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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:110575
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: colon health because of the fiber content , plant-based diets may
produce healthier stools and lead to larger bowel movements , which is important
for the prevention of a number of medical conditions . interestingly , antioxidant-rich
fruits and vegetables appear to increase stool size independent of fiber . furthermore
sentences:
- 'Abstract BACKGROUND: The aging process can lead to a decline in cellular immunity.
Therefore, the elderly could benefit from safe and effective interventions that
restore cellular immune functions. OBJECTIVE: We determined whether dietary supplementation'
- 'Abstract OBJECTIVES: Standard therapies for antibiotic-associated diarrhea (AAD)
and Clostridium difficile-associated diarrhea (CDAD) have limited efficacy. Probiotic
prophylaxis is a promising alternative for reduction of AAD and CDAD incidence.'
- 'Abstract BACKGROUND: Heterocyclic amines, mutagens formed in meats cooked at
high temperatures, have been demonstrated as mammary carcinogens in animals. We
conducted a nested, case-control study among 41836 cohort members of the Iowa'
- source_sentence: 'supplements contrary to the claims of many sellers of supplements
( including bad advice from health food stores ) , we should strive to get most
of our nutrients from produce not pills , though there are rare diseases that
require supplementation.there are tens of thousands of phytonutrients in plants'
sentences:
- Abstract Various dietary flavonoids were evaluated in vitro for their inhibitory
effect on xanthine oxidase, which has been implicated in oxidative injury to tissue
by ischemia-reperfusion. Xanthine oxidase activity was determined by directly
- 'Abstract PURPOSE: Age and advanced disease in the fellow eye are the two most
important risk factors for age-related macular degeneration (AMD). In this study,
the authors investigated the relationship between these variables and the optical
density of macular pigment (MP) in a group of subjects from a northern European'
- 'Abstract BACKGROUND AND METHODS: We estimated the prevalence of self-reported
asthma in adult Indians and examined several risk factors influencing disease
prevalence. Analysis is based on 99 574 women and 56 742 men aged 20–49 years
included in India’s third National Family Health Survey, 2005–2006. Multiple logistic
regression analysis was used to estimate the prevalence odds ratios for asthma,
adjusting for various risk factors. RESULTS: The prevalence of self-reported'
- source_sentence: harvard nurses ' health study - - plant-based diets , fruit , vegetables
, cancer , vegetarians , mortality , vegans , oxidative stress , breast cancer
, inflammation , fat , meat , animal fat , antioxidants , women 's health - -
sentences:
- Abstract The effect of meat consumption on cancer risk is a controversial issue.
However, recent meta-analyses show that high consumers of cured meats and red
meat are at increased risk of colorectal cancer. This increase is significant
- 'Abstract Background: Nitrate and nitrite are present in many foods and are precursors
of N-nitroso compounds, known animal carcinogens and potential human carcinogens.
We prospectively investigated the association between nitrate and nitrite intake'
- 'Abstract Background It is unknown whether individuals at high cardiovascular
risk sustain a benefit in cardiovascular disease from increased olive oil consumption.
The aim was to assess the association between total olive oil intake, its varieties
(extra virgin and common olive oil) and the risk of cardiovascular disease and'
- source_sentence: peaches - - fruit , phytonutrients , carrots , vegetables , antioxidants
, bananas , vision , supplements , oranges , zeaxanthin , apples , anthocyanins
, cancer , blueberries , berries - -
sentences:
- 'Abstract Purpose To explore the association between consumption of fruits and
vegetables and the presence of glaucoma in older African American women. Design
Cross-sectional study. Methods Disc photographs and suprathreshold visual fields'
- Abstract Previous cohort and case-control studies on the association between cruciferous
vegetables consumption and risk of renal cell carcinoma have illustrated conflicting
results so far. To demonstrate the potential association between them, a meta-analysis
- 'Abstract Hit Reaction Time latencies (HRT) in the Continuous Performance Test
(CPT) measure the speed of visual information processing. The latencies may involve
different neuropsychological functions depending on the time from test initiation,'
- source_sentence: 'wart cancer viruses in food last year , i talked about butcher
’ s warts , a condition that afflicts those who handle fresh meat for a living
because of the viruses in meat , but it ’ s more than just a cosmetic issue .
