README.md

---
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
license: mit
---

# BiCA-Base

This is BiCA-Base a SOTA dense retriever 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)

## 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.*
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<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### 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}
}
```

#### If our work was helpful consider citing us ☺️
```bibtext
@misc{sinha2025bicaeffectivebiomedicaldense,
      title={BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negatives}, 
      author={Aarush Sinha and Pavan Kumar S and Roshan Balaji and Nirav Pravinbhai Bhatt},
      year={2025},
      eprint={2511.08029},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2511.08029}, 
}
```

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