| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:17319 |
| - loss:TripletMNRLCombinedLoss |
| base_model: intfloat/multilingual-e5-large-instruct |
| widget: |
| - source_sentence: 'query: Thrilled to spot our study in @BJSM_BMJ on injury incidence |
| & burden in youth football, taking into account the immature skeleton from a big |
| cohort during 4 back-to-back seasons @aspetar @RoaldBahr' |
| sentences: |
| - 'passage: title: Longitudinal study of six seasons of match injuries in elite |
| female rugby union |
| |
| abstract: ObjectiveTo establish match injury rates and patterns in elite female |
| rugby union players in England.We conducted a six-season (2011/2012-2013/2014 |
| and 2017/2018-2019/2020) prospective cohort study of time-loss match injuries |
| in elite-level female players in the English Premiership competition. A 24-hour |
| time-loss definition was used.Five-hundred and thirty-four time-loss injuries |
| were recorded during 13 680 hours of match exposure. Injury incidence was 39 injuries |
| per 1000 hours (95% CIs 36 to 42) with a mean severity of 48 days (95% CIs 42 |
| to 54) and median severity of 20 days (IQR: 7-57). Concussion was the most common |
| specific injury diagnosis (five concussions per 1000 hours, 95% CIs 4 to 6). The |
| tackle event was associated with the greatest burden of injury (615 days absence |
| per 1000 hours 95% CIs 340 to 1112), with ''being tackled'' specifically causing |
| the most injuries (28% of all injuries) and concussions (22% of all concussions).This |
| is the first multiple-season study of match injuries in elite women''s rugby union |
| players. Match injury incidence was similar to that previously reported within |
| international women''s rugby union. Injury prevention strategies centred on the |
| tackle would focus on high-burden injuries, which are associated with substantial |
| player time-loss and financial costs to teams as well as the high-priority area |
| of concussions.' |
| - 'passage: title: Single, Dual, and Triple Use of Cigarettes, e-Cigarettes, and |
| Snus among Adolescents in the Nordic Countries |
| |
| abstract: New tobacco and nicotine products have emerged on the market in recent |
| years. Most research has concerned only one product at a time, usually e-cigarettes, |
| while little is known about the multiple use of tobacco and nicotine products |
| among adolescents. We examined single, dual, and triple use of cigarettes, e-cigarettes, |
| and snus among Nordic adolescents, using data of 15–16-year-olds (n = 16,125) |
| from the European School Survey Project on Alcohol and other Drugs (ESPAD) collected |
| in 2015 and 2019 from Denmark, Finland, Iceland, Norway, Sweden, and the Faroe |
| Islands. Country-specific lifetime use of any of these products ranged between |
| 40% and 50%, and current use between 17% and 31%. Cigarettes were the most common |
| product in all countries except for Iceland, where e-cigarettes were remarkably |
| more common. The proportion of dual and triple users was unexpectedly high among |
| both experimental (24%–49%) and current users (31–42%). Triple use was less common |
| than dual use. The users’ patterns varied somewhat between the countries, and |
| Iceland differed substantially from the other countries, with a high proportion |
| of single e-cigarette users. More knowledge on the patterns of multiple use of |
| tobacco and nicotine products and on the potential risk and protective factors |
| is needed for targeted intervention and prevention efforts.' |
| - 'passage: title: Injury incidence and burden in a youth elite football academy: |
| a four-season prospective study of 551 players aged from under 9 to under 19 years |
| |
| abstract: Objective Investigate the incidence and burden of injuries by age group |
| in youth football (soccer) academy players during four consecutive seasons. Methods |
| All injuries that caused time-loss or required medical attention (as per consensus |
| definitions) were prospectively recorded in 551 youth football players from under |
| 9 years to under 19 years. Injury incidence (II) and burden (IB) were calculated |
| as number of injuries per squad season (s-s), as well as for type, location and |
| age groups. Results A total of 2204 injuries were recorded. 40% (n=882) required |
| medical attention and 60% (n=1322) caused time-loss. The total time-loss was 25 |
| 034 days. A squad of 25 players sustained an average of 30 time-loss injuries |
| (TLI) per s-s with an IB of 574 days lost per s-s. Compared with the other age |