earlier this year , a landmark study of cancer mortality in poultry workers was'
sentences:
- 'Abstract AIMS: In animals, intracerebroventricular glucose and fructose have
opposing effects on appetite and weight regulation. In humans, functional brain
magnetic resonance imaging (fMRI) studies during glucose ingestion or infusion'
- Abstract Purpose The effect of brewers’ yeast (1,3)-(1,6)-beta-d-glucan consumption
on the number of common cold episodes in healthy subject was investigated. Methods
In a placebo-controlled, double-blind, randomized, multicentric clinical trial,
- Abstract Background In October 2007, a cluster of patients experiencing a novel
polyradiculoneuropathy was identified at a pork abattoir (Plant A). Patients worked
in the primary carcass processing area (warm room); the majority processed severed
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 sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dev
type: dev
metrics:
- type: cosine_accuracy@1
value: 0.4382716049382716
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6820987654320988
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7592592592592593
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8333333333333334
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4382716049382716
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3765432098765432
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.35555555555555557
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.3006172839506173
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.060621844168537185
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11706769457554864
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.16024590873705977
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23691386170667908
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3884628362478446
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.580213844797178
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.25377737715713644
name: Cosine Map@100
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned on NFCorpus from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2).
It is specialised for Information retrieval (IR). Here is the relative improvement compared to the original model:
| Metric | Value | Value after finetuning |
|:--------------------|:-----------|:-----------------------|
| cosine_accuracy@1 | 0.34 | 0.49 |
| cosine_accuracy@3 | 0.57 | 0.68 |
| cosine_accuracy@5 | 0.64 | 0.76 |
| cosine_accuracy@10 | 0.72 | 0.83 |
| cosine_ndcg@10 | 0.26 | 0.39 |
| cosine_mrr@10 | 0.47 | 0.58 |
| cosine_map@100 | 0.12 | 0.26 |
More information on training and dev can be found below.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 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/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(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("dboyker-code/all-MiniLM-L6-v2-nfcorpus")
# Run inference
queries = [
"wart cancer viruses in food last year , i talked about butcher \u2019 s warts , a condition that afflicts those who handle fresh meat for a living because of the viruses in meat , but it \u2019 s more than just a cosmetic issue . earlier this year , a landmark study of cancer mortality in poultry workers was released . we \u2019 ve known that people who handle a lot of fresh chicken get a lot of warts on their hands , but the concern is that some of the wart viruses are oncogenic , or cancer-causing . workers in poultry slaughtering and processing are exposed to these cancer-causing viruses , some of which are the most potent cancer-causing agents known in animals , but what does that mean for people ? well , compared to the general population , poultry workers appear to have an excess of cancers of the mouth , nasal cavities , throat . cancer of the tongue , the tonsils , the inner ear , then down the esophagus , rectal / anal cancer , and liver , bone marrow , and blood cancers as well . the reason it \u2019 s so important to study this group is because it \u2019 s possible that the cancer-causing viruses present in poultry and poultry products could be transmitted to anyone handing raw poultry . proper cooking will kill any and all chicken wart and cancer viruses , but the problem is that meat may come into our homes fresh or frozen and contaminate our hands or kitchen surfaces before it gets into the pot . same concern with other meat . there was a fascinating case report about pork intake and human papillomavirus , hpv , which can cause cancerous anal and genital warts . oh , the poor guy . 