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Must be >0.0, will normally be >1.0.Factor by which p(w|h) for higher-than-bigram history state h (with the backoff term excluded) has to be greater than p(w|backoff-state) for us to include it in the model (in addition to the --unigram-factor constraint). Must be >0.0 and < unigram-factorFactor by which p(w|h) for h == the BOS history state (with the backoff term excluded) has to be higher than p(w|unigram-state) for us to include it in the model. Must be >0.0 and <= unigram-factor.Integer id for the BOS word ()Integer id for the EOS word ()Integer id for the Break word (). Not needed but included for ease of scriptingImportant configuration value. After all other stages of estimating the model, the unigram probabilities are taken to this power and then rescaled to sum to 1.0. Note: it's traditional in importance sampling to use this kind of power term. There are both theoretical and practical reasons why we want to just apply this power to the unigram portion. E.g. 0.75.vocab-sizengram-orderdiscounting-constantunigram-factorbackoff-factorbos-factorbos-symboleos-symbolbrk-symbolunigram-powerEstimate highly-pruned backoff LM for use in importance sampling for RNNLM training. Reads integerized text. Usage: rnnlm-get-sampling-lm [options] \ (this form writes a non-human-readable format that can be read by rnnlm-get-egs). e.g.: ... | rnnlm-get-sampling-lm --vocab-size=10002 - sampling.lm The word symbol table is used to write the ARPA file, but is expected to already have been used to convert the words into integer form. Each line of integerized input text should have a corpus weight as the first field, e.g.: 1.0 782 1271 3841 82 and lines of input text should not be repeated (just increase the weight). 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