| groups, U-16 players had the highest TLI incidence per s-s (95% CI lower-upper): |
| II= 59 (52 to 67); IB=992 days; (963 to 1022) and U-18 players had the greatest |
| burden per s-s: II= 42.1 (36.1 to 49.1); IB= 1408 days (1373 to 1444). Across |
| the cohort of players, contusions (II=7.7/s-s), sprains (II=4.9/s-s) and growth-related |
| injuries (II=4.3/s-s) were the most common TLI. Meniscus/cartilage injuries had |
| the greatest injury severity (95% CI lower-upper): II= 0.4 (0.3 to 0.7), IB= 73 |
| days (22 to 181). The burden (95% CI lower-upper) of physeal fractures (II= 0.8; |
| 0.6 to 1.2; IB= 58 days; 33 to 78) was double than non-physeal fractures. Summary |
| At this youth football academy, each squad of 25 players averaged 30 injuries |
| per season which resulted in 574 days lost.' |
| - source_sentence: 'query: @DrCanuckMD Pathetic loser. There''s proof they work. It''s |
| funny how the folks who refuse to wear them are the same ones claiming they don''t |
| work.' |
| sentences: |
| - 'passage: title: Impact of community masking on COVID-19: A cluster-randomized |
| trial in Bangladesh |
| |
| abstract: Persuading people to mask Even in places where it is obligatory, people |
| tend to optimistically overstate their compliance for mask wearing. How then can |
| we persuade more of the population at large to act for the greater good? Abaluck |
| et al . undertook a large, cluster-randomized trial in Bangladesh involving hundreds |
| of thousands of people (although mostly men) over a 2-month period. Colored masks |
| of various construction were handed out free of charge, accompanied by a range |
| of mask-wearing promotional activities inspired by marketing research. Using a |
| grassroots network of volunteers to help conduct the study and gather data, the |
| authors discovered that mask wearing averaged 13.3% in villages where no interventions |
| took place but increased to 42.3% in villages where in-person interventions were |
| introduced. Villages where in-person reinforcement of mask wearing occurred also |
| showed a reduction in reporting COVID-like illness, particularly in high-risk |
| individuals. —CA' |
| - 'passage: . Analysis of survey data found that on the third day before policy |
| introduction, 44% of participants reported “often” or “always” wearing a mask; |
| on the fourth day after, 100% reported “always” doing so. |
| |
| |
| title: The introduction of a mandatory mask policy was associated with significantly |
| reduced COVID-19 cases in a major metropolitan city |
| |
| No potentially confounding factors were associated with the observed change in |
| growth rates. Conclusions The mandatory mask use policy substantially increased |
| public use of masks and was associated with a significant decline in new COVID-19 |
| cases after introduction of the policy. This study strongly supports the use of |
| masks for controlling epidemics in the broader community.' |
| - 'passage: title: Food and soft drink industry has too much influence over US dietary |
| guidelines, report says |
| |
| abstract: A powerful, industry funded group is playing an “outsized role” in steering |
| the development of new US dietary guidelines and must have its influence curbed |
| to protect public health, a pressure group has urged. |
| |
| |
| In a report published this week to coincide with Coca-Cola’s annual meeting of |
| shareholders,1 the campaign group Corporate Accountability noted that over half |
| of people appointed to the US 2020 Dietary Guidelines Advisory Committee had ties |
| to the International Life Sciences Institute (ILSI), whose funders include Coke |
| and other global corporations. |
| |
| |
| ILSI was set up by a Coca-Cola executive 40 years ago in the US and operates throughout |
| the world. It is a not-for-profit organisation and …' |
| - source_sentence: 'query: The output of many crops in the US is curbed by a shortage |
| of pollinators, and most of the pollination that''s occurring is thanks to wild |
| pollinators. Compelling evidence that we need to help wild pollinators!' |
| sentences: |
| - 'passage: title: Elapsed time since BNT162b2 vaccine and risk of SARS-CoV-2 infection |
| in a large cohort |
| |
| abstract: Israel was among the first countries to launch a large-scale COVID-19 |
| vaccination campaign, and quickly vaccinated its population, achieving early control |
| over the spread of the virus. However, the number of COVID-19 cases is now rapidly |