19 years old . giant warty tumor nearly an inch in diameter protruding from the tip of his penis . they cut it off , but it grew right back and so they asked for a dietary history . he was eating more than a pound of pork a day . they told him to stop the pork , and the tumor completely regressed on its own \u2014 totally disappeared . the doctors were so blown away , they even went as far as to suggest that the low cervical cancer rates in israel could be because they eat so little pork . so why do i have warts on my fingers ? i have been vegan for four and a half years . how can i get rid of them ? the animal you most likely got your wart virus infection from was homo sapiens . wart viruses are thought to be typically transmitted when using a towel or something someone else with a wart has used . salicylic acid is probably the most effective treatment ( 75 % success rate compared to about 50 % for placebo ) . thanks for your question , heidi ! hi dr i love your videos , keep up the good work . i have been trying to find information about the prevention of sore throats on your website . i am a vegetarian and do no have milk but i seem to constantly getting a sore throat can you help ? hi andrew , there are a number of reasons why someone might get a sore throat . if you \u2019 re getting them often , you might want to look at possible lifestyle factors . things like pollution , not washing your hands , or even dehydration could contribute to the frequency and severity of the symptoms you \u2019 re feeling . preventative dietary strategy ? drink lots of fluids ( hot or room temperature ) when you \u2019 re feeling a sore throat come on . you might want to try a warm bowl of miso soup to get the pro-biotic benefits ( just don \u2019 t over-heat and kill the health promoting enzymes ) . avoid alcohol . and make sure you \u2019 re getting vitamins and minerals such as zinc , vitamin e and vitamin c. oh , and make sure you \u2019 re getting enough sleep : http : / / nutritionfacts.org / videos / sleep-immunity / also , please check out my associated blog post : http : / / nutritionfacts.org / blog / 2012 / 05 / 17 / poultry-and-penis-cancer / ! please also check out my associated blog post , poultry and penis cancer ! butcher \u0027s warts , cancer , carcinogens , chicken , colon health , ear health , esophageal cancer , esophagus health , inner ear cancer , mortality , mouth cancer , nasal cavity cancer , oral health , pork , poultry , poultry workers , skin health , throat cancer , throat health , tongue cancer , tonsil cancer , viral infections , wart viruses , warts the wart-causing viruses in animals may present more than just a cosmetic issue for consumers . other videos on cancer viruses and meat include : chicken dioxins , viruses , or antibiotics ? carcinogenic retrovirus found in eggs poultry exposure tied to liver and pancreatic cancer poultry exposure and neurological diseaseplease feel free to post any ask-the-doctor type questions here in the comments section and i \u2019 d be happy to try to answer them . and check out the other videos on poultry . also , there are 1,686 other subjects covered in the rest of my videos--please feel free to explore them as well ! for more context , check out my associated blog post , poultry and penis cancer .",
]
documents = [
'Abstract Background In October 2007, a cluster of patients experiencing a novel polyradiculoneuropathy was identified at a pork abattoir (Plant A). Patients worked in the primary carcass processing area (warm room); the majority processed severed heads (head-table). An investigation was initiated to determine risk factors for illness. Methods and Results Symptoms of the reported patients were unlike previously described occupational associated illnesses. A case-control study was conducted at Plant A. A case was defined as evidence of symptoms of peripheral neuropathy and compatible electrodiagnostic testing in a pork abattoir worker. Two control groups were used - randomly selected non-ill warm-room workers (n\u200a=\u200a49), and all non-ill head-table workers (n\u200a=\u200a56). Consenting cases and controls were interviewed and blood and throat swabs were collected. The 26 largest U.S. pork abattoirs were surveyed to identify additional cases. Fifteen cases were identified at Plant A; illness onsets occurred during May 2004–November 2007. Median age was 32 years (range, 21–55 years). Cases were more likely than warm-room controls to have ever worked at the head-table (adjusted odds ratio [AOR], 6.6; 95% confidence interval [CI], 1.6–26.7), removed brains or removed muscle from the backs of heads (AOR, 10.3; 95% CI, 1.5–68.5), and worked within 0–10 feet of the brain removal operation (AOR, 9.9; 95% CI, 