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gorgeousbeasty's Projects

bert-deep-learning-keras-tf-tutorial icon bert-deep-learning-keras-tf-tutorial

Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.

hitabvanillabert icon hitabvanillabert

A hierarchical table dataset for question answering and data-to-text generation (ACL 2022).

indonlu icon indonlu

The first-ever vast natural language processing benchmark for Indonesian Language. We provide multiple downstream tasks, pre-trained IndoBERT models, and a starter code! (AACL-IJCNLP 2020)

predicting_economic_growth_gdp icon predicting_economic_growth_gdp

This project tests the hypothesis that signals in the form of sentiment from newspaper text can be used to materially improve the forecasts of macroeconomic variables including infaltion, GDP and unemployment rate. Our news corpus is drawn from GDELT database which contains news from all popular US newspapers. A financial sentiment model trained on financial news headlines to predict sentiments of daily news articles from 2013 to 2021.A timeseries of these sentiments is constructed which is then used in an auto-regressive model to predict the inflation and GDP at various time lags. This methodology should allow researchers in policy-making institutions to produce more accurate assessment of the macroeconomy of United States.

sustainabilityprofitcorrelation icon sustainabilityprofitcorrelation

This analysis provides the basis for using multivariate ML models to accurately predict the movement of a country's GDP growth. I posit that using a country's renewable energy consumption as an additional variable in ML models will increase the predictive power for changes in GDP. This can help businesses make informed decisions. More variable may be considered and potential market drivers found.

tensor2tensor___bert icon tensor2tensor___bert

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

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