Homeworks on NLP course
Tasks description:
- Homework 1: Data Preprocessing (Normalization, Tokenization, Stop Word Removal, Lemmatization/Stemming)
- Homework 2: Vectorization Methods Comparison (CountVectorizer, TfidfVectorizer) and Model Training (DecisionTreeClassifier, LogisticRegression, Naive Bayes)
- Homework 3: Vectorization with Word2Vec and fastText, Model Training with LogisticRegression
- Homework 4: Text Classification using CNN and LSTM Models
- Homework 5: Topic Modeling with LDA and BigARTM, Visualization, and Evaluation
- Homework 6: Question Answering with HuggingFace Models, Fine-tuning with Sberquad Dataset
- Homework 7: Fine-tuning BERT/GPT Models for Text Classification and Text Generation, Evaluation with Specific Metrics
- Homework 8: Model Distillation (Teacher-Student Approach) for Classification and Comparison of Models