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installed_py_pangoling() to check if required
Python dependencies (transformers and torch)
are installed.word_n argument in
causal_words_pred() to indicate word order of the
texts.checkpoint parameter to
causal_preload() and masked_preload() to allow
loading models from checkpoints.causal_next_tokens_pred_tbl(), which
replaces causal_next_tokens_tbl() and provides improved
predictability calculations.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().masked_tokens_pred_tbl(), replacing
masked_tokens_tbl(), for retrieving possible tokens and
their log probabilities.masked_targets_pred(), replacing
masked_lp(), for calculating predictability based on left
and right context.transformer_vocab() with an optional
decode parameter to return decoded tokenized words.df_jaeger14: Self-paced
reading data on Chinese relative clauses.df_sent: Example dataset
with two word-by-word sentences.sep argument in causal_words_pred()
to support languages without spaces between words (e.g., Chinese).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).tokenize_lst() now
supports decoded outputs via the decode parameter.install_py_pangoling() to enhance Python
environment handling.perplexity_calc() for computing perplexity from
probabilities.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.masked_tokens_tbl() and
masked_lp(). Use masked_tokens_pred_tbl() and
masked_targets_pred() instead..by in favor of by..by is unorderedset_cache_folder() function added.causal_lp get a l_contexts argument.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.
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