| increasing, which may indicate that vaccine protection decreases over time. To |
| determine whether time elapsed since the second BNT162b2 messenger RNA (mRNA) |
| vaccine (Pfizer-BioNTech) injection is significantly associated with the risk |
| of post-vaccination COVID-19 infection. This is a retrospective cohort study performed |
| in a large state-mandated health care organization in Israel. All fully vaccinated |
| adults who have received a RT-PCR test between May 15, 2021 and July 26, 2021, |
| at least two weeks after their second vaccine injection were included. Patients |
| with a history of past COVID-19 infection were excluded. Positive result for the |
| RT-PCR test. The cohort included 33,993 fully vaccinated adults, 49% women, with |
| a mean age of 47 years (SD, 17 years), who received an RT-PCR test for SARS-CoV-2 |
| during the study period. The median time between the second dose of the vaccine |
| and the RT-PCR test was 146 days, interquartile range [121-167] days. 608 (1.8%) |
| patients had positive test results. There was a significantly higher rate of positive |
| results among patients who received their second vaccine dose at least 146 days |
| before the RT-PCR test compared to patients who have received their vaccine less |
| than 146 days before: odds ratio for infection was 3.00 for patients aged over |
| 60 (95% CI 1.86-5.11); 2.29 for patients aged between 40 and 59 (95% CI 1.67-3.17); |
| and 1.74 for patients aged between 18 and 39 (95% CI 1.27-2.37); P<0.001 in each |
| age group.' |
| - 'passage: title: Crop production in the USA is frequently limited by a lack of |
| pollinators |
| |
| abstract: Most of the world''s crops depend on pollinators, so declines in both |
| managed and wild bees raise concerns about food security. However, the degree |
| to which insect pollination is actually limiting current crop production is poorly |
| understood, as is the role of wild species (as opposed to managed honeybees) in |
| pollinating crops, particularly in intensive production areas. We established |
| a nationwide study to assess the extent of pollinator limitation in seven crops |
| at 131 locations situated across major crop-producing areas of the USA. We found |
| that five out of seven crops showed evidence of pollinator limitation. Wild bees |
| and honeybees provided comparable amounts of pollination for most crops, even |
| in agriculturally intensive regions. We estimated the nationwide annual production |
| value of wild pollinators to the seven crops we studied at over $1.5 billion; |
| the value of wild bee pollination of all pollinator-dependent crops would be much |
| greater. Our findings show that pollinator declines could translate directly into |
| decreased yields or production for most of the crops studied, and that wild species |
| contribute substantially to pollination of most study crops in major crop-producing |
| regions.' |
| - 'passage: title: Historical decrease in agricultural landscape diversity is associated |
| with shifts in bumble bee species occurrence |
| |
| abstract: Abstract Agricultural intensification is a key suspect among putative |
| drivers of recent insect declines, but an explicit link between historical change |
| in agricultural land cover and insect occurrence is lacking. Determining whether |
| agriculture impacts beneficial insects (e.g. pollinators), is crucial to enhancing |
| agricultural sustainability. Here, we combine large spatiotemporal sets of historical |
| bumble bee and agricultural records to show that increasing cropland extent and |
| decreasing crop richness were associated with declines in over 50% of bumble bee |
| species in the agriculturally intensive Midwest, USA. Critically, we found that |
| high crop diversity was associated with a higher occurrence of many species pre‐1950 |
| even in agriculturally dominated areas, but that current agricultural landscapes |
| are devoid of high crop diversity. Our findings suggest that insect conservation |
| and agricultural production may be compatible, with increasing on‐farm and landscape‐level |
| crop diversity predicted to have positive effects on bumble bees.' |
| - source_sentence: 'query: @user Masern sind hier passenderer Vergleich. Die Beeinträchtigung |
| des Immunsystems sollte, gegenüber Aids, umkehrbar sein' |
| sentences: |
| - 'passage: title: Long-term measles-induced immunomodulation increases overall |
| childhood infectious disease mortality |
| |
| abstract: Extra dividends from measles vaccine Vaccination against measles has |
