked to neurodegenerative diseases. Computational modeling suggests
+    that the load-dependent off-rate is the strongest determinant of which motor 'wins'
+    a kinesin-dynein tug-of-war, and optical tweezer experiments find that the load-dependent
+    detachment sensitivity of transport kinesins is kinesin-3 > kinesin-2 > kinesin-1.
+    However, in reconstituted kinesin-dynein pairs vitro, all three kinesin families
+    compete nearly equally well against dynein. Modeling and experiments have confirmed
+    that vertical forces inherent to the large trapping beads enhance kinesin-1 dissociation
+    rates. In vivo, vertical forces are expected to range from negligible to dominant,
+    depending on cargo and microtubule geometries. To investigate the detachment and
+    reattachment kinetics of kinesin-1, 2 and 3 motors against loads oriented parallel
+    to the microtubule, we created a DNA tensiometer comprising a DNA entropic spring
+    attached to the microtubule on one end and a motor on the other. Kinesin dissociation
+    rates at stall were slower than detachment rates during unloaded runs, and the
+    complex reattachment kinetics were consistent with a weakly-bound 'slip' state
+    preceding detachment. Kinesin-3 behaviors under load suggested that long KIF1A
+    run lengths result from the concatenation of multiple short runs connected by
+    diffusive episodes. Stochastic simulations were able to recapitulate the load-dependent
+    detachment and reattachment kinetics for all three motors and provide direct comparison
+    of key transition rates between families. These results provide insight into how
+    kinesin-1, -2 and -3 families transport cargo in complex cellular geometries and
+    compete against dynein during bidirectional transport.
+  - 'AP-1 and AP-2 adaptor protein (AP) complexes mediate clathrin-dependent trafficking
+    at the trans-Golgi network (TGN) and the plasma membrane, respectively. Whereas
+    AP-1 is required for trafficking to plasma membrane and vacuoles, AP-2 mediates
+    endocytosis. These AP complexes consist of four subunits (adaptins): two large
+    subunits (β1 and γ for AP-1 and β2 and α for AP-2), a medium subunit μ, and a
+    small subunit σ. In general, adaptins are unique to each AP complex, with the
+    exception of β subunits that are shared by AP-1 and AP-2 in some invertebrates.
+    Here, we show that the two putative Arabidopsis thaliana AP1/2β adaptins co-assemble
+    with both AP-1 and AP-2 subunits and regulate exocytosis and endocytosis in root
+    cells, consistent with their dual localization at the TGN and plasma membrane.
+    Deletion of both β adaptins is lethal in plants. We identified a critical role
+    of β adaptins in pollen wall formation and reproduction, involving the regulation
+    of membrane trafficking in the tapetum and pollen germination. In tapetal cells,
+    β adaptins localize almost exclusively to the TGN and mediate exocytosis of the
+    plasma membrane transporters such as ATP-binding cassette (ABC)G9 and ABCG16.
+    This study highlights the essential role of AP1/2β adaptins in plants and their
+    specialized roles in specific cell types.'
+  - A single kinesin molecule can move "processively" along a microtubule for more
+    than 1 micrometer before detaching from it. The prevailing explanation for this
+    processive movement is the "walking model," which envisions that each of two motor
+    domains (heads) of the kinesin molecule binds coordinately to the microtubule.
+    This implies that each kinesin molecule must have two heads to "walk" and that
+    a single-headed kinesin could not move processively. Here, a motor-domain construct
+    of KIF1A, a single-headed kinesin superfamily protein, was shown to move processively
+    along the microtubule for more than 1 micrometer. The movement along the microtubules
+    was stochastic and fitted a biased Brownian-movement model.
+- source_sentence: Phylogenetic analysis of mitochondrial genes in Macquarie perch
+    from three river basins
+  sentences:
+  - Sedentary behavior is an emerging risk factor for cardiovascular disease (CVD)
+    and may be particularly relevant to the cardiovascular health of older adults.
+    This scoping review describes the existing literature examining the prevalence
+    of sedentary time in older adults with CVD and the association of sedentary behavior
+    with cardiovascular risk in older adults. We found that older adults with CVD
+    spend >75 % of their waking day sedentary, and that sedentary time is higher among
+    older adults with CVD than among older adults without CVD. High sedentary behavior
+    is consistently associated with worse cardiac lipid profiles and increased cardiac
+    risk scores in older adults; the associations of sedentary behavior with blood
+    pressure, CVD incidence, and CVD-related mortality among older adults are less
+    clear. Future research with larger sample sizes using validated methods to measure
+    sedentary behavior are needed to clarify the association between sedentary behavior
+    and cardiovascular outcomes in older adults.
