R: Access to Large Language Model Predictions

# Access to Large Language Model Predictions

## Documentation for package ‘pangoling’ version 1.0.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

causal_config | Returns the configuration of a causal model
causal_next_tokens_pred_tbl | Generate next tokens after a context and their predictability using a causal transformer model
causal_pred_mats | Generate a list of predictability matrices using a causal transformer model
causal_preload | Preloads a causal language model
causal_targets_pred | Compute predictability using a causal transformer model
causal_tokens_pred_lst | Compute predictability using a causal transformer model
causal_words_pred | Compute predictability using a causal transformer model
df_jaeger14 | Self-Paced Reading Dataset on Chinese Relative Clauses
df_sent | Example dataset: Two word-by-word sentences
installed_py_pangoling | Check if the required Python dependencies for 'pangoling' are installed
install_py_pangoling | Install the Python packages needed for 'pangoling'
masked_config | Returns the configuration of a masked model
masked_preload | Preloads a masked language model
masked_targets_pred | Get the predictability of a target word (or phrase) given a left and right context
masked_tokens_pred_tbl | Get the possible tokens and their log probabilities for each mask in a sentence
ntokens | The number of tokens in a string or vector of strings
perplexity_calc | Calculates perplexity
set_cache_folder | Set cache folder for HuggingFace transformers
tokenize_lst | Tokenize an input
transformer_vocab | Returns the vocabulary of a model