| many benefits, not only lifelong protection against this potentially serious virus. |
| Mina et al. analyzed data collected since mass vaccination began in high-income |
| countries when measles was common. Measles vaccination is associated with less |
| mortality from other childhood infections. Measles is known to cause transient |
| immunosuppression, but close inspection of the mortality data suggests that it |
| disables immune memory for 2 to 3 years. Vaccination thus does more than safeguard |
| children against measles; it also stops other infections taking advantage of measles-induced |
| immune damage. Science , this issue p. 694' |
| - 'passage: title: Association of BCG, DTP, and measles containing vaccines with |
| childhood mortality: systematic review |
| |
| abstract: <b>Objectives</b> To evaluate the effects on non-specific and all |
| cause mortality, in children under 5, of Bacillus Calmette-Guérin (BCG), diphtheria-tetanus-pertussis |
| (DTP), and standard titre measles containing vaccines (MCV); to examine internal |
| validity of the studies; and to examine any modifying effects of sex, age, vaccine |
| sequence, and co-administration of vitamin A. <b>Design</b> Systematic review, |
| including assessment of risk of bias, and meta-analyses of similar studies. <b>Study |
| eligibility criteria</b> Clinical trials, cohort studies, and case-control |
| studies of the effects on mortality of BCG, whole cell DTP, and standard titre |
| MCV in children under 5. <b>Data sources</b> Searches of Medline, Embase, |
| Global Index Medicus, and the WHO International Clinical Trials Registry Platform, |
| supplemented by contact with experts in the field. To avoid overlap in children |
| studied across the included articles, findings from non-overlapping birth cohorts |
| were identified. <b>Results</b> Results from 34 birth cohorts were identified. |
| Most evidence was from observational studies, with some from short term clinical |
| trials. Most studies reported on all cause (rather than non-specific) mortality. |
| Receipt of BCG vaccine was associated with a reduction in all cause mortality: |
| the average relative risks were 0.70 (95% confidence interval 0.49 to 1.01) from |
| five clinical trials and 0.47 (0.32 to 0.69) from nine observational studies at |
| high risk of bias. Receipt of DTP (almost always with oral polio vaccine) was |
| associated with a possible increase in all cause mortality on average (relative |
| risk 1.38, 0.92 to 2.08) from 10 studies at high risk of bias; this effect seemed |
| stronger in girls than in boys.' |
| - 'passage: title: L’effet des dictées métacognitives-interactives sur la compétence |
| à orthographier les homophones grammaticaux en rédaction |
| |
| abstract: Les homophones grammaticaux sont souvent présentés en paires dans les |
| exercices, au risque de conforter leur confusion. Dans un projet mené au Québec |
| dans des classes du primaire et du secondaire (482 élèves), la phrase dictée du |
| jour et la dictée zéro faute ont été expérimentées pendant sept mois. Les effets |
| de ces dictées métalinguistiques-interactives sur la compétence des élèves à orthographier |
| les homophones grammaticaux font l''objet de cet article. Les résultats montrent |
| des effets positifs pour l''ensemble du groupe ; une analyse plus fine révèle |
| à qui elles profitent le plus.' |
| - source_sentence: 'query: It’s been obvious for ages that mRNA vaccines constituted |
| a 3+ dose series. A 3‑dose series is very effective. The fourth dose is still |
| better and ought to be made available. Why does Canada still label partially (2 |
| dose) vaccinated as “fully vaccinated”?' |
| sentences: |
| - 'passage: title: Protection against omicron severe disease 0-7 months after BNT162b2 |
| booster |
| |
| abstract: Abstract Following a rise in cases due to the delta variant and evidence |
| of waning immunity after 2 doses of the BNT162b2 vaccine, Israel began administering |
| a third BNT162b2 dose (booster) in July 2021. Recent studies showed that the 3rd |
| dose provides a much lower protection against infection with the omicron variant |
| compared to the delta variant and that this protection wanes quickly. In this |
| study, we used data from Israel to estimate the protection of the 3rd dose against |
| severe disease up to 7 months from receiving the booster dose. The analysis shows |
| that protection conferred by the 3rd dose against omicron did not wane over a |
| 7-month period and that a 4th dose further increased protection, with a severe |
