Update README.md

README.md

@@ -25,3 +25,72 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+Script that created it
+
+```python
+import os
+from huggingface_hub import login
+from datasets import Dataset, DatasetDict, load_dataset
+from concurrent.futures import ThreadPoolExecutor
+
+# Function to load the Hugging Face API token from a file
+def load_token(file_path: str) -> str:
+    """Load API token from a specified file path."""
+    with open(os.path.expanduser(file_path)) as f:
+        return f.read().strip()
+
+# Function to log in to Hugging Face using a token
+def login_to_huggingface(token: str) -> None:
+    """Authenticate with Hugging Face Hub."""
+    login(token=token)
+    print("Login successful")
+
+# Function to sample a specific number of examples from a dataset split
+def sample_from_split(split_name: str, num_samples: int) -> list:
+    """Sample a specified number of examples from a dataset split."""
+    c4_split = load_dataset("allenai/c4", "en", split=split_name, streaming=True)
+    samples = []
+    for i, example in enumerate(c4_split):
+        if i >= num_samples:
+            break
+        samples.append(example["text"])
+    return samples
+
+# Main function to create a smaller C4 dataset with three subsets and upload it
+def main() -> None:
+    # Step 1: Load token and log in
+    key_file_path: str = "/lfs/skampere1/0/brando9/keys/brandos_hf_token.txt"
+    token: str = load_token(key_file_path)
+    login_to_huggingface(token)
+
+    # Step 2: Define sampling parameters
+    num_samples = 10000
+
+    # Step 3: Sample subsets concurrently
+    with ThreadPoolExecutor(max_workers=3) as executor:
+        future_train = executor.submit(sample_from_split, "train", num_samples)
+        future_val = executor.submit(sample_from_split, "validation", num_samples)
+        future_test = executor.submit(sample_from_split, "validation", num_samples * 2)
+
+        train_samples = future_train.result()
+        val_samples = future_val.result()
+        test_samples = future_test.result()[num_samples:]  # Second 10k from validation for test
+
+    # Step 4: Create DatasetDict
+    small_c4_dataset = DatasetDict({
+        "train": Dataset.from_dict({"text": train_samples}),
+        "validation": Dataset.from_dict({"text": val_samples}),
+        "test": Dataset.from_dict({"text": test_samples})
+    })
+
+    # Step 5: Upload to Hugging Face Hub
+    dataset_name_c4: str = "brando/small-c4-dataset"
+    small_c4_dataset.push_to_hub(dataset_name_c4)
+    print(f"Small C4 dataset uploaded to https://huggingface.co/datasets/{dataset_name_c4}")
+
+# Run the main function
+if __name__ == "__main__":
+    main()
+
+```