---
language:
- es
- af
- ar
- arz
- as
- bn
- fr
- sw
- eu
- ca
- zh
- en
- hi
- ur
- id
- pt
- vi
- gu
- kn
- ml
- mr
- ta
- te
- yo
- de
tags:
- kenlm
- perplexity
- n-gram
- kneser-ney
- bigscience
license: mit
datasets:
- wikipedia
- oscar
duplicated_from: edugp/kenlm
---

# Fork of `edugp/kenlm`

* adds German wikipedia model. 

# 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](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).

* `{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](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.

# 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.