| disease rate approximately 3-fold lower than in the 3-dose cohorts.' |
| - 'passage: title: Neurovascular injury with complement activation and inflammation |
| in COVID-19 |
| |
| abstract: The underlying mechanisms by which severe acute respiratory syndrome |
| coronavirus 2 (SARS-CoV-2) leads to acute and long-term neurological manifestations |
| remains obscure. We aimed to characterize the neuropathological changes in patients |
| with coronavirus disease 2019 and determine the underlying pathophysiological |
| mechanisms. In this autopsy study of the brain, we characterized the vascular |
| pathology, the neuroinflammatory changes and cellular and humoral immune responses |
| by immunohistochemistry. All patients died during the first wave of the pandemic |
| from March to July 2020. All patients were adults who died after a short duration |
| of the infection, some had died suddenly with minimal respiratory involvement. |
| Infection with SARS-CoV-2 was confirmed on ante-mortem or post-mortem testing. |
| Descriptive analysis of the pathological changes and quantitative analyses of |
| the infiltrates and vascular changes were performed. All patients had multifocal |
| vascular damage as determined by leakage of serum proteins into the brain parenchyma. |
| This was accompanied by widespread endothelial cell activation. Platelet aggregates |
| and microthrombi were found adherent to the endothelial cells along vascular lumina. |
| Immune complexes with activation of the classical complement pathway were found |
| on the endothelial cells and platelets. Perivascular infiltrates consisted of |
| predominantly macrophages and some CD8+ T cells. Only rare CD4+ T cells and CD20+ |
| B cells were present. Astrogliosis was also prominent in the perivascular regions. |
| Microglial nodules were predominant in the hindbrain, which were associated with |
| focal neuronal loss and neuronophagia. Antibody-mediated cytotoxicity directed |
| against the endothelial cells is the most likely initiating event that leads to |
| vascular leakage, platelet aggregation, neuroinflammation and neuronal injury. |
| Therapeutic modalities directed against immune complexes should be considered.' |
| - 'passage: title: A fourth dose of the mRNA-1273 SARS-CoV-2 vaccine improves serum |
| neutralization against the delta variant in kidney transplant recipients |
| |
| abstract: Abstract In immunocompetent subjects, the effectiveness of SARS-CoV-2 |
| vaccines against the delta variant appears three- to five-fold lower than that |
| observed against the alpha variant. Additionally, three doses of SARS-CoV-2 mRNA-based |
| vaccines might be unable to elicit a sufficient immune response against any variant |
| in immunocompromised kidney transplant recipients. This study describes the kinetics |
| of the neutralizing antibody (NAbs) response against the delta strain before and |
| after a fourth dose of a mRNA vaccine in 67 kidney transplant recipients who had |
| experienced a weak antibody response after three doses. While only 16% of patients |
| harbored NAbs against the delta strain prior to the fourth injection – this percentage |
| raised to 66% afterwards. We also found that, after the fourth dose, the NAbs |
| titer increased significantly (p=0.0001) from <7.5 (IQR : <7.5−15.1) to |
| 47.1 (IQR <7.5−284.2). Collectively, our data indicate that a fourth dose of |
| the mRNA-1273 vaccine in kidney transplant recipients with a weak antibody response |
| after three previous doses improves serum neutralization against the delta variant.' |
| 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 intfloat/multilingual-e5-large-instruct |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: 10 percent dev split |
| type: 10-percent-dev-split |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.4748051948051948 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.6581818181818182 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.7148051948051948 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.7781818181818182 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.4748051948051948 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.2193939393939394 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.14296103896103896 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.07781818181818181 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.4748051948051948 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.6581818181818182 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.7148051948051948 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.7781818181818182 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.6259059611181643 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.5771395588538446 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.5824203727726155 |
