This project explores language modeling using LSTM-based architectures trained on the WikiText-2 dataset. Two models are implemented: a standard LSTM language model and an advanced AWD-LSTM variant with regularization techniques such as weight dropout and locked dropout. Given a text prompt, both models generate coherent sentence continuations.

Build, analyze, and visualize a custom Byte Pair Encoding (BPE) tokenizer trained on the WikiText-2 dataset with vocabulary analysis, compression evaluation, and an interactive GitHub Pages demo.

Proyecto de modelado de lenguaje usando una RNN Elman implementada desde cero con BPTT en WikiText-2. Incluye modelo baseline de n-gramas y evaluación con perplexity.

Custom BPE tokenizer built from scratch on WikiText-2 (30k vocab). Covers data cleaning, deduplication, HuggingFace tokenizers training, evaluation (compression ratio, UNK-free coverage, consistency), and save/reload as PreTrainedTokenizerFast.