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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:650596
- loss:CachedGISTEmbedLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: 'Represent this sentence for searching relevant passages: How does
a high-carbohydrate diet affect inflammation markers and cytokine levels in goats?'
sentences:
- "During carcinogenesis, the tested lactobacilli mix, especially the anti-inflammatory\
\ M2-programming VD23 strain, ameliorates the inflammatory conditions (in the\
\ early stages) and/or the pro-inflammatory M1-programming MS3 strain can boost\
\ an anti-tumour immune response with the down-stream effect of eliminating dysplastic\
\ and cancerous cells. With respect to long-term study of CRC, where cancer arises\
\ from chronic inflammation and leads to an immunosuppressive state with tumour\
\ presence, a mixture of probiotic bacteria with both anti- and pro-inflammatory\
\ (M2- and M1-programming) features was used, and this may represent a realistic\
\ approach to harnessing probiotic strains in the modulation of CRC. \nWhile body\
\ weight gain over the experimental period did not differ, there was a significant\
\ difference in daily food intake between all experimental groups. Despite the\
\ increased food intake of the DMH group compared to the DMH+P group, the rats’\
\ ability to convert food into body mass (expressed by FER) was not significantly\
\ affected. The probiotic-fed group was shown to have the highest FER, therefore\
\ it can be suggested that probiotic treatment can improve absorption and digestion\
\ of food."
- LBP is highly sensitive to LPS, and its plasma levels drastically raise up to
200% in goats fed HC diets and hence considered as reliable biomarker of systemic
inflammation (Chang, Zhang, Xu, Jin, Seyfert, et al., ; Dong et al., ). The APPs
production is stimulated by HC diet‐derived LPS in liver through activation of
toll‐like receptor‐4 (TLR‐4)‐mediated nuclear factor kappa B (NF‐kB)‐tumor necrosis
factor‐α (TNF‐α) signaling pathway in immune cells (Ciesielska et al., ; Kany
et al., ). It has been shown that HC diets induce NF‐κB expression through LPS
and thereby modulate the expressions of related cytokines, such as TNF‐α, interleukin‐1β
(IL‐1β), IL‐6, and IL‐10, and consequently altered the AAPs production in livers
of ruminants (Chang, Zhang, Xu, Jin, Guo, et al., ; Dong et al., ; Guo et al., ).
- "After 48 h transfection, cells were used in the electrophysiology assays in the\
\ automated whole-cell patch clamp system QPatch 16X (Sophion Bioscience). \n\
The extracellular solution comprised 140 NaCl, 5 KCl, 10 CaCl 2, 2 MgCl 2, 10\
\ glucose and 10 HEPES at pH 7.4 and 320 mOsm. The intracellular solution comprised\
\ (in mM) 150 KCl, 1 MgCl 2, 4 NaCl, 0.5 EGTA and 10 HEPES at pH 7.4 and 320 mOsm.\
\ Cells were maintained at a holding potential –90 mV and K + currents elicited\
\ by +20 mV pulse for 500 ms followed by –40 mV pulse for additional 500 ms."
- source_sentence: 'Represent this sentence for searching relevant passages: What
software is used for carrying out statistics in experiments?'
sentences:
- Regarding to the association of dietary intake and CRC, the cases with TT genotype
of FTO rs9939609 polymorphism had lower intake of copper (1.49 ± 0.64 vs. 1.76 ± 0.71 g/d,
p = 0.02), selenium (56.15 ± 22.97 vs. 67.26 ± 15.11 g/d, p < 0.01), β-carotene
(2189.73 ± 474.3 vs. 2461.75 ± 772.57 g/d, p = 0.01), vitamin E (10.58 ± 4.14
vs. 13.99 ± 6.4 g/d, p < 0.01), tocopherol (8.46 ± 2.91 vs. 9.79 ± 4.53 g/d, p
= 0.032), vitamin B 1 (1.91 ± 0.87 vs. 2.3 ± 0.82 g/d, p = 0.01), folate (528 ± 0.61
vs. 574.39 ± 95.19 g/d, p = 0.01), biotin (26.76 ± 3.75 vs. 29.33 ± 6.61 g/d,
p < 0.01) and higher intake of calorie (2500.48 ± 165.87 vs. 2594.64 ± 333.4 g/d,
p = 0.021), fat (86.57 ± 10.38 vs. 93.25, ± 17.13 p < 0.01), fluoride (13967.59 ± 5662.25
vs. 11112.32 ± 3051.44 g/d, p < 0.01), vitamin A (819.7 ± 251.03 vs. 712.76 ± 113.86 g/d,
p = 0.01), and vitamin K (157.9 ± 30.4 vs. 146.74 ± 21.64 g/d, p = 0.03).
