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---
task_categories:
- text-generation
---
# Comma v0.1 Training Dataset (10 Billion Token Sample)
This is a 10 billion token subset of the [Comma v0.1 Training Set](https://huggingface.co/datasets/common-pile/comma_v0.1_training_dataset) intended
as a convenience for small deep learning experiments. It is similar in spirit to the [1 billion token RedPajama sample](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample)
which is no longer functioning with HuggingFace transformers due to involving the execution of arbitrary code at load time.
## Method
The data was subsetted using the following script:
```python
import os
import json
import gzip
import math
import random
import requests
from pathlib import Path
from tqdm import tqdm
from datasets import load_dataset
from transformers import AutoTokenizer
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--output-dir", type=Path, default="shards")
parser.add_argument("--tokens", default=10**9, type=int,
help="The number of tokens to subset.")
parser.add_argument("--shard-size", type=int, default=(250 * (10 ** 6)))
args = parser.parse_args()
tokenizer = AutoTokenizer.from_pretrained("common-pile/comma-v0.1-1t")
dataset = load_dataset("common-pile/comma_v0.1_training_dataset")
if not os.path.exists("shards"):
os.mkdir("shards")
used = set()
token_count = 0
shard_index = 0
if os.path.exists("subset_resume.json"):
with open("subset_resume.json") as infile:
data = json.load(infile)
spans = set(data["used"])
token_count = data["token_count"]
shard_index = data["shard_index"]
num_shards = math.ceil(args.tokens / args.shard_size)
milestone = args.shard_size
progress = tqdm(total=args.tokens)
while token_count < args.tokens:
progress.set_description(f"Tokens Processed (Shard {shard_index})")
filename = f"train-{shard_index:05d}-of-{num_shards:05d}.jsonl.gz"
filepath = Path(args.output_dir) / filename
with gzip.open(filepath, 'wt', encoding='utf-8') as outfile:
while token_count < milestone:
choices = set()
for i in range(64):
choice = random.randrange(dataset["train"].num_rows)
while choice in used:
choice = random.randrange(dataset["train"].num_rows)
used.add(choice)
choices.add(choice)
assert len(choices) == 64
items = []
for choice in choices:
items.append(dataset["train"][choice])
texts = [item["text"] for item in items]
new_tokens = sum([len(i) for i in tokenizer(texts)["input_ids"]])
token_count += new_tokens
progress.update(new_tokens)
for item in items:
json_line = json.dumps(item)
outfile.write(json_line + "\n")
if token_count > milestone:
with open("subset_resume.json", "w") as outfile:
serial_used = list(used)
json.dump({"used":serial_used, "token_count":token_count, "shard_index":shard_index}, outfile)
milestone += args.shard_size
shard_index += 1
```
Feel free to adapt and use this script to make other subsets. |