| name: Cosine Map@100 |
| --- |
| |
| # SentenceTransformer based on intfloat/multilingual-e5-large-instruct |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision 274baa43b0e13e37fafa6428dbc7938e62e5c439 --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 1024 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'}) |
| (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', '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("MinhPhuc0804/me5-512-docling-checkthat-task1-v1.2") |
| # Run inference |
| sentences = [ |
| 'query: It’s been obvious for ages that mRNA vaccines constituted a 3+ dose series. A 3‑dose series is very effective. The fourth dose is still better and ought to be made available. Why does Canada still label partially (2 dose) vaccinated as “fully vaccinated”?', |
| 'passage: title: Protection against omicron severe disease 0-7 months after BNT162b2 booster\nabstract: Abstract Following a rise in cases due to the delta variant and evidence of waning immunity after 2 doses of the BNT162b2 vaccine, Israel began administering a third BNT162b2 dose (booster) in July 2021. Recent studies showed that the 3rd dose provides a much lower protection against infection with the omicron variant compared to the delta variant and that this protection wanes quickly. In this study, we used data from Israel to estimate the protection of the 3rd dose against severe disease up to 7 months from receiving the booster dose. The analysis shows that protection conferred by the 3rd dose against omicron did not wane over a 7-month period and that a 4th dose further increased protection, with a severe disease rate approximately 3-fold lower than in the 3-dose cohorts.', |
| 'passage: title: A fourth dose of the mRNA-1273 SARS-CoV-2 vaccine improves serum neutralization against the delta variant in kidney transplant recipients\nabstract: Abstract In immunocompetent subjects, the effectiveness of SARS-CoV-2 vaccines against the delta variant appears three- to five-fold lower than that observed against the alpha variant. Additionally, three doses of SARS-CoV-2 mRNA-based vaccines might be unable to elicit a sufficient immune response against any variant in immunocompromised kidney transplant recipients. This study describes the kinetics of the neutralizing antibody (NAbs) response against the delta strain before and after a fourth dose of a mRNA vaccine in 67 kidney transplant recipients who had experienced a weak antibody response after three doses. While only 16% of patients harbored NAbs against the delta strain prior to the fourth injection – this percentage raised to 66% afterwards. We also found that, after the fourth dose, the NAbs titer increased significantly (p=0.0001) from <7.5 (IQR : <7.5−15.1) to 47.1 (IQR <7.5−284.2). Collectively, our data indicate that a fourth dose of the mRNA-1273 vaccine in kidney transplant recipients with a weak antibody response after three previous doses improves serum neutralization against the delta variant.', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 1024] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.8132, 0.2067], |
| # [0.8132, 1.0000, 0.1794], |
| # [0.2067, 0.1794, 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.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Information Retrieval |
|
|
| * Dataset: `10-percent-dev-split` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.4748 | |
| | cosine_accuracy@3 | 0.6582 | |
| | cosine_accuracy@5 | 0.7148 | |
| | cosine_accuracy@10 | 0.7782 | |
| | cosine_precision@1 | 0.4748 | |
| | cosine_precision@3 | 0.2194 | |
| | cosine_precision@5 | 0.143 | |
| | cosine_precision@10 | 0.0778 | |
| | cosine_recall@1 | 0.4748 | |
| | cosine_recall@3 | 0.6582 | |
| | cosine_recall@5 | 0.7148 | |
| | cosine_recall@10 | 0.7782 | |
| | **cosine_ndcg@10** | **0.6259** | |
| | cosine_mrr@10 | 0.5771 | |
| | cosine_map@100 | 0.5824 | |
| |
| <!-- |
| ## 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: 17,319 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | sentence_2 | |
| |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 25 tokens</li><li>mean: 58.26 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 311.97 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 320.42 tokens</li><li>max: 512 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | sentence_2 | |
