From dc5e2862edd038af3dd1c03d71d83ea10af2127c Mon Sep 17 00:00:00 2001
From: shabie <30535146+shabie@users.noreply.github.com>
Date: Thu, 29 Jul 2021 12:03:12 +0200
Subject: [PATCH] Typo fix `tokenize_exemple`

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 79be8ba224f..de32ca543f4 100644
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 🤗 Datasets is a lightweight library providing **two** main features:
 
 - **one-line dataloaders for many public datasets**: one liners to download and pre-process any of the ![number of datasets](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/datasets&color=brightgreen) major public datasets (in 467 languages and dialects!) provided on the [HuggingFace Datasets Hub](https://huggingface.co/datasets). With a simple command like `squad_dataset = load_dataset("squad")`, get any of these datasets ready to use in a dataloader for training/evaluating a ML model (Numpy/Pandas/PyTorch/TensorFlow/JAX),
-- **efficient data pre-processing**: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. With simple commands like `tokenized_dataset = dataset.map(tokenize_exemple)`, efficiently prepare the dataset for inspection and ML model evaluation and training.
+- **efficient data pre-processing**: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. With simple commands like `tokenized_dataset = dataset.map(tokenize_example)`, efficiently prepare the dataset for inspection and ML model evaluation and training.
 
 [🎓 **Documentation**](https://huggingface.co/docs/datasets/) [🕹 **Colab tutorial**](https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb)