- Sentiment Analysis on 'Sentiment140' Dataset:
- Using Count Vectorizer and TFIDF Vectorizer with Logistic Regression, SVC, and Random Forest.
- Sentiment Analysis on 'Sentiment140' Dataset:
- Using single-layered and double-layered LSTM.
- Machine Translation:
- Translate from English to Hindi using a custom dataset.
- Hindi Summarization:
- Summarize 'Inshorts Dataset - Hindi News'.
- Question Answering on 'COQA-Train' Dataset:
- Using T5, DistilBERT, and GPT2 Models.
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