+  - An improved Bayesian method is presented for estimating phylogenetic trees using
+    DNA sequence data. The birth-death process with species sampling is used to specify
+    the prior distribution of phylogenies and ancestral speciation times, and the
+    posterior probabilities of phylogenies are used to estimate the maximum posterior
+    probability (MAP) tree. Monte Carlo integration is used to integrate over the
+    ancestral speciation times for particular trees. A Markov Chain Monte Carlo method
+    is used to generate the set of trees with the highest posterior probabilities.
+    Methods are described for an empirical Bayesian analysis, in which estimates of
+    the speciation and extinction rates are used in calculating the posterior probabilities,
+    and a hierarchical Bayesian analysis, in which these parameters are removed from
+    the model by an additional integration. The Markov Chain Monte Carlo method avoids
+    the requirement of our earlier method for calculating MAP trees to sum over all
+    possible topologies (which limited the number of taxa in an analysis to about
+    five). The methods are applied to analyze DNA sequences for nine species of primates,
+    and the MAP tree, which is identical to a maximum-likelihood estimate of topology,
+    has a probability of approximately 95%.
+  - 'Genetic variation in mitochondrial genes could underlie metabolic adaptations
+    because mitochondrially encoded proteins are directly involved in a pathway supplying
+    energy to metabolism. Macquarie perch from river basins exposed to different climates
+    differ in size and growth rate, suggesting potential presence of adaptive metabolic
+    differences. We used complete mitochondrial genome sequences to build a phylogeny,
+    estimate lineage divergence times and identify signatures of purifying and positive
+    selection acting on mitochondrial genes for 25 Macquarie perch from three basins:
+    Murray-Darling Basin (MDB), Hawkesbury-Nepean Basin (HNB) and Shoalhaven Basin
+    (SB). Phylogenetic analysis resolved basin-level clades, supporting incipient
+    speciation previously inferred from differentiation in allozymes, microsatellites
+    and mitochondrial control region. The estimated time of lineage divergence suggested
+    an early- to mid-Pleistocene split between SB and the common ancestor of HNB+MDB,
+    followed by mid-to-late Pleistocene splitting between HNB and MDB. These divergence
+    estimates are more recent than previous ones. Our analyses suggested that evolutionary
+    drivers differed between inland MDB and coastal HNB. In the cooler and more climatically
+    variable MDB, mitogenomes evolved under strong purifying selection, whereas in
+    the warmer and more climatically stable HNB, purifying selection was relaxed.
+    Evidence for relaxed selection in the HNB includes elevated transfer RNA and 16S
+    ribosomal RNA polymorphism, presence of potentially mildly deleterious mutations
+    and a codon (ATP6'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on thenlper/gte-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-base](https://huggingface.co/thenlper/gte-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [thenlper/gte-base](https://huggingface.co/thenlper/gte-base) <!-- at revision c078288308d8dee004ab72c6191778064285ec0c -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Phylogenetic analysis of mitochondrial genes in Macquarie perch from three river basins',
+    'Genetic variation in mitochondrial genes could underlie metabolic adaptations because mitochondrially encoded proteins are directly involved in a pathway supplying energy to metabolism. Macquarie perch from river basins exposed to different climates differ in size and growth rate, suggesting potential presence of adaptive metabolic differences. We used complete mitochondrial genome sequences to build a phylogeny, estimate lineage divergence times and identify signatures of purifying and positive selection acting on mitochondrial genes for 25 Macquarie perch from three basins: Murray-Darling Basin (MDB), Hawkesbury-Nepean Basin (HNB) and Shoalhaven Basin (SB). Phylogenetic analysis resolved basin-level clades, supporting incipient speciation previously inferred from differentiation in allozymes, microsatellites and mitochondrial control region. The estimated time of lineage divergence suggested an early- to mid-Pleistocene split between SB and the common ancestor of HNB+MDB, followed by mid-to-late Pleistocene splitting between HNB and MDB. These divergence estimates are more recent than previous ones. Our analyses suggested that evolutionary drivers differed between inland MDB and coastal HNB. In the cooler and more climatically variable MDB, mitogenomes evolved under strong purifying selection, whereas in the warmer and more climatically stable HNB, purifying selection was relaxed. Evidence for relaxed selection in the HNB includes elevated transfer RNA and 16S ribosomal RNA polymorphism, presence of potentially mildly deleterious mutations and a codon (ATP6',
+    'An improved Bayesian method is presented for estimating phylogenetic trees using DNA sequence data. The birth-death process with species sampling is used to specify the prior distribution of phylogenies and ancestral speciation times, and the posterior probabilities of phylogenies are used to estimate the maximum posterior probability (MAP) tree. Monte Carlo integration is used to integrate over the ancestral speciation times for particular trees. A Markov Chain Monte Carlo method is used to generate the set of trees with the highest posterior probabilities. Methods are described for an empirical Bayesian analysis, in which estimates of the speciation and extinction rates are used in calculating the posterior probabilities, and a hierarchical Bayesian analysis, in which these parameters are removed from the model by an additional integration. The Markov Chain Monte Carlo method avoids the requirement of our earlier method for calculating MAP trees to sum over all possible topologies (which limited the number of taxa in an analysis to about five). The methods are applied to analyze DNA sequences for nine species of primates, and the MAP tree, which is identical to a maximum-likelihood estimate of topology, has a probability of approximately 95%.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.9449, 0.8056],
