Spaces:
Running
Running
File size: 3,872 Bytes
751936e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
# Copyright (c) 2021, EleutherAI
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Assumes a dataset of jsonl files in the same format as the neox training set.
"""
from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
from tokenizers.normalizers import NFKC
from glob import glob
import os
import json
import argparse
def load_jsonl(input_path, quiet=True) -> list:
"""
Read list of objects from a JSON lines file.
"""
data = []
with open(input_path, "r", encoding="utf-8") as f:
for line in f:
data.append(json.loads(line.rstrip("\n|\r")))
if not quiet:
print("Loaded {} records from {}".format(len(data), input_path))
return data
def json_iterator(input_dir, text_key="text"):
all_jsonls = glob(f"{input_dir}/*.jsonl") + glob(f"{input_dir}/*.json")
for j in all_jsonls:
data = load_jsonl(j)
for doc in data:
yield doc[text_key]
def train_tokenizer(
input_dir: str, save_path: str, tokenizer_type: str = "BPE", vocab_size: int = 52000
):
"""
Trains a tokenizer on all the json files in `input_dir` and saves it to `save_path`
:param input_dir: input directory containing jsonl files
:param save_path: path to save tokenizer to
:param tokenizer_type: type of tokenizer to train.
:param vocab_size: int, size of tokenizer's vocab
:return:
"""
if tokenizer_type == "BPE":
model = models.BPE()
else:
raise NotImplementedError(f"Tokenizer type {tokenizer_type} not implemented")
tokenizer = Tokenizer(model)
# Customize pre-tokenization and decoding
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)
tokenizer.normalizer = NFKC()
# And then train
trainer = trainers.BpeTrainer(
vocab_size=vocab_size, special_tokens=["<|endoftext|>", "<|padding|>"]
)
tokenizer.train_from_iterator(json_iterator(input_dir), trainer)
# And Save it
tokenizer.save(save_path, pretty=True)
print(f"Tokenizer saved at {save_path}")
def parse_args():
parser = argparse.ArgumentParser(
description="script for training a multilingual "
"HF tokenizer on CC dumps with upweighting for low resource languages"
)
parser.add_argument(
"--json_input_dir",
type=str,
help="Path to folder containing tokenizer training data in jsonl format",
)
parser.add_argument(
"--tokenizer_output_path",
type=str,
help="Path to which your trained tokenizer will be saved (should end in .json)",
)
parser.add_argument(
"--tokenizer_type",
type=str,
help="type of tokenizer to train, currently only BPE is supported",
choices=["BPE"],
default=["BPE"],
)
parser.add_argument(
"-v",
"--vocab_size",
help="vocabulary size of tokenizer, default=52k",
type=int,
default=52000,
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
train_tokenizer(
args.json_input_dir,
save_path=args.tokenizer_output_path,
tokenizer_type=args.tokenizer_type,
vocab_size=args.vocab_size,
)
|