Reading comprehension based question-answering model for news articles.
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Updated
Jun 22, 2022 - Jupyter Notebook
Reading comprehension based question-answering model for news articles.
A project about fine-tuning bert-base-uncased model for reading comprehension tasks.
Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)
Sentiment Classifier using: Softmax-Regression, Feed-Forward Neural Network, Bidirectional stacked LSTM/GRU Recursive Neural Network, fine-tuning on BERT pre-trained model. Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)
Fork of THUNLP-MT/Mask-Align to translate NewsQA to Spanish and create NewsQA-es
Code to rebuild the NewsQA-es dataset: a Spanish version of the NewsQA dataset
Experiments related to reading comprehension datasets: SQuAD and NewsQA
MTP-FlanT5-SBERT-Model-for-NewsQA-and-Teacher-Student-Model
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