| |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>query: I was fact-checked when I covered this topic for @user last year. Since the story back then was, apparently, that damp strips of fabric dangling over people's faces for hours on end couldn't possibly spawn anything nasty - because Science™!!</code> | <code>passage: title: Bacterial and fungal isolation from face masks under the COVID-19 pandemic |
| abstract: Abstract The COVID-19 pandemic has led people to wear face masks daily in public. Although the effectiveness of face masks against viral transmission has been extensively studied, there have been few reports on potential hygiene issues due to bacteria and fungi attached to the face masks. We aimed to (1) quantify and identify the bacteria and fungi attaching to the masks, and (2) investigate whether the mask-attached microbes could be associated with the types and usage of the masks and individual lifestyles. We surveyed 109 volunteers on their mask usage and lifestyles, and cultured bacteria and fungi from either the face-side or outer-side of their masks. The bacterial colony numbers were greater on the face-side than the outer-side; the fungal colony numbers were fewer on the face-side than the outer-side. A longer mask usage significantly increased the fungal colony numbers but not ...</code> | <code>passage: is very low. |
| |
| title: Do facemasks protect against <scp>COVID</scp>‐19? |
| Symptomatic health-care workers should not return to work until they have been tested and found to be negative for COVID-19. The public might wear masks to avoid infection or to protect others. During the 2009 pandemic of H1N1 influenza (swine flu), encouraging the public to wash their hands reduced the incidence of infection significantly whereas wearing facemasks did not.5 There is no good evidence that facemasks protect the public against infection with respiratory viruses, including COVID-19.6 However, absence of proof of an effect is not the same as proof of absence of an effect. During the pandemics caused by swine flu and by the coronaviruses which caused SARS and MERS, many people in Asia and elsewhere walked around wearing surgical or homemade cotton masks to protect themselves. One danger of doing this is the illusion of protection. Surgical facemasks are designed to be discarded after single use....</code> | |
| | <code>query: @user If just the US government had some National Institution of Health entity which could’ve been showcasing, studying and verifying this type of advantage from data years earlier .. to apply immediately without political spin</code> | <code>passage: title: Chloroquine is a potent inhibitor of SARS coronavirus infection and spread |
| abstract: Abstract Background Severe acute respiratory syndrome (SARS) is caused by a newly discovered coronavirus (SARS-CoV). No effective prophylactic or post-exposure therapy is currently available. Results We report, however, that chloroquine has strong antiviral effects on SARS-CoV infection of primate cells. These inhibitory effects are observed when the cells are treated with the drug either before or after exposure to the virus, suggesting both prophylactic and therapeutic advantage. In addition to the well-known functions of chloroquine such as elevations of endosomal pH, the drug appears to interfere with terminal glycosylation of the cellular receptor, angiotensin-converting enzyme 2. This may negatively influence the virus-receptor binding and abrogate the infection, with further ramifications by the elevation of vesicular pH, resulting in the inhibition of infection and spread of SAR...</code> | <code>passage: title: A National Medical Response to Crisis — The Legacy of World War II<br>abstract: A National Medical Response to Crisis World War II’s massive casualties were mitigated by lives saved as a result of medical care. Many of the advances made would persist long after the war conclud...</code> | |
| | <code>query: UNDENIABLE EVIDENCE OF MY SPIKE PROTEIN TRIGGERED WIDESPREAD AMYLOIDOSES THEORY. IT. IS. OCCURRING.</code> | <code>passage: title: Amyloidogenesis of SARS-CoV-2 Spike Protein |
