In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
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Question: The focus of this section is on recently published datasets and LID research applicable to the South African context. An in depth survey of algorithms, features, datasets, shared tasks and evaluation methods may be found in BIBREF0.

The datasets for the DSL 2015 & DSL 2017 shared tasks BIBREF1 are often used in LID benchmarks and also available on Kaggle . The recently published JW300 parallel corpus BIBREF2 covers over 300 languages with around 100 thousand parallel sentences per language pair on average. The WiLI-2018 benchmark dataset BIBREF4 for monolingual written natural language identification includes around 1000 paragraphs of 235 languages. The NCHLT text corpora BIBREF7 is likely a good starting point for a shared LID task dataset for the South African languages BIBREF8. 
 Question: Which datasets are employed for South African languages LID?

Answer: DSL 2015 DSL 2017 JW300 parallel corpus  NCHLT text corpora


Question: We consider QA PGNet and Multi-decoder QA PGNet with lookup table embedding as baseline models and improve on the baselines with other variations described below. 
 Question: What is the baseline?

Answer: QA PGNet Multi-decoder QA PGNet with lookup table embedding


Question: With a single text, we were already able to predict the electricity consumption with a relative error of less than 5% for both data sets. 
 Question: How accurate is model trained on text exclusively?

Answer:
Relative error is less than 5%