#@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>')

! 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)