1.2–80.0). Associations remained when comparing head-table cases and head-table controls. Workers removed brains by using compressed air that liquefied brain and generated aerosolized droplets, exposing themselves and nearby workers. Eight additional cases were identified in the only two other abattoirs using this technique. The three abattoirs that used this technique have stopped brain removal, and no new cases have been reported after 24 months of follow up. Cases compared to controls had higher median interferon-gamma (IFNγ) levels (21.7 pg/ml; vs 14.8 pg/ml, P<0.001). Discussion This novel polyradiculoneuropathy was associated with removing porcine brains with compressed air. An autoimmune mechanism is supported by higher levels of IFNγ in cases than in controls consistent with other immune mediated illnesses occurring in association with neural tissue exposure. Abattoirs should not use compressed air to remove brains and should avoid procedures that aerosolize CNS tissue. This outbreak highlights the potential for respiratory or mucosal exposure to cause an immune-mediated illness in an occupational setting.',
'Abstract Purpose The effect of brewers’ yeast (1,3)-(1,6)-beta-d-glucan consumption on the number of common cold episodes in healthy subject was investigated. Methods In a placebo-controlled, double-blind, randomized, multicentric clinical trial, 162 healthy participants with recurring infections received 900\xa0mg of either placebo (n\xa0=\xa081) or an insoluble yeast (1,3)-(1,6)-beta-d-glucan preparation (n\xa0=\xa081) per day over a course of 16\xa0weeks. Subjects were instructed to document each occurring common cold episode in a diary and to rate ten predefined infection symptoms during an infections period, resulting in a symptom score. The subjects were examined by the investigator during the episode visit on the 5th day of each cold episode. Results In the per protocol population, supplementation with insoluble yeast (1,3)-(1,6)-beta-glucan reduced the number of symptomatic common cold infections by 25\xa0% as compared to placebo (p\xa0=\xa00.041). The mean symptom score was 15\xa0% lower in the beta-glucan as opposed to the placebo group (p\xa0=\xa00.125). Beta-glucan significantly reduced sleep difficulties caused by cold episode as compared to placebo (p\xa0=\xa00.028). Efficacy of yeast beta-glucan was rated better than the placebo both by physicians (p\xa0=\xa00.004) participants (p\xa0=\xa00.012). Conclusion The present study demonstrated that yeast beta-glucan preparation increased the body’s potential to defend against invading pathogens.',
'Abstract AIMS: In animals, intracerebroventricular glucose and fructose have opposing effects on appetite and weight regulation. In humans, functional brain magnetic resonance imaging (fMRI) studies during glucose ingestion or infusion have demonstrated suppression of hypothalamic signalling, but no studies have compared the effects of glucose and fructose. We therefore sought to determine if the brain response differed to glucose vs. fructose in humans independently of the ingestive process. METHODS: Nine healthy, normal weight subjects underwent blood oxygenation level dependent (BOLD) fMRI measurements during either intravenous (IV) glucose (0.3 mg/kg), fructose (0.3 mg/kg) or saline, administered over 2 min in a randomized, double-blind, crossover study. Blood was sampled every 5 min during a baseline period and following infusion for 60 min in total for glucose, fructose, lactate and insulin levels. RESULTS: No significant brain BOLD signal changes were detected in response to IV saline. BOLD signal in the cortical control areas increased during glucose infusion (p = 0.002), corresponding with increased plasma glucose and insulin levels. In contrast, BOLD signal decreased in the cortical control areas during fructose infusion (p = 0.006), corresponding with increases of plasma fructose and lactate. Neither glucose nor fructose infusions significantly altered BOLD signal in the hypothalamus. CONCLUSION: In normal weight humans, cortical responses as assessed by BOLD fMRI to infused glucose are opposite to those of fructose. Differential brain responses to these sugars and their metabolites may provide insight into the neurologic basis for dysregulation of food intake during high dietary fructose intake. © 2011 Blackwell Publishing Ltd.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 384] [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.6485, 0.3633, 0.2052]])
```
<!--
### 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
* Dataset: `dev`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) on NFCorpus.