| abstract: ABSTRACT SARS-CoV-2 infection is associated with a surprising number of morbidities. Uncanny similarities with amyloid-disease associated blood coagulation and fibrinolytic disturbances together with neurologic and cardiac problems led us to investigate the amyloidogenicity of the SARS-CoV-2 Spike protein (S-protein). Amyloid fibril assays of peptide library mixtures and theoretical predictions identified seven amyloidogenic sequences within the S-protein. All seven peptides in isolation formed aggregates during incubation at 37°C. Three 20-amino acid long synthetic Spike peptides (sequence 191-210, 599-618, 1165-1184) fulfilled three amyloid fibril criteria: nucleation dependent polymerization kinetics by ThT, Congo red positivity and ultrastructural fibrillar morphology. Full-length folded S-protein did not form amyloid fibrils, but amyloid-like fibrils with evident branching were formed during 24 hours of S-protei...</code> | <code>passage: title: Amyloidogenesis of SARS-CoV-2 Spike Protein |
| abstract: SARS-CoV-2 infection is associated with a surprising number of morbidities. Uncanny similarities with amyloid-disease associated blood coagulation and fibrinolytic disturbances together with neurologic and cardiac problems led us to investigate the amyloidogenicity of the SARS-CoV-2 spike protein (S-protein). Amyloid fibril assays of peptide library mixtures and theoretical predictions identified seven amyloidogenic sequences within the S-protein. All seven peptides in isolation formed aggregates during incubation at 37 °C. Three 20-amino acid long synthetic spike peptides (sequence 192–211, 601–620, 1166–1185) fulfilled three amyloid fibril criteria: nucleation dependent polymerization kinetics by ThT, Congo red positivity, and ultrastructural fibrillar morphology. Full-length folded S-protein did not form amyloid fibrils, but amyloid-like fibrils with evident branching were formed during 24 h of S-protein coincubat...</code> | |
| * Loss: <code>__main__.TripletMNRLCombinedLoss</code> |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 48 |
| - `per_device_eval_batch_size`: 48 |
| - `num_train_epochs`: 20 |
| - `fp16`: True |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 48 |
| - `per_device_eval_batch_size`: 48 |
| - `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`: 20 |
| - `max_steps`: -1 |
| - `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`: True |
| - `fp16_opt_level`: O1 |
| - `half_precision_backend`: auto |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: 0 |
| - `ddp_backend`: None |
| - `tpu_num_cores`: None |
| - `tpu_metrics_debug`: False |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 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} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `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 |
| - `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`: False |
| - `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`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | 10-percent-dev-split_cosine_ndcg@10 | |
| |:-------:|:----:|:-------------:|:-----------------------------------:| |
| | 1.0 | 361 | - | 0.6980 | |
| | 1.3850 | 500 | 1.6273 | - | |
| | 2.0 | 722 | - | 0.7033 | |
| | 2.7701 | 1000 | 0.9528 | - | |
| | 3.0 | 1083 | - | 0.7110 | |
| | 4.0 | 1444 | - | 0.6994 | |
| | 4.1551 | 1500 | 0.6268 | - | |
| | 5.0 | 1805 | - | 0.6933 | |
| | 5.5402 | 2000 | 0.4279 | - | |
| | 6.0 | 2166 | - | 0.6883 | |
| | 6.9252 | 2500 | 0.3117 | - | |
| | 7.0 | 2527 | - | 0.6620 | |
| | 8.0 | 2888 | - | 0.6707 | |
| | 8.3102 | 3000 | 0.2262 | - | |
| | 9.0 | 3249 | - | 0.6671 | |
| | 9.6953 | 3500 | 0.1799 | - | |
| | 10.0 | 3610 | - | 0.6579 | |
| | 11.0 | 3971 | - | 0.6470 | |
| | 11.0803 | 4000 | 0.139 | - | |
| | 12.0 | 4332 | - | 0.6469 | |
| | 12.4654 | 4500 | 0.1094 | - | |
| | 13.0 | 4693 | - | 0.6415 | |
| | 13.8504 | 5000 | 0.0911 | - | |
| | 14.0 | 5054 | - | 0.6439 | |
| | 15.0 | 5415 | - | 0.6284 | |
| | 15.2355 | 5500 | 0.0755 | - | |
| | 16.0 | 5776 | - | 0.6272 | |
| | 16.6205 | 6000 | 0.0664 | - | |
| | 17.0 | 6137 | - | 0.6290 | |
| | 18.0 | 6498 | - | 0.6253 | |
| | 18.0055 | 6500 | 0.0573 | - | |
| | 19.0 | 6859 | - | 0.6275 | |
| | 19.3906 | 7000 | 0.052 | - | |
| | 20.0 | 7220 | - | 0.6259 | |
| |
| |
| ### Training Time |
| - **Training**: 2.2 hours |
| |
| ### Framework Versions |
| - Python: 3.12.6 |
| - Sentence Transformers: 5.4.1 |
| - Transformers: 4.56.0 |
| - PyTorch: 2.8.0+cu129 |
| - Accelerate: 1.10.1 |
| - Datasets: 4.8.5 |
| - Tokenizers: 0.22.0 |
| |
| ## 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", |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| ## Model Card Contact |
| |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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