- "All concentration estimates are standardized by faecal weight and depicted as\
\ concentration per gram of faeces. \nAll quantitative PCR reactions were conducted\
\ in 12.5 μl volumes using the SYBR green Master Mix (Roche). Quantitative PCR\
\ experiments were conducted on a Lightcycler LC480 instrument (Roche). Template\
\ quantity and quality was assessed using a Nanodrop spectrophotometer. Abundance\
\ estimates are standardized to the concentration of input DNA per reaction and\
\ are represented as copies per nanogram of faecal DNA. Template extraction for\
\ quantification of faecal bacteria loads: DNA was extracted from fresh faecal\
\ pellets using the PowerFecal DNA Isolation Kit (Mo Bio) following kit instructions.\
\ Bacterial loads were quantified using previously validated bacterial group-specific\
\ 16S primers. \nStatistics were carried out using JMP9.0 (SAS), Prism 6.0 (Graphpad)\
\ and R software. permutational analysis of variance was used for hypothesis testing\
\ of significance between groups shown in PcoA plots."
- 'Postmenopausal diabetic women are at higher risk to develop cardiovascular diseases
(CVD) compared with nondiabetic women. Alterations in cardiac cellular metabolism
caused by changes in sirtuins are one of the main causes of CVD in postmenopausal
diabetic women. Several studies have demonstrated the beneficial actions of the
G protein-coupled estrogen receptor (GPER) in postmenopausal diabetic CVD. However,
the molecular mechanisms by which GPER has a cardioprotective effect are still
not well understood. In this study, we used an ovariectomized (OVX) type-two diabetic
(T2D) rat model induced by high-fat diet/streptozotocin to investigate the effect
of G-1 (GPER-agonist) on sirtuins, and their downstream pathways involved in regulation
of cardiac metabolism and function. Animals were divided into five groups: Sham-Control,
T2D, OVX+T2D, OVX+T2D+Vehicle, and OVX+T2D+G-1. G-1 was administrated for six
weeks.'
- source_sentence: 'Represent this sentence for searching relevant passages: Why might
a VRAM flap be a more optimal choice for patients with an end colostomy?'
sentences:
- As they will have an end colostomy, which will be their only stoma, then a VRAM
flap is a more optimal choice given the bulk and ability to fill dead space with
this flap. Very few patients had infection or dehiscence in the postoperative
period. Donor-site hernia is a concern with the VRAM flap, particularly given
an open very large laparotomy incision which may often be a reoperation. This
occurred in 9.5% of the VRAM patients, and the same number of patients required
a delayed reoperation which was on an elective basis. VRAM, as well as ALT flaps
can be used to restore the anatomy of the pelvic floor preventing herniation into
the resection space. The ‘marine patch’ principle applies where the flap lies
on the side of hydrostatic pressure, so even if there is perineal skin breakdown
then the muscle flap component still provides cover for the abdominal contents.
Compared with Baird and colleagues, we reserved VRAM flaps for this reason to
APR and ELAPE patients. VRAM is not used in exenteration in our centre due to
two stomas being formed during urinary diversion.
- In the present study, we used a recently developed novel steatohepatitis-inducing
HFD, STHD-01 , to induce NASH. This novel HFD contains a high amount of cholesterol,
which is not contained in conventionally used HFDs, and induces the development
of severe NASH, while conventionally-used HFDs only induce mild to moderate NASH
in a shorter period of time. Another specific feature of STHD-01 is that STHD-01
does not affect fasting blood glucose levels (Additional file ). While certain
type of diet, such as methionine- and choline-deficient diet (MCD), can also cause
an advanced NASH , this diet decreases fasting blood glucose levels in experimental
animals. Since non-overweight human patients with NAFLD do not show decreased
fasting blood glucose levels compared to non-fatty liver disease patients , STHD-01
is a better approximation of the clinical condition. One obvious difference in
the phenotypes between the mice fed with the STHD-01 and the conventional HFD
is body weight gain.
