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