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Ability to train from memory #544

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merged 8 commits into from
Nov 28, 2020
Merged

Ability to train from memory #544

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ecc22a2
Add ability to train from Iterator
n1t0 Nov 12, 2020
5e96085
Also accept iterators of batches in train_from_iterator
n1t0 Nov 25, 2020
b08bdb9
Improve progress tracking while training
n1t0 Nov 25, 2020
723ee7c
Add example for training with datasets
n1t0 Nov 25, 2020
0e6c822
Make sure we first try to extract a string
n1t0 Nov 25, 2020
99140aa
Use train_from_files in benchmarks
n1t0 Nov 25, 2020
a20fbf7
Improve docs and fix tests around training
n1t0 Nov 27, 2020
f5ec740
Node - Update bindings with train_from_files
n1t0 Nov 27, 2020
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Add example for training with datasets
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@n1t0
n1t0 committed Nov 28, 2020
commit 723ee7c9ee3b1619463d411784e358166098c691
19 changes: 19 additions & 0 deletions bindings/python/examples/train_with_datasets.py
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import datasets
from tokenizers import normalizers, pre_tokenizers, Tokenizer, models, trainers

# Build a tokenizer
bpe_tokenizer = Tokenizer(models.BPE())
bpe_tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
bpe_tokenizer.normalizer = normalizers.Lowercase()

# Initialize a dataset
dataset = datasets.load_dataset("wikitext", "wikitext-103-raw-v1")

# Build an iterator over this dataset
def batch_iterator():
batch_length = 1000
for i in range(0, len(dataset["train"]), batch_length):
yield dataset["train"][i : i + batch_length]["text"]

# And finally train
bpe_tokenizer.train_from_iterator(batch_iterator(), length=len(dataset["train"]))