- "Only 107 (13.8%) were satisfied, and 667 (84%) were dissatisfied. Regarding the\
\ reasons for dissatisfaction, 355 (45.9%) subjects reported that they did not\
\ get enough explanation, 292 (37.7%) reported that they did not get enough investigations,\
\ and only 20 (2.6%) thought that they did not get enough medications, as shown\
\ in Figure. \nOf 863 subjects with heartburn, QoL was not affected at all in\
\ 295 (34%), a little in 210 (24%), somewhat in 125 (15%), a lot in 208 (24%),\
\ and a great deal in 25 (3%) subjects. Considering a lot and a great deal as\
\ the significant impairment of QoL, 233 (27%) of the subjects had impaired QoL\
\ due to heartburn. \nThis cross‐sectional study conducted among the adult population\
\ in a rural community of Bangladesh found that about 26% of the population had\
\ heartburn, 11% chest pain, 8% globus, and 4% had dysphagia. One‐third of the\
\ study population had at least one esophageal symptom."
- source_sentence: 'Represent this sentence for searching relevant passages: What
percentage of the UAE''s population resides in Sharjah?'
sentences:
- "Currently, there is a scarcity of data about the practice and impact of OTC medication\
\ usage among pregnant women in UAE. Accordingly, this study was planned and designed\
\ with the aim of exploring the awareness and assessing the usage of OTC medications\
\ among pregnant women in Sharjah, UAE. \nThe study was conducted after the approval\
\ of the University of Sharjah Ethics Committee, Sharjah, UAE (reference number:\
\ REC-16-10-03-01-S). \nA cross-sectional survey was conducted to assess the level\
\ of awareness and knowledge of pregnant women concerning OTC drugs. The study\
\ took place in the Emirate of Sharjah, UAE, over a period of three months (October\
\ to December 2016). \nSharjah is the third largest of the seven emirates that\
\ make up the UAE and is the only one to have land on both the Arabian Gulf Coast\
\ and the Gulf of Oman. Residents of Sharjah represent around 19% of the UAE's\
\ population (4.76 million) (Ministry of Economy, 2008). Within the UAE, it has\
\ been reported that the crude birth rate or birth rate per 1,000 population was\
\ 15.54 during the year of 2014."
- "However, following a more painful surgery, children in the VR group needed rescue\
\ analgesia significantly less often ( p = 0.002). In 2021, a total of 50 children\
\ aged 6–12-years old were included in a RCT evaluating the effect of VR compared\
\ to standard screen TV in reducing anxiety for buccal infiltration anesthesia.\
\ No significant difference was observed between the groups, but female and younger\
\ patients showed higher pain scores during the dentistry procedure. Two recent\
\ meta-analyses that included a maximum of 17 studies evaluating the effect of\
\ VR on pain and anxiety in a pediatric population concluded that VR is an effective\
\ distraction intervention to reduce pain and anxiety in children. \nFinally,\
\ other medical fields have also explored the role of VR in anxiety reduction.\
\ In gastroenterology, VR has been used prior to endoscopic procedures to reduce\
\ anxiety and has shown promising results, reducing anxiety significantly in patients\
\ with a higher anxiety level (STAI-score ≥ 45) at baseline ( p = 0.007)."
- Picrosirius Red staining also demonstrated an increase in total collagen deposition
in the right carotid artery due to TAC-induced vascular changes. Alamandine treatment
effectively prevented the increase in reactive oxygen species production and depletion
of nitric oxide levels, which were induced by TAC. Finally, alamandine treatment
was also shown to prevent the increased expression of nuclear factor erythroid
2-related factor 2 and 3-nitrotyrosine that were induced by TAC. Our results suggest
that alamandine can effectively attenuate pathophysiological stress in the right
carotid artery of animals subjected to TAC.
- source_sentence: 'Represent this sentence for searching relevant passages: What
are some effects of maternal iron deficiency on adult male offspring development?'
sentences:
- "Parents report encouraging their children to engage in “healthy” lifestyle choices,\
\ including making alterations to diet, physical activity (PA), and sleep behavior,\
\ which may (1) help parents feel more in control over the impact of the condition,\
\ and (2) allow them gain a more positive outlook on the future. Unfortunately,\
\ even in the adult MS literature, there is insufficient evidence to make clinical\
\ recommendations regarding lifestyle modifications. Improving the body of literature\
\ on modifiable lifestyle factors in pediatric MS with the goal of creating guidelines\
\ that will help POMS patients and their parents deal with these difficult decisions\
\ is needed. \nOur objective in this manuscript is to summarize and identify gaps\
\ in current research on modifiable lifestyle factors and pediatric MS. Two questions\
\ guided this review: (1) Which modifiable lifestyle factors have been investigated\
\ in the context of POMS? And (2) which factors have been shown to play a role\
\ in the risk of POMS, disease course, or quality of life? \nWe used the Arksey\
\ and O’Malley framework to guide this review."
