--- license: cc-by-nc-nd-4.0 dataset_info: - config_name: default features: - name: text dtype: string splits: - name: train num_bytes: 678202759.44 num_examples: 27000 - name: val num_bytes: 75355862.16 num_examples: 3000 - name: test num_bytes: 188389655.4 num_examples: 7500 download_size: 520788766 dataset_size: 941948277.0 - config_name: email-corpus features: - name: file dtype: string - name: message dtype: string splits: - name: train num_bytes: 1424661264 num_examples: 517401 download_size: 606473179 dataset_size: 1424661264 - config_name: indic-corpus features: - name: lang_id dtype: string - name: text dtype: string splits: - name: train num_bytes: 242818701 num_examples: 11 download_size: 105665834 dataset_size: 242818701 - config_name: wiki-topics features: - name: article dtype: string - name: category dtype: string - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 338290047 num_examples: 8000 - name: test num_bytes: 84610785 num_examples: 2000 download_size: 236620494 dataset_size: 422900832 - config_name: Assignment-3-word2vec features: - name: text dtype: string splits: - name: train num_bytes: 59964905 num_examples: 200000 download_size: 59964905 dataset_size: 59964905 - config_name: Assignment-3-word2vec-analogy features: - name: word1 dtype: string - name: word2 dtype: string - name: word3 dtype: string splits: - name: test num_bytes: 25776 num_examples: 1000 download_size: 25776 dataset_size: 25776 - config_name: Assignment-3-naive-bayes features: - name: text dtype: string - name: category dtype: string splits: - name: train num_bytes: 690247310 num_examples: 8000 - name: test num_bytes: 171875690 num_examples: 2000 download_size: 862123000 dataset_size: 862123000 - config_name: Assignment-3-em features: - name: text dtype: string - name: category dtype: string splits: - name: train num_bytes: 683095685 num_examples: 8000 - name: test num_bytes: 171875690 num_examples: 2000 download_size: 854971375 dataset_size: 854971375 - config_name: Assignment-4 features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: int32 splits: - name: train num_bytes: 38232055 num_examples: 75827 - name: val num_bytes: 5464732 num_examples: 10851 - name: test num_bytes: 10939918 num_examples: 21657 download_size: 54636705 dataset_size: 54636705 - config_name: Deep-learning-assignment features: - name: Category dtype: string - name: Description sequence: string splits: - name: train num_bytes: 15089359 num_examples: 120000 - name: test num_bytes: 1014560 num_examples: 7600 download_size: 16103919 dataset_size: 16103919 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* - config_name: email-corpus data_files: - split: train path: email-corpus/train-* - config_name: indic-corpus data_files: - split: train path: indic-corpus/train-* - config_name: wiki-topics data_files: - split: train path: wiki-topics/train-* - split: test path: wiki-topics/test-* - config_name: Assignment-3-word2vec data_files: - split: train path: Assignment-3/word2vec/train* - config_name: Assignment-3-word2vec-analogy data_files: - split: test path: Assignment-3/word2vec/test* - config_name: Assignment-3-naive-bayes data_files: - split: train path: Assignment-3/naive_bayes/train* - split: test path: Assignment-3/naive_bayes/test_nb_with_labels* - config_name: Assignment-3-em data_files: - split: train path: Assignment-3/em/train* - split: test path: Assignment-3/em/test* - config_name: Assignment-4 data_files: - split: train path: Assignment-4/train* - split: test path: Assignment-4/test* - split: val path: Assignment-4/val* - config_name: Deep-learning-assignment data_files: - split: train path: Deep-learning-assignment/train* - split: test path: Deep-learning-assignment/test* --- # CS779-Fall 2025 IIT-Kanpur Instructor: Dr. Ashutosh Modi ## Assignment-3 There are 3 main tasks in Assignment-3: 1. Neural Network Implementation from Scratch for Word2Vec using Wikipedia Text 2. Naive Bayes Classifier for Topic classification on Wikipedia Articles 3. Expectation-Maximization Based clustering on Wikipedia Articles The data can be fetched using the datasets API as follows: ```python from datasets import load_dataset # Word2Vec Dataset word2vec_train = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-3-word2vec", split="train") word2vec_test = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-3-word2vec-analogy", split="test") # Naive Bayes naive_bayes_train = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-3-naive-bayes", split="train") naive_bayes_test = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-3-naive-bayes", split="test") # Expectation-Maximization em_train = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-3-em", split="train") em_test = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-3-em", split="test") ``` ## Assignment-4 This assignment involves Named Entity Recognition (NER) on a Hindi dataset (NED) on a custom dataset. The dataset consists of sentences with tokens and their corresponding NER tags. The list of NER tags includes: - B-FESTIVAL - B-GAME - B-LANGUAGE - B-LITERATURE - B-LOCATION - B-MISC - B-NUMEX - B-ORGANIZATION - B-PERSON - B-RELIGION - B-TIMEX - I-FESTIVAL - I-GAME - I-LANGUAGE - I-LITERATURE - I-LOCATION - I-MISC - I-NUMEX - I-ORGANIZATION - I-PERSON - I-RELIGION - I-TIMEX - O The data can be fetched using the datasets API as follows: ```python from datasets import load_dataset # Train train = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-4", split="train") # Test test = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-4", split="test") # Validation val = load_dataset("Exploration-Lab/CS779-Fall25", "Assignment-4", split="val") ``` ## Deep Learning Assignment This assignment involves text classification on a dataset containing product descriptions and their corresponding categories. The dataset consists of two columns: "Category" and "Description". The "Category" column contains the category labels, while the "Description" column contains the product descriptions. The data can be fetched using the datasets API as follows: ```python from datasets import load_dataset # Train train = load_dataset("Exploration-Lab/CS779-Fall25", "Deep-learning-assignment", split="train") # Test test = load_dataset("Exploration-Lab/CS779-Fall25", "Deep-learning-assignment", split="test") ```