# KenLM models

This repo contains several KenLM models trained on different tokenized datasets and languages.
KenLM models are probabilistic n-gram languge models that models. One use case of these models consist on fast perplexity estimation for filtering or sampling large datasets. For example, one could use a KenLM model trained on French Wikipedia to run inference on a large dataset and filter out samples that are very unlike to appear on Wikipedia (high perplexity), or very simple non-informative sentences that could appear repeatedly (low perplexity).

At the root of this repo you will find different directories named after the dataset models were trained on (e.g. wikipedia, oscar). Within each directory, you will find several models trained on different language subsets of the dataset (e.g. en (English), es (Spanish), fr (French)). For each language you will find three different files

• {language}.arpa.bin: The trained KenLM model binary
• {language}.sp.model: The trained SentencePiece model used for tokenization
• {language}.sp.vocab: The vocabulary file for the SentencePiece model

The models have been trained using some of the preprocessing steps from cc_net, in particular replacing numbers with zeros and normalizing punctuation. So, it is important to keep the default values for the parameters: lower_case, remove_accents, normalize_numbers and punctuation when using the pre-trained models in order to replicate the same pre-processing steps at inference time.

# Dependencies

• KenLM: pip install https://github.com/kpu/kenlm/archive/master.zip
• SentencePiece: pip install sentencepiece

# Example:

from model import KenlmModel

# Load model trained on English wikipedia
model = KenlmModel.from_pretrained("wikipedia", "en")

# Get perplexity
model.get_perplexity("I am very perplexed")
# 341.3 (low perplexity, since sentence style is formal and with no grammar mistakes)

model.get_perplexity("im hella trippin")
# 46793.5 (high perplexity, since the sentence is colloquial and contains grammar mistakes)


In the example above we see that, since Wikipedia is a collection of encyclopedic articles, a KenLM model trained on it will naturally give lower perplexity scores to sentences with formal language and no grammar mistakes than colloquial sentences with grammar mistakes.