- The mRNA expression levels of the OMH-treated HT-115 cells indicated that the
cytosolic CYP1A levels were two-fold upregulated. In addition, OMH triggers the
mitochondrial release of cytochrome c, which stabilize the fundamental oxido-reduction
cycle in mitochondria. The activation of CYP1A effectively controls the pro-oxidants
and oxidative stress in colon cancer cells further, suppressing the proinflammatory
cytokines IL-1β and TNF-α, which favors the deactivation of malignant cell apoptosis
inhibitor NF-kB in colon cancer cells. The observed antioxidant capacity neutralizes
proinflammatory TNF-α/IL-1β, inhibiting protumorigenic COX-2/PGE-2 and stimulating
the apoptosis mechanism via the inhibition of NF-kB, an apoptosis inhibitor. OMH
effectively maintains the balance between Bcl-2 and Bax (Bcl-2-associated X pro-apoptotic
gene) and inclines the cells to apoptotic stimulation.
- "We found three differentially abundant taxonomic classes in the IDD group using\
\ an LDA effect size calculation with an LDA score higher than 4.0. The results\
\ showed that the Bacteroidaceae genus Bacteroides and Lachnospiraceae genus Marvinbryantia\
\ were significantly increased in rats in the IDD group compared to rats in the\
\ other groups (C). \nIn this study, we showed that maternal iron deficiency may\
\ program and alter adult male offspring development with regard to spatial learning\
\ and memory, dorsal hippocampus BDNF expression, gut microbiota, and SCFA concentrations.\
\ Our results showed that the adult male offspring of rats that were fed a low-iron\
\ diet before pregnancy and throughout the lactation period had (1) spatial deficits\
\ via a Morris water maze evaluation; (2) decreased dorsal hippocampal BDNF mRNA\
\ and protein concentrations accompanied by a low TrkB abundance; (3) a decreased\
\ plasma acetate concentration without changes in butyrate and propionate concentrations;\
\ (4) enrichment of the Bacteroidaceae genus Bacteroides and Lachnospiraceae genus\
\ Marvinbryantia."
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.5853673532124193
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7196126652320934
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7634798647402398
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8083922533046418
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5853673532124193
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2398708884106978
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15269597294804796
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0808392253304642
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5853673532124193
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7196126652320934
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7634798647402398
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8083922533046418
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6971481810101028
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6614873816111168
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6662955818767544
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the csv dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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 = [
'Represent this sentence for searching relevant passages: What are some effects of maternal iron deficiency on adult male offspring development?',
