CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutSign UpSign In
huggingface

Real-time collaboration for Jupyter Notebooks, Linux Terminals, LaTeX, VS Code, R IDE, and more,
all in one place. Commercial Alternative to JupyterHub.

GitHub Repository: huggingface/notebooks
Path: blob/main/course/videos/fast_tokenizers.ipynb
Views: 2542
Kernel: Unknown Kernel

This notebook regroups the code sample of the video below, which is a part of the Hugging Face course.

#@title from IPython.display import HTML HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/g8quOxoqhHQ?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

Install the Transformers and Datasets libraries to run this notebook.

! pip install datasets transformers[sentencepiece]
from datasets import load_dataset raw_datasets = load_dataset("glue", "mnli") raw_datasets
from transformers import AutoTokenizer fast_tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") def tokenize_with_fast(examples): return fast_tokenizer( examples["premise"], examples["hypothesis"], truncation=True )
slow_tokenizer = AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False) def tokenize_with_slow(examples): return fast_tokenizer( examples["premise"], examples["hypothesis"], truncation=True )
%time tokenized_datasets = raw_datasets.map(tokenize_with_fast)
%time tokenized_datasets = raw_datasets.map(tokenize_with_slow)
%time tokenized_datasets = raw_datasets.map(tokenize_with_fast, batched=True)
%time tokenized_datasets = raw_datasets.map(tokenize_with_slow, batched=True)