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--- |
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language: |
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- es |
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- af |
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- ar |
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- arz |
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- as |
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- bn |
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- fr |
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- sw |
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- eu |
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- ca |
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- zh |
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- en |
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- hi |
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- ur |
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- id |
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- pt |
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- vi |
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- gu |
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- kn |
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- ml |
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- mr |
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- ta |
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- te |
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- yo |
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tags: |
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- kenlm |
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- perplexity |
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- n-gram |
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- kneser-ney |
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- bigscience |
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license: "mit" |
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datasets: |
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- wikipedia |
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- oscar |
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--- |
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# KenLM models |
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This repo contains several KenLM models trained on different tokenized datasets and languages. |
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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](https://huggingface.co/bertin-project/bertin-roberta-base-spanish). 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). |
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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 |
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* `{language}.arpa.bin`: The trained KenLM model binary |
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* `{language}.sp.model`: The trained SentencePiece model used for tokenization |
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* `{language}.sp.vocab`: The vocabulary file for the SentencePiece model |
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The models have been trained using some of the preprocessing steps from [cc_net](https://github.com/facebookresearch/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. |
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# Dependencies |
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* KenLM: `pip install https://github.com/kpu/kenlm/archive/master.zip` |
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* SentencePiece: `pip install sentencepiece` |
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# Example: |
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``` |
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from model import KenlmModel |
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# Load model trained on English wikipedia |
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model = KenlmModel.from_pretrained("wikipedia", "en") |
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# Get perplexity |
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model.get_perplexity("I am very perplexed") |
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# 341.3 (low perplexity, since sentence style is formal and with no grammar mistakes) |
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model.get_perplexity("im hella trippin") |
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# 46793.5 (high perplexity, since the sentence is colloquial and contains grammar mistakes) |
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``` |
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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. |
