first commit, readme and download

.gitignore

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+.mypy_cache/
+data/

README.md

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 # gpt-2-output-dataset
-Dataset of GPT-2 outputs for research in detection, biases, and more
+
+This dataset contains:
+- 250K samples from the WebText test set
+- For each GPT-2 model (trained on the WebText training set), 250K plain samples (temperature 1, no truncation) and 250K samples generated with top-k 40 truncation
+
+We look forward to the research produced using this data!
+
+### Download
+
+For each, we have a training split of 250K samples, as well as validation and test splits of 5K samples.
+
+For each model, we're releasing temperature 1 samples
+
+All data is located in Google Cloud Storage, at under the directory `gs://gpt-2/output-dataset/v1`.
+
+There, you will find files:
+
+- `webtext.${split}.jsonl`
+- `small-117M.${split}.jsonl`
+- `small-117M-k40.${split}.jsonl`
+- `medium-345M.${split}.jsonl`
+- `medium-345M-k40.${split}.jsonl`
+- `large-762M.${split}.jsonl`
+- `large-762M-k40.${split}.jsonl`
+- `xl-1542M.${split}.jsonl`
+- `xl-1542M-k40.${split}.jsonl`
+
+where split is one of `train`, `test`, and `valid`.
+
+We've provided a script to download all of them, in `download_dataset.py`.
+
+### Detectability baselines
+
+We're interested in seeing research in detectability of our model generations.
+
+We've provided a baseline of logistic regression on tf-idf, in `baseline.py`.
+
+### Data removal requests
+
+If you believe your work is included in our dataset and would like us to remove it, please let us know at webtextdata@openai.com.

download_dataset.py

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+import os
+import sys
+import requests
+from tqdm import tqdm
+
+subdir = 'data'
+if not os.path.exists(subdir):
+    os.makedirs(subdir)
+subdir = subdir.replace('\\','/') # needed for Windows
+
+for ds in [
+    'webtext',
+    'small-117M',  'small-117M-k40',
+    'medium-345M', 'medium-345M-k40',
+    'large-762M',  'large-762M-k40',
+    'xl-1542M',    'xl-1542M-k40',
+]:
+    for split in ['train', 'valid', 'test']:
+        filename = ds + "." + split + '.jsonl'
+        r = requests.get("https://storage.googleapis.com/gpt-2/output-dataset/v1/" + filename, stream=True)
+
+        with open(os.path.join(subdir, filename), 'wb') as f:
+            file_size = int(r.headers["content-length"])
+            chunk_size = 1000
+            with tqdm(ncols=100, desc="Fetching " + filename, total=file_size, unit_scale=True) as pbar:
+                # 1k for chunk_size, since Ethernet packet size is around 1500 bytes
+                for chunk in r.iter_content(chunk_size=chunk_size):
+                    f.write(chunk)
+                    pbar.update(chunk_size)