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4383 |
| cosine_accuracy@3 | 0.6821 |
| cosine_accuracy@5 | 0.7593 |
| cosine_accuracy@10 | 0.8333 |
| **cosine_ndcg@10** | **0.3885** |
| cosine_mrr@10 | 0.5802 |
| cosine_map@100 | 0.2538 |
<!--
## 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: 110,575 training samples
* Columns: <code>query</code> and <code>doc</code>
* Approximate statistics based on the first 1000 samples:
| | query | doc |
|:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 39 tokens</li><li>mean: 193.4 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 67 tokens</li><li>mean: 239.95 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| query | doc |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>phytonutrients plants contain more than 100,000 phytonutrients , one of the reasons nine servings of fruits and vegetables a day are recommended . some phytonutrients are fat soluble and , thus , need fat to be properly absorbed . certain phytonutrients are also better absorbed from cooked , rather than raw , food . phytonutrients may in part account for the benefits of whole plant foods in cancer prevention . dates , berries , strawberries , coffee , earl grey tea , chai tea , and green tea ( see also here , here ) are high in phytonutrients . milk and soymilk , however , may block the absorption of phytonutrients ( see also here ) .variety in a diet appears to boost the effectiveness of phytonutrients due to nutrient synergy and also because different plants and vegetables have different phytonutrients . a healthy eating index has even been created based on phytochemical consumption . phytonutrients , when eaten , literally bathe our systems , as evidenced by garlic breath and pink u...</code> | <code>Abstract Caregivers of Alzheimer’s disease patients endure chronic stress associated with a decline of immune function. To assess the psychological and immunological changes of caregivers, we compared depressive symptoms, PBMC composition, in vitro activation-induced proliferation and cytokine production, and telomere length and telomerase activity of 82 individuals (41 caregivers and 41 age- and gender-matched controls). We found depressive symptoms were significantly higher in caregivers than in controls (p < 0.001). Correspondingly, caregivers had significantly lower T cell proliferation but higher production of immune-regulatory cytokines (TNF-α and IL-10) than controls in response to stimulation in vitro. We examined the impact of these changes on cellular replicative lifespan and found that caregivers had significantly shorter telomere lengths in PBMC than controls (6.2 and 6.4 kb, respectively, p < 0.05) with similar shortening in isolated T cells and monocytes and that this tel...</code> |
| <code>big sugar takes on the world health organization the world health organization recommends we reduce our consumption of salt , trans fats , saturated fats , and added sugars . why ? because consumption of such foods is the cause of at least 14 million deaths every year from chronic diseases.several decades ago , it was heresy to talk about an impending global pandemic of obesity , but now we ’ re seeing chronic disease rates skyrocket around the world . we have exported our western diet to the far reaches of the planet , with white flour , sugar , fat , and animal-source foods replacing beans , peas , lentils , other vegetables , and whole grains.understanding the reasons underlying this trend toward increased consumption of animal products , oils , and sugar and the reduced consumption of whole plant foods begins with understanding the purposeful economic manipulations that have occurred since world war ii relating to agricultural policies around the world.for example , the u.s. govern...</code> | <code>Abstract The effect of polyphenols, phenolic acids and tannins (PPTs) from strawberry and apple on uptake and apical to basolateral transport of glucose was investigated using Caco-2 intestinal cell monolayers. Substantial inhibition on both uptake and transport was observed by extracts from both strawberry and apple. Using sodium-containing (glucose transporters SGLT1 and GLUT2 both active) and sodium-free (only GLUT2 active) conditions, we show that the inhibition of GLUT2 was greater than that of SGLT1. The extracts were analyzed and some of the constituent PPTs were also tested. Quercetin-3-O-rhamnoside (IC₅₀ =31 μM), phloridzin (IC₅₀=146 μM), and 5-caffeoylquinic acid (IC₅₀=2570 μM) contributed 26, 52 and 12%, respectively, to the inhibitory activity of the apple extract, whereas pelargonidin-3-O-glucoside (IC₅₀=802 μM) contributed 26% to the total inhibition by the strawberry extract. For the strawberry extract, the inhibition of transport was non-competitive based on kinetic ana...</code> |
