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---
license: bsd-3-clause
language:
- zh
- en
- id
- ja
- es
---
# TUBELEX Statistical Language Models
N-gram models on the TUBELEX YouTube subtitle corpora. We provide modified Kneser-Ney language models of order 5 ([Heafield et al., 2013](https://aclanthology.org/P13-2121)), i.e. [KenLM](https://kheafield.com/code/kenlm/) models.
The files are in LZMA-compressed ARPA format.
# What is TUBELEX?
TUBELEX is a YouTube subtitle corpus currently available for Chinese, English, Indonesian, Japanese, and Spanish.
- [preprint](https://arxiv.org/abs/2410.03240), BibTeX entry:
```
@article{nohejl_etal_2024_film,
title={Beyond {{Film Subtitles}}: {{Is YouTube}} the {{Best Approximation}} of {{Spoken Vocabulary}}?},
author={Nohejl, Adam and Hudi, Frederikus and Kardinata, Eunike Andriani and Ozaki, Shintaro and Riera Machin, Maria Angelica and Sun, Hongyu and Vasselli, Justin and Watanabe, Taro},
year={2024}, eprint={2410.03240}, archiveprefix={arXiv}, primaryclass={cs.CL},
url={https://arxiv.org/abs/2410.03240v1}, journal={ArXiv preprint}, volume={arXiv:2410.03240v1 [cs]}
}
```
- [fastText word embeddings](https://huggingface.co/naist-nlp/tubelex-fasttext)
- [word frequencies and code](https://github.com/naist-nlp/tubelex)
# Usage
To download and use the KenLM models in Python, first install dependencies:
```
pip install huggingface_hub
pip install https://github.com/kpu/kenlm/archive/master.zip
```
You can then use e.g. the English (`en`) model in the following way:
```
import kenlm
from huggingface_hub import hf_hub_download
model_file = hf_hub_download(repo_id='naist-nlp/tubelex-kenlm', filename='tubelex-en.arpa.xz')
# Loading the model requires KenLM to be compiled with LZMA support (`HAVE_XZLIB`).
# Otherwise you fill first need to decompress the model.
model = kenlm.Model(model_file)
text = ''a sequence of words' # pre-tokenized, lower-cased, without punctuation
model.perplexity(text)
```
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