Upload create_dataset.py with huggingface_hub

create_dataset.py

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+import json
+import argparse
+import random
+from pathlib import Path
+from tqdm import tqdm
+import datasets
+from huggingface_hub import HfApi, RepoCard
+from transformers import HfArgumentParser
+
+random.seed(0)
+
+def generate_unique_multiplication_data(a_max, b_max, n_train, n_test):
+    """Generate train and test datasets for each multiplication range ensuring no overlap."""
+    datasets = {}
+    
+    for a in range(1, a_max + 1):
+        for b in range(1, b_max + 1):
+            all_pairs = [(x, y) for x in range(1, a + 1) for y in range(1, b + 1)]
+            
+            # Convert to list before sampling to avoid Python 3.9+ warning
+            test_data = set(random.sample(list(all_pairs), min(n_test, len(all_pairs))))  
+            train_data = set(random.sample(list(set(all_pairs) - test_data), min(n_train, len(set(all_pairs) - test_data))))
+
+            datasets[f"{a}x{b}"] = {"train": list(train_data), "test": list(test_data)}
+
+    return datasets
+
+def save_to_jsonl(data, file_path):
+    """Save dataset to JSONL format."""
+    with open(file_path, "w") as f:
+        for a, b in data:
+            json.dump({"problem": f"What is {a} times {b}?", "answer": str(a * b)}, f)
+            f.write("\n")
+
+def prepare_datasets(output_dir):
+    """Prepare train and test datasets ensuring no overlap for all 1x1 to 15x15 combinations."""
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    all_datasets = generate_unique_multiplication_data(a_max=15, b_max=15, n_train=1000, n_test=100)
+
+    train_files, test_files = [], []
+    for name, data in all_datasets.items():
+        train_file = output_dir / f"multiplication_train_{name}.jsonl"
+        test_file = output_dir / f"multiplication_test_{name}.jsonl"
+
+        save_to_jsonl(data["train"], train_file)
+        save_to_jsonl(data["test"], test_file)
+
+        train_files.append(train_file)
+        test_files.append(test_file)
+
+    print(f"\n✅ Datasets saved to {output_dir}")
+    return train_files, test_files
+
+def process_file(file_path):
+    """Convert JSONL data into Hugging Face dataset format."""
+    with open(file_path, "r") as f:
+        data = [json.loads(line.strip()) for line in f if line.strip()]
+
+    dataset = {
+        "messages": [[
+            {"role": "user", "content": item["problem"]},
+            {"role": "assistant", "content": item["answer"]},
+        ] for item in data],
+        "ground_truth": [item["answer"] for item in data],
+        "dataset": ["multiplication"] * len(data),
+    }
+    return datasets.Dataset.from_dict(dataset)
+
+def push_to_huggingface(train_files, test_files, hf_entity):
+    """Push datasets to Hugging Face Hub and print the dataset link."""