Create README.md

README.md

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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 cript to make other subsets.