Multi Language Code Summarization Via Bimodal Encoder Decoder

via “encoder-decoder models for sequence-to-sequence tasks with beam search”

Hugging Face's model library — thousands of pretrained transformers for NLP, vision, audio.

Unique: Provides encoder-decoder models with unified API for multiple tasks (translation, summarization, QA), supporting beam search and other decoding strategies. Cross-attention between encoder and decoder enables context-aware generation.

vs others: More flexible than task-specific models because the same architecture works for multiple tasks. More efficient than decoder-only models for tasks with long inputs because encoder processes input once.