| <code>alzheimer 's and apple juice what if you or a loved one already has alzheimer ’ s ? the processed apples institute funded a study suggesting apple juice may help prevent alzheimer 's and now they 're back , after changing their name to the apple products research and education council , funding funded a new study in which instead of dripping apple juice on nerve cells in a petri dish they had 21 alzheimers patients drink a cup of apple juice every day for a month to see what would happen . works in the lab , but does it work in the nursing home ? after a month of apple juice , no change in cognitive performance or day-to-day functioning but they did report improvements in mood and behavior . less apathy , anxiety , agitation , depression , and delusion . did seem to hallucinate a bit more though . now this was a tiny study with no placebo control — but hey , there are worse things doctors could be giving them then apple juice , though if i was going to repeat the study i ’ d choose pom...</code> | <code>Abstract Increased oxidative stress contributes to the decline in cognitive performance during normal aging and in neurodegenerative conditions such as Alzheimer's disease. Dietary supplementation with fruits and vegetables that are high in antioxidant potential have in some cases compensated for oxidative stress. Herein, we examined whether apple juice could alleviate the neurotoxic consequences of exposure of cultured neuronal cells to amyloid-beta (Abeta), since at least a portion of the neurotoxicity of Abeta is due to oxidative stress. Apple juice concentrate (AJC; 70 degree brix) was diluted into culture medium of SH-SY-5Y human neuroblastoma cells that had been differentiated for 7 days with 5 microM retinoic acid concurrent with the addition of 20 microM Abeta. AJC prevented the increased generation of reactive oxygen species (ROS) normally induced by Abeta treatment under these conditions. AJC also prevented Abeta-induced calcium influx and apoptosis, each of which results in ...</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",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 11,385 evaluation samples
* Columns: <code>query</code> and <code>doc</code>
* Approximate statistics based on the first 1000 samples:
| | query | doc |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 28 tokens</li><li>mean: 182.05 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 238.63 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| query | doc |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>golden glow the reason. the peacock looks like this and the peahen looks like this , is because of sexual selection . the females prefer males with the most elaborate displays . how does this make sense evolutionarily ? how can appearance offer insight into the health of a potential mate.having such ornate , excessive plumage is risky . the poor peacock can hardly get off the ground . and this is hardly effective camouflage against predators . and so the fact that the peacock could survive despite such costly ornamentation offers a sense of how robust the bird ’ s genetics must be . so natural selection might favor females attracted to that sort of thing.so are you just born attractive or not ? well some species use diet to increase their sexual attractiveness . great tits prefer carotenoid-rich caterpillars which play an role in plumage pigmentation ; their breasts become brighter yellow , which is more attractive to potential mates , and a signal of how good they may be at procuring ...</code> | <code>Abstract Studies have shown an association between depression and both antioxidant levels and oxidant stress, but generally have not included intakes of antioxidants and antioxidant-rich fruits and vegetables. The present study examined the cross-sectional associations between clinically-diagnosed depression and intakes of antioxidants, fruits and vegetables in a cohort of older adults. Antioxidant, fruit and vegetable intakes were assessed in 278 elderly participants (144 with depression, 134 without depression) using a Block 1998 food frequency questionnaire, which was administered between 1999 and 2007. All participants were age 60 years or over. Vitamin C, lutein and cryptoxanthin intakes were significantly lower among depressed individuals than in comparison participants (p<0.05). In addition, fruit and vegetable consumption, a primary determinant of antioxidant intake, was lower in depressed individuals. In multivariable models, controlling for age, sex, education, vascular comor...</code> |