'We found three differentially abundant taxonomic classes in the IDD group using an LDA effect size calculation with an LDA score higher than 4.0. The results showed that the Bacteroidaceae genus Bacteroides and Lachnospiraceae genus Marvinbryantia were significantly increased in rats in the IDD group compared to rats in the other groups (C). \nIn this study, we showed that maternal iron deficiency may program and alter adult male offspring development with regard to spatial learning and memory, dorsal hippocampus BDNF expression, gut microbiota, and SCFA concentrations. Our results showed that the adult male offspring of rats that were fed a low-iron diet before pregnancy and throughout the lactation period had (1) spatial deficits via a Morris water maze evaluation; (2) decreased dorsal hippocampal BDNF mRNA and protein concentrations accompanied by a low TrkB abundance; (3) a decreased plasma acetate concentration without changes in butyrate and propionate concentrations; (4) enrichment of the Bacteroidaceae genus Bacteroides and Lachnospiraceae genus Marvinbryantia.',
'Parents report encouraging their children to engage in “healthy” lifestyle choices, including making alterations to diet, physical activity (PA), and sleep behavior, which may (1) help parents feel more in control over the impact of the condition, and (2) allow them gain a more positive outlook on the future. Unfortunately, even in the adult MS literature, there is insufficient evidence to make clinical recommendations regarding lifestyle modifications. Improving the body of literature on modifiable lifestyle factors in pediatric MS with the goal of creating guidelines that will help POMS patients and their parents deal with these difficult decisions is needed. \nOur objective in this manuscript is to summarize and identify gaps in current research on modifiable lifestyle factors and pediatric MS. Two questions guided this review: (1) Which modifiable lifestyle factors have been investigated in the context of POMS? And (2) which factors have been shown to play a role in the risk of POMS, disease course, or quality of life? \nWe used the Arksey and O’Malley framework to guide this review.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5854 |
| cosine_accuracy@3 | 0.7196 |
| cosine_accuracy@5 | 0.7635 |
| cosine_accuracy@10 | 0.8084 |
| cosine_precision@1 | 0.5854 |
| cosine_precision@3 | 0.2399 |
| cosine_precision@5 | 0.1527 |
| cosine_precision@10 | 0.0808 |
| cosine_recall@1 | 0.5854 |
| cosine_recall@3 | 0.7196 |
| cosine_recall@5 | 0.7635 |
| cosine_recall@10 | 0.8084 |
| **cosine_ndcg@10** | **0.6971** |
| cosine_mrr@10 | 0.6615 |
| cosine_map@100 | 0.6663 |
<!--
## 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
#### csv
* Dataset: csv
* Size: 650,596 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: 16 tokens</li><li>mean: 26.5 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 229.67 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Represent this sentence for searching relevant passages: What conditions are excluded as secondary causes of hypercholesterolemia?</code> | <code>In addition, no abnormalities were revealed under physical examination. <br>The exclusion criteria comprised secondary causes of hypercholesterolemia, including hypothyroidism, kidney diseases, poorly-controlled diabetes, cholestasis or the use of drugs impairing lipid metabolism. <br>The investigation was approved by the Bioethics Committee of the Medical University of Lodz (RNN/191/21/KE). Informed consent was obtained from all participants. All methods were carried out in accordance with relevant guidelines and regulations. <br>All participants were interviewed for their personal history of diabetes, hypertension, smoking, cardiovascular disease, pharmacological treatment, family history of hypercholesterolemia and cardiovascular disease. During the same visit, a physical examination for the presence of corneal arcus and tendon xanthomas was performed. <br>In both the control and research groups, peripheral blood mononuclear cells (PBMCs) and serum were isolated from peripheral whole blood. All...</code> |