+#         [0.9449, 1.0000, 0.7868],
+#         [0.8056, 0.7868, 1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 95,253 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                          | sentence_2                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              | string                                                                               |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 19.51 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 223.97 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 309.24 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                  | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Sox5 modulates the activity of Sox10 in the melanocyte lineage</code> | <code>The transcription factor Sox5 has previously been shown in chicken to be expressed in early neural crest cells and neural crest-derived peripheral glia. Here, we show in mouse that Sox5 expression also continues after neural crest specification in the melanocyte lineage. Despite its continued expression, Sox5 has little impact on melanocyte development on its own as generation of melanoblasts and melanocytes is unaltered in Sox5-deficient mice. Loss of Sox5, however, partially rescued the strongly reduced melanoblast generation and marker gene expression in Sox10 heterozygous mice arguing that Sox5 functions in the melanocyte lineage by modulating Sox10 activity. This modulatory activity involved Sox5 binding and recruitment of CtBP2 and HDAC1 to the regulatory regions of melanocytic Sox10 target genes and direct inhibition of Sox10-dependent promoter activation. Both binding site competition and recruitment of corepressors thus help Sox5 to modulate the activity of Sox10 in the melano...</code> | <code>Transcripts for a new form of Sox5, called L-Sox5, and Sox6 are coexpressed with Sox9 in all chondrogenic sites of mouse embryos. A coiled-coil domain located in the N-terminal part of L-Sox5, and absent in Sox5, showed >90% identity with a similar domain in Sox6 and mediated homodimerization and heterodimerization with Sox6. Dimerization of L-Sox5/Sox6 greatly increased efficiency of binding of the two Sox proteins to DNA containing adjacent HMG sites. L-Sox5, Sox6 and Sox9 cooperatively activated expression of the chondrocyte differentiation marker Col2a1 in 10T1/2 and MC615 cells. A 48 bp chondrocyte-specific enhancer in this gene, which contains several HMG-like sites that are necessary for enhancer activity, bound the three Sox proteins and was cooperatively activated by the three Sox proteins in non-chondrogenic cells. Our data suggest that L-Sox5/Sox6 and Sox9, which belong to two different classes of Sox transcription factors, cooperate with each other in expression of Col2a1 a...</code> |
+  | <code>are asgard archaea related to eukaryotes</code>                       | <code>Asgard archaea are considered to be the closest known relatives of eukaryotes. Their genomes contain hundreds of eukaryotic signature proteins (ESPs), which inspired hypotheses on the evolution of the eukaryotic cell</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | <code>Eukaryotes evolved from a symbiosis involving alphaproteobacteria and archaea phylogenetically nested within the Asgard clade. Two recent studies explore the metabolic capabilities of Asgard lineages, supporting refined symbiotic metabolic interactions that might have operated at the dawn of eukaryogenesis.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>Fanconi Anemia in Pediatric Medulloblastoma and Fanconi Anemia</code> | <code>The outcome of children with medulloblastoma (MB) and Fanconi Anemia (FA), an inherited DNA repair deficiency, has not been described systematically. Treatment is complicated by high vulnerability to treatment-associated side effects, yet structured data are lacking. This study aims to give a comprehensive overview of clinical and molecular characteristics of pediatric FA MB patients.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | <code>The Sonic Hedgehog (SHH) signaling pathway is indispensable for development, and functions to activate a transcriptional program modulated by the GLI transcription factors. Here, we report that loss of a regulator of the SHH pathway, Suppressor of Fused (Sufu), resulted in early embryonic lethality in the mouse similar to inactivation of another SHH regulator, Patched1 (Ptch1). In contrast to Ptch1+/- mice, Sufu+/- mice were not tumor prone. However, in conjunction with p53 loss, Sufu+/- animals developed tumors including medulloblastoma and rhabdomyosarcoma. Tumors present in Sufu+/-p53-/- animals resulted from Sufu loss of heterozygosity. Sufu+/-p53-/- medulloblastomas also expressed a signature gene expression profile typical of aberrant SHH signaling, including upregulation of N-myc, Sfrp1, Ptch2 and cyclin D1. Finally, the Smoothened inhibitor, hedgehog antagonist, did not block growth of tumors arising from Sufu inactivation. These data demonstrate that Sufu is essential for deve...</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
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `max_steps`: 20
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 1
+- `max_steps`: 20
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `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`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 5.0.0
+- Transformers: 4.52.4
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.52.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4951e1b8dca548632eeeb2deab1ff2a45497fc6df610bf7cf0d205902e7a2186
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "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 +1,63 @@
+{
+  "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_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 512,
+  "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]"
+}