Access to Large Language Model Predictions

Provides access to word predictability estimates using large language models (LLMs) based on 'transformer' architectures via integration with the 'Hugging Face' ecosystem < https://huggingface.co/>. The package interfaces with pre-trained neural networks and supports both causal/auto-regressive LLMs (e.g., 'GPT-2') and masked/bidirectional LLMs (e.g., 'BERT') to compute the probability of words, phrases, or tokens given their linguistic context. For details on GPT-2 and causal models, see Radford et al. (2019) < https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf>, for details on BERT and masked models, see Devlin et al. (2019) . By enabling a straightforward estimation of word predictability, the package facilitates research in psycholinguistics, computational linguistics, and natural language processing (NLP).