| <code>Represent this sentence for searching relevant passages: What type of mannose linkage in side chains has the highest impact on antibody response?</code> | <code>On the other hand, side chains with β-(1→2)-linked mannose residues, which have the highest impact on antibody response , were found only in Candida spp.. The oligomannoside sequence within S. cerevisiae mannan corresponding to antibodies associated with Crohn’s disease was assigned to be the following mannotetraoside: Man(1→3)Man(1→2)Man(1→2)Man , which is illustrated in. Therefore, the corresponding oligosaccharide 1 was selected in this study as a basis for the creation of structurally related glycoarray. Ligands 2 and 3 stem from 1 after formally replacing the terminal α-(1→3)-mannoside fragment with α-(1→2)- and β-(1→2)-mannoside units, respectively. Additional glycosylation of ligand 1 leads to the formation of ligands 4 and 5.</code> |
| <code>Represent this sentence for searching relevant passages: How do fluctuations in nest temperature affect bumblebee colonies in aboveground nest boxes?</code> | <code>Impairments to colony function, as a result a sublethal environmental stressors, are linked with reduced colony success , therefore, combined increases in worker abandonment and reduced offspring production may act to have the greatest impact on bumblebee colony success under chronic heat stress. <br>The results obtained from our laboratory study inform about the capacity of bumblebee colonies to cope with chronic warm temperatures, but there are several distinctions when transposed to natural settings. Conditions used correspond more to surface or aboveground nesting that provide minor buffering from the environment. Underground nest sites are the most frequently observed nesting strategies across multiple bumblebee species, including B. impatiens. However, surface or aboveground nest sites combined are almost as frequently reported for natural settings and even more frequent when nesting in artificial nest such as human made structures. Aboveground temperatures can cause wide fluctuatio...</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 1024, '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): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
), 'temperature': 0.01}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32768
- `num_train_epochs`: 8
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### 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`: 32768
- `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`: 5e-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`: 8
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: 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
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | cosine_ndcg@10 |
|:------:|:----:|:-------------:|:--------------:|
| 0.0526 | 1 | 7.2666 | - |
| 0.1053 | 2 | 7.2688 | - |
| 0.1579 | 3 | 6.8798 | - |
| 0.2105 | 4 | 6.0896 | - |
| 0.2632 | 5 | 5.1499 | 0.5392 |
| 0.3158 | 6 | 4.2179 | - |
| 0.3684 | 7 | 3.4166 | - |
| 0.4211 | 8 | 2.9593 | - |
| 0.4737 | 9 | 2.8846 | - |
| 0.5263 | 10 | 2.8879 | 0.5541 |
| 0.5789 | 11 | 2.728 | - |
| 0.6316 | 12 | 2.5792 | - |
| 0.6842 | 13 | 2.4242 | - |
| 0.7368 | 14 | 2.2856 | - |
| 0.7895 | 15 | 2.2488 | 0.5852 |
| 0.8421 | 16 | 2.1646 | - |
| 0.8947 | 17 | 2.0432 | - |
| 0.9474 | 18 | 1.9749 | - |
| 1.0 | 19 | 1.8132 | - |
| 1.0526 | 20 | 1.8851 | 0.6135 |
| 1.1053 | 21 | 1.8024 | - |
| 1.1579 | 22 | 1.777 | - |
| 1.2105 | 23 | 1.7047 | - |
| 1.2632 | 24 | 1.6751 | - |
| 1.3158 | 25 | 1.6875 | 0.6283 |
| 1.3684 | 26 | 1.6396 | - |
| 1.4211 | 27 | 1.5756 | - |
| 1.4737 | 28 | 1.5591 | - |
| 1.5263 | 29 | 1.533 | - |
| 1.5789 | 30 | 1.5035 | 0.6449 |
| 1.6316 | 31 | 1.4705 | - |
| 1.6842 | 32 | 1.4446 | - |
| 1.7368 | 33 | 1.4092 | - |
