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tokenizer-wiki-bench

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---
license: apache-2.0
pretty_name: Multilingual Tokenizer Wikipedia Benchmark
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path: ja/train-*
- split: clean
path: ja/clean-*
- config_name: ko
data_files:
- split: train
path: ko/train-*
- split: clean
path: ko/clean-*
- config_name: lt
data_files:
- split: train
path: lt/train-*
- split: clean
path: lt/clean-*
- config_name: lv
data_files:
- split: train
path: lv/train-*
- split: clean
path: lv/clean-*
- config_name: mr
data_files:
- split: train
path: mr/train-*
- split: clean
path: mr/clean-*
- config_name: nl
data_files:
- split: train
path: nl/train-*
- split: clean
path: nl/clean-*
- config_name: 'no'
data_files:
- split: train
path: no/train-*
- split: clean
path: no/clean-*
- config_name: pl
data_files:
- split: train
path: pl/train-*
- split: clean
path: pl/clean-*
- config_name: pt
data_files:
- split: train
path: pt/train-*
- split: clean
path: pt/clean-*
- config_name: ro
data_files:
- split: train
path: ro/train-*
- split: clean
path: ro/clean-*
- config_name: ru
data_files:
- split: train
path: ru/train-*
- split: clean
path: ru/clean-*
- config_name: sa
data_files:
- split: train
path: sa/train-*
- split: clean
path: sa/clean-*
- config_name: sk
data_files:
- split: train
path: sk/train-*
- split: clean
path: sk/clean-*
- config_name: sl
data_files:
- split: train
path: sl/train-*
- split: clean
path: sl/clean-*
- config_name: sr
data_files:
- split: train
path: sr/train-*
- split: clean
path: sr/clean-*
- config_name: sv
data_files:
- split: train
path: sv/train-*
- split: clean
path: sv/clean-*
- config_name: ta
data_files:
- split: train
path: ta/train-*
- split: clean
path: ta/clean-*
- config_name: te
data_files:
- split: train
path: te/train-*
- split: clean
path: te/clean-*
- config_name: tr
data_files:
- split: train
path: tr/train-*
- split: clean
path: tr/clean-*
- config_name: uk
data_files:
- split: train
path: uk/train-*
- split: clean
path: uk/clean-*
- config_name: ur
data_files:
- split: train
path: ur/train-*
- split: clean
path: ur/clean-*
- config_name: vi
data_files:
- split: train
path: vi/train-*
language:
- af
- ar
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- ga
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ko
- lt
- lv
- mr
- nl
- no
- pl
- pt
- ro
- ru
- sa
- sk
- sl
- sr
- sv
- ta
- te
- tr
- uk
- ur
- vi
---
# Multilingual Tokenizer Benchmark
This dataset includes pre-processed wikipedia data for tokenizer evaluation in 45 languages.
## Usage
The dataset allows us to easily calculate tokenizer fertility and the proportion of continued words on any of the supported languages. In the example below we take the Mistral tokenizer and evaluate its performance on Slovak.
```python
from transformers import AutoTokenizer
from datasets import load_dataset
import numpy as np
def calculate_metrics(tokens):
tmp = np.array([len(y) for y in tokens])
return {'fertility': np.mean(tmp), 'cont_prop': np.count_nonzero(tmp > 1) / tmp.shape[0]}
tokenizer_name = 'mistralai/Mistral-7B-v0.1'
language = 'sk' #Slovak
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
ds = load_dataset('occiglot/tokenizer-wiki-bench', name=language, split='clean')
remove_columns = list(set(ds.column_names) - set(["text"]))
ds = ds.map(lambda x: {'tokens': tokenizer(x['split_text'], add_special_tokens=False)['input_ids']} ,num_proc=256, remove_columns=remove_columns, batched=False)
remove_columns = None#list(set(ds.column_names))
ds = ds.map(lambda x: calculate_metrics(x['tokens']), num_proc=256, remove_columns=remove_columns, batched=False)
df = ds.to_pandas()
print('Fertility: ', df.fertility.mean())
print('Prop. continued words:', df.cont_prop.mean())
```
## Dataset Creation
We loosely follow the approach of [Rust _et al.](https://arxiv.org/abs/2012.15613) using the fast [UDPipe](https://ufal.mff.cuni.cz/udpipe) to pre-split documents into words and subsequently run the tokenizer over isolated words. For all languages we use the respective November 2023 snapshot from [Wikipedia](wikimedia/wikipedia). Since Wikipedia, by nature, contains significantly more numbers and dates than other text and most tokenizers split those into single digits, we filtered all lone-standing numbers from the documents. Additionally, we removed any documents that still contained non-parsed HTML code (less than 1%).