| <code>cabbage - - vegetables , fruit , plant-based diets , standard american diet , cruciferous vegetables , vegetarians , vegans , cauliflower , greens , breast cancer , women 's health , broccoli , meat , fiber , kale - -</code> | <code>Abstract AIMS: Various spices display insulin-potentiating activity in vitro, and in particular, cinnamon spice and its phenolic extracts have been shown to exhibit these capabilities. In vivo study shows that cinnamon may have beneficial effects on glucose homeostasis; therefore the aim of this study was to further investigate this phenomenon in humans. METHODS: Seven lean healthy male volunteers, aged 26 +/- 1 years, body mass index 24.5 +/- 0.3 kg/m(2) (mean +/- s.e.m.), underwent three oral glucose tolerance tests (OGTT) supplemented with either a 5 g placebo (OGTT(control)), 5 g of cinnamon (OGTT(cin)), or 5 g of cinnamon taken 12 h before (OGTT(cin12hpre)) in a randomized-crossover design. RESULTS: Cinnamon ingestion reduced total plasma glucose responses (AUC) to oral glucose ingestion [-13% and -10% for OGTT(cin) (p < 0.05) and OGTT(cin12hpre) (p < 0.05), respectively], as well as improving insulin sensitivity as assessed by insulin sensitivity index measures based on Matsuda's...</code> |
| <code>dietary guidelines - - heart disease , cardiovascular disease , heart health , diabetes , cardiovascular health , vegetables , meat , saturated fat , standard american diet , cholesterol , beans , prediabetes , lifestyle medicine , animal fat , chicken - -</code> | <code>Abstract The hypothesis that plasma chylomicrons in persons who ingest a cholesterol-rich diet are atherogenic is evaluated. Evidence is presented that in humans, and experimental animals, chylomicron remnants as well as low-density lipoproteins are taken up by arterial cells. In persons who do not have familial hyperlipoproteinemia, atherogenesis may occur during the postprandial period. Research directions that may contribute to the evaluation of chylomicron remnants as a risk factor for atherogenesis are discussed. Lipoprotein studies after administration of a test meal containing fat and cholesterol are urgently needed.</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",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 3e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `disable_tqdm`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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`: 3e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: True
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `hub_revision`: None
- `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`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | dev_cosine_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:------------------:|
| 0.0579 | 100 | 4.5784 | 3.6899 | 0.2575 |
| 0.1158 | 200 | 3.7454 | 3.4444 | 0.2943 |
| 0.1737 | 300 | 3.6286 | 3.3864 | 0.2997 |
| 0.2316 | 400 | 3.5706 | 3.3173 | 0.3167 |
| 0.2895 | 500 | 3.4935 | 3.2910 | 0.3183 |
| 0.3474 | 600 | 3.4682 | 3.2391 | 0.3230 |
| 0.4053 | 700 | 3.4361 | 3.2089 | 0.3250 |
| 0.4632 | 800 | 3.3731 | 3.1708 | 0.3300 |
| 0.5211 | 900 | 3.3421 | 3.1532 | 0.3424 |
| 0.5790 | 1000 | 3.2904 | 3.1223 | 0.3422 |
| 0.6369 | 1100 | 3.2709 | 3.0798 | 0.3400 |
| 0.6948 | 1200 | 3.2191 | 3.0619 | 0.3503 |
| 0.7528 | 1300 | 3.1983 | 3.0471 | 0.3470 |
| 0.8107 | 1400 | 3.1622 | 3.0461 | 0.3424 |
| 0.8686 | 1500 | 3.1495 | 3.0081 | 0.3518 |
| 0.9265 | 1600 | 3.1218 | 2.9875 | 0.3544 |
| 0.9844 | 1700 | 3.0686 | 2.9786 | 0.3585 |
| 1.0423 | 1800 | 3.0237 | 2.9984 | 0.3579 |
| 1.1002 | 1900 | 2.9812 | 2.9910 | 0.3611 |
| 1.1581 | 2000 | 2.9644 | 2.9669 | 0.3622 |
| 1.2160 | 2100 | 2.9052 | 3.0249 | 0.3611 |
| 1.2739 | 2200 | 2.9233 | 2.9563 | 0.3690 |
| 1.3318 | 2300 | 2.9177 | 2.9564 | 0.3692 |
| 1.3897 | 2400 | 2.8904 | 2.9492 | 0.3709 |
| **1.4476** | **2500** | **2.9017** | **2.9441** | **0.3699** |
| 1.5055 | 2600 | 2.8931 | 2.9119 | 0.3677 |
| 1.5634 | 2700 | 2.8529 | 2.9349 | 0.3741 |
| 1.6213 | 2800 | 2.8649 | 2.9082 | 0.3740 |
| 1.6792 | 2900 | 2.8951 | 2.8919 | 0.3682 |
| 1.7371 | 3000 | 2.8254 | 2.9023 | 0.3784 |
| 1.7950 | 3100 | 2.8014 | 2.8863 | 0.3803 |
| 1.8529 | 3200 | 2.8144 | 2.9305 | 0.3875 |
| 1.9108 | 3300 | 2.8314 | 2.8928 | 0.3836 |
| 1.9687 | 3400 | 2.7778 | 2.8889 | 0.3885 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 4.4.2
- Tokenizers: 0.22.1
## 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}
}
```
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