| 1.7895 | 34 | 1.4139 | - |
| 1.8421 | 35 | 1.3996 | 0.6557 |
| 1.8947 | 36 | 1.365 | - |
| 1.9474 | 37 | 1.3397 | - |
| 2.0 | 38 | 1.2443 | - |
| 2.0526 | 39 | 1.3322 | - |
| 2.1053 | 40 | 1.2862 | 0.6632 |
| 2.1579 | 41 | 1.2965 | - |
| 2.2105 | 42 | 1.2544 | - |
| 2.2632 | 43 | 1.2474 | - |
| 2.3158 | 44 | 1.2748 | - |
| 2.3684 | 45 | 1.2509 | 0.6688 |
| 2.4211 | 46 | 1.2271 | - |
| 2.4737 | 47 | 1.2172 | - |
| 2.5263 | 48 | 1.2263 | - |
| 2.5789 | 49 | 1.1919 | - |
| 2.6316 | 50 | 1.1962 | 0.6748 |
| 2.6842 | 51 | 1.1732 | - |
| 2.7368 | 52 | 1.1683 | - |
| 2.7895 | 53 | 1.1711 | - |
| 2.8421 | 54 | 1.1783 | - |
| 2.8947 | 55 | 1.1353 | 0.6784 |
| 2.9474 | 56 | 1.1301 | - |
| 3.0 | 57 | 1.0551 | - |
| 3.0526 | 58 | 1.1436 | - |
| 3.1053 | 59 | 1.0967 | - |
| 3.1579 | 60 | 1.1259 | 0.6822 |
| 3.2105 | 61 | 1.085 | - |
| 3.2632 | 62 | 1.1107 | - |
| 3.3158 | 63 | 1.104 | - |
| 3.3684 | 64 | 1.1113 | - |
| 3.4211 | 65 | 1.0884 | 0.6849 |
| 3.4737 | 66 | 1.079 | - |
| 3.5263 | 67 | 1.0946 | - |
| 3.5789 | 68 | 1.0751 | - |
| 3.6316 | 69 | 1.0585 | - |
| 3.6842 | 70 | 1.0601 | 0.6877 |
| 3.7368 | 71 | 1.0576 | - |
| 3.7895 | 72 | 1.0558 | - |
| 3.8421 | 73 | 1.0642 | - |
| 3.8947 | 74 | 1.0349 | - |
| 3.9474 | 75 | 1.0368 | 0.6889 |
| 4.0 | 76 | 0.9558 | - |
| 4.0526 | 77 | 1.0487 | - |
| 4.1053 | 78 | 1.0164 | - |
| 4.1579 | 79 | 1.0359 | - |
| 4.2105 | 80 | 1.0095 | 0.6908 |
| 4.2632 | 81 | 1.0194 | - |
| 4.3158 | 82 | 1.0359 | - |
| 4.3684 | 83 | 1.0266 | - |
| 4.4211 | 84 | 1.0161 | - |
| 4.4737 | 85 | 1.0188 | 0.6913 |
| 4.5263 | 86 | 1.0265 | - |
| 4.5789 | 87 | 1.0193 | - |
| 4.6316 | 88 | 1.0052 | - |
| 4.6842 | 89 | 0.9994 | - |
| 4.7368 | 90 | 1.0024 | 0.6934 |
| 4.7895 | 91 | 1.0134 | - |
| 4.8421 | 92 | 1.0259 | - |
| 4.8947 | 93 | 0.9807 | - |
| 4.9474 | 94 | 0.9947 | - |
| 5.0 | 95 | 0.9139 | 0.6945 |
| 5.0526 | 96 | 0.9956 | - |
| 5.1053 | 97 | 0.9615 | - |
| 5.1579 | 98 | 0.9942 | - |
| 5.2105 | 99 | 0.9616 | - |
| 5.2632 | 100 | 0.9848 | 0.6947 |
| 5.3158 | 101 | 0.9967 | - |
| 5.3684 | 102 | 0.9861 | - |
| 5.4211 | 103 | 0.9694 | - |
| 5.4737 | 104 | 0.984 | - |
| 5.5263 | 105 | 0.9953 | 0.6953 |
| 5.5789 | 106 | 0.987 | - |
| 5.6316 | 107 | 0.9745 | - |
| 5.6842 | 108 | 0.9582 | - |
| 5.7368 | 109 | 0.957 | - |
| 5.7895 | 110 | 0.9826 | 0.6960 |
| 5.8421 | 111 | 0.9911 | - |
| 5.8947 | 112 | 0.96 | - |
| 5.9474 | 113 | 0.9593 | - |
| 6.0 | 114 | 0.8886 | - |
| 6.0526 | 115 | 0.9722 | 0.6963 |
| 6.1053 | 116 | 0.9507 | - |
| 6.1579 | 117 | 0.9767 | - |
| 6.2105 | 118 | 0.9394 | - |
| 6.2632 | 119 | 0.9569 | - |
| 6.3158 | 120 | 0.9674 | 0.6965 |
| 6.3684 | 121 | 0.9674 | - |
| 6.4211 | 122 | 0.9606 | - |
| 6.4737 | 123 | 0.96 | - |
| 6.5263 | 124 | 0.9767 | - |
| 6.5789 | 125 | 0.9664 | 0.6968 |
| 6.6316 | 126 | 0.948 | - |
| 6.6842 | 127 | 0.9581 | - |
| 6.7368 | 128 | 0.9491 | - |
| 6.7895 | 129 | 0.9627 | - |
| 6.8421 | 130 | 0.9723 | 0.6971 |
| 6.8947 | 131 | 0.9447 | - |
| 6.9474 | 132 | 0.9502 | - |
| 7.0 | 133 | 0.8796 | - |
| 7.0526 | 134 | 0.9589 | - |
| 7.1053 | 135 | 0.9377 | 0.6971 |
| 7.1579 | 136 | 0.9573 | - |
| 7.2105 | 137 | 0.9369 | - |
| 7.2632 | 138 | 0.9559 | - |
| 7.3158 | 139 | 0.9662 | - |
| 7.3684 | 140 | 0.9615 | 0.6971 |
| 7.4211 | 141 | 0.9555 | - |
| 7.4737 | 142 | 0.9579 | - |
| 7.5263 | 143 | 0.9719 | - |
| 7.5789 | 144 | 0.9664 | - |
| 7.6316 | 145 | 0.9554 | 0.6972 |
| 7.6842 | 146 | 0.9526 | - |
| 7.7368 | 147 | 0.9456 | - |
| 7.7895 | 148 | 0.9621 | - |
| 7.8421 | 149 | 0.9669 | - |
| 7.8947 | 150 | 0.9473 | 0.6971 |
| 7.9474 | 151 | 0.9519 | - |
| 8.0 | 152 | 0.8705 | - |
</details>
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.0
- Datasets: 2.19.2
- 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",
}
```
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