Create README.md

README.md

@@ -0,0 +1,70 @@
+---
+task_categories:
+- text-generation
+---
+
+# Comma v0.1 Training Dataset (1 Billion Token Sample)
+
+This is a 1 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 subset was created using a single item batch version of the following script which I no longer have:
+
+```python
+import os
+import json
+import random
+import requests
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from argparse import ArgumentParser
+
+parser = ArgumentParser()
+parser.add_argument("--tokens", default=10**9, type=int,
+                    help="The number of tokens to subset.")
+args = parser.parse_args()
+
+tokenizer = AutoTokenizer.from_pretrained("common-pile/comma-v0.1-1t")
+dataset = load_dataset("common-pile/comma_v0.1_training_dataset")
+
+used = set()
+token_count = 0
+split = {"train":[]}
+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"]
+        split = data["split"]
+
+milestone = 10 ** 6
+while token_count < args.tokens:
+    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]
+    token_count += sum([len(i) for i in tokenizer(texts)["input_ids"]])
+    split["train"].extend(items)
+    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, "split":split}, outfile)
+        milestone += 10 ** 6
+        print(token_count, f"{(token_count / args.tokens) * 100}%")
+
+with open(f"subset_{args.tokens}.json", "w") as outfile:
+    json.dump(split, outfile)
+```
+
+Feel free to modify and use this script to create subsets of other datasets.