language: en
datasets:
- Jean-Baptiste/wikiner_fr
widget:
- example_title: Sample Article 1
- example: >-
('A case of a patient with type 1 neurofibromatosis associated with
popliteal and coronary artery aneurysms is described in which
cross-sectional', 'imaging provided diagnostic information.', 'The aim of
this study was to compare the exercise intensity and competition load
during Time Trial (TT), Flat (FL), Medium Mountain (MM) and High ',
'Mountain (HM) stages based heart rate (HR) and session rating of
perceived exertion (RPE).METHODS: We monitored both HR and RPE of 12
professional ', 'cyclists during two consecutive 21-day cycling races in
order to analyze the exercise intensity and competition load (TRIMPHR and
TRIMPRPE).', 'RESULTS:The highest (P<0.05) mean HR was found in TT (169±2
bpm) versus those observed in FL (135±1 bpm), MM (139±3 bpm), HM (143±1
bpm)')
- example_title: Sample Article 2
- example: >-
('The association of body mass index (BMI) with blood pressure may be
stronger in Asian than non-Asian populations, however, longitudinal
studies ', 'with direct comparisons between ethnicities are lacking. We
compared the relationship of BMI with incident hypertension over
approximately 9.5 years', ' of follow-up in young (24-39 years) and
middle-aged (45-64 years) Chinese Asians (n=5354), American Blacks
(n=6076) and American Whites (n=13451).', 'We estimated risk differences
using logistic regression models and calculated adjusted incidences and
incidence differences. ', 'To facilitate comparisons across ethnicities,
standardized estimates were calculated using mean covariate values for
age, sex, smoking, education', 'and field center, and included the
quadratic terms for BMI and age. Weighted least-squares regression models
with were constructed to summarize', 'ethnic-specific incidence
differences across BMI. Wald statistics and p-values were calculated based
on chi-square distributions. The association of', 'BMI with the incidence
difference for hypertension was steeper in Chinese (p<0.05) than in
American populations during young and middle-adulthood.', 'For example, at
a BMI of 25 vs 21 kg/m2 the adjusted incidence differences per 1000
persons (95% CI) in young adults with a BMI of 25 vs those with', 'a BMI
of 21 was 83 (36- 130) for Chinese, 50 (26-74) for Blacks and 30 (12-48)
for Whites')
Multi-Label-Classification-of-Pubmed-Articles
The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BertForSequenceClassification model to fine tune on Pubmed MultiLabel classification Datset.
Also tried RobertaForSequenceClassification and XLNetForSequenceClassification models for Fine-Tuning the Model on Pubmed MultiLabel Datset.
I have integrated Weight and Bias for visualizations and logging artifacts and comparisons of different models!
[Multi Label Classification of PubMed Articles (Paper Night Presentation)] https://wandb.ai/owaiskhan9515/Multi%20Label%20Classification%20of%20PubMed%20Articles%20(Paper%20Night%20Presentation)
- To get the API key, create an account in the website .
- Use secrets to use API Keys more securely inside Kaggle.
For more information on the attributes visit the Kaggle Dataset Description here.