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BART (Bidirectional and Auto-Regressive Transformer) is a neural network-based sequence-to-sequence language model developed by Facebook AI. It combines bidirectional and autoregressive transformers to enhance text generation and understanding tasks.
Linformer is a neural network architecture designed to reduce the computational complexity of the Transformer model by approximating self-attention with low-rank projections. It enables efficient processing of long sequences in natural language processing and other tasks.
word2vec is a group of related models used to produce word embeddings, representing words in a continuous vector space.
NeMo is an open-source toolkit developed by NVIDIA for building conversational AI applications such as speech recognition, natural language processing, and text-to-speech systems. It provides modular components and pre-trained models to facilitate research and deployment of conversational AI technologies.
BoolQ is a dataset designed for evaluating natural language understanding and question-answering systems, featuring yes/no questions derived from Wikipedia articles.
Constrained decoding is a technique in natural language processing and machine learning that restricts the output of a generative model to satisfy specific constraints. This method is used to guide the generation process to produce valid, relevant, or contextually appropriate outputs according to predefined rules or conditions.
SPLADE (sparse lexical and dense) is a neural information retrieval model designed to combine the benefits of sparse lexical representations and dense embeddings. It aims to improve search effectiveness by leveraging both term-level relevance and semantic matching.
CoQA is a dataset designed to facilitate the development of conversational question answering systems. It allows computers to engage in human-like dialogue by answering questions based on provided context.
GPT-2 is an advanced language processing AI developed by OpenAI, known for its ability to generate human-like text based on given prompts.
The SST-2 is a dataset for sentiment analysis, part of the Stanford Sentiment Treebank, used extensively in natural language processing.