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pangoling 1.0.3

• Internal changes to comply with CRAN requirements.
• HF_HOME is used now to store the models rather than TRANSFORMERS_CACHE

pangoling 1.0.2

• Internal changes: OMP THREAD LIMIT was set to 1.

pangoling 1.0.1

New Features

• Added installed_py_pangoling() to check if required Python dependencies (transformers and torch) are installed.

Other changes

• Informative startup message if python dependencies not installed.
• Documentation examples won’t run if python dependencies not installed
• Articles are now pre-computed vignettes. See

pangoling 1.0.0

• changed the ownership of the repo to ropensci
• deprecated functions are now defunct and have been replaced with their respective alternative functions

pangoling 0.0.0.9011

• Added word_n argument in causal_words_pred() to indicate word order of the texts.
• Allows for models with larger vocabulary than tokenizer.

pangoling 0.0.0.9010

New Features:

• Added checkpoint parameter to causal_preload() and masked_preload() to allow loading models from checkpoints.
• Introduced causal_next_tokens_pred_tbl(), which replaces causal_next_tokens_tbl() and provides improved predictability calculations.
• Added causal_words_pred(), causal_targets_pred(), and causal_tokens_pred_lst() to compute predictability for words, phrases, or tokens, replacing causal_lp() and causal_tokens_lp_tbl().
• Introduced masked_tokens_pred_tbl(), replacing masked_tokens_tbl(), for retrieving possible tokens and their log probabilities.
• Introduced masked_targets_pred(), replacing masked_lp(), for calculating predictability based on left and right context.
• Introduced transformer_vocab() with an optional decode parameter to return decoded tokenized words.
• New dataset df_jaeger14: Self-paced reading data on Chinese relative clauses.
• New dataset df_sent: Example dataset with two word-by-word sentences.
• New vignette: Added a worked-out example of a causal model.

Enhancements:

• Added sep argument in causal_words_pred() to support languages without spaces between words (e.g., Chinese).
• New log.p argument across multiple functions to specify how predictability is calculated (e.g., log base e, log base 2 for bits, or raw probabilities).
• Improved tokenization utilities: tokenize_lst() now supports decoded outputs via the decode parameter.
• Updated install_py_pangoling() to enhance Python environment handling.
• Added perplexity_calc() for computing perplexity from probabilities.

Deprecations:

• Deprecated causal_next_tokens_tbl(), causal_lp(), causal_tokens_lp_tbl(), and causal_lp_mats(). Use causal_next_tokens_pred_tbl(), causal_targets_pred(), causal_words_pred(), and causal_pred_mats() instead.
• Deprecated masked_tokens_tbl() and masked_lp(). Use masked_tokens_pred_tbl() and masked_targets_pred() instead.

pangoling 0.0.0.9009

• Deprecated .by in favor of by.

pangoling 0.0.0.9008

• Fix a bug when .by is unordered

pangoling 0.0.0.9007

• set_cache_folder() function added.
• Message when the package loads.
• New troubleshooting vignette.

pangoling 0.0.0.9006

• causal_lp get a l_contexts argument.
• Checkpoints work for causal models (not yet for masked models).
• Ropensci badge added.

pangoling 0.0.0.9005

• Strings with no tokens no longer throw errors.
• Requires correct version of R.

pangoling 0.0.0.9004

• Causal models accept batches.

pangoling 0.0.0.9003

• bug in causal_tokens_lp_tbl fixed

pangoling 0.0.0.9002

• minor function names to avoid conflict with other packages

pangoling 0.0.0.9001

• Tons of stuff. Fully functional package now.

pangoling 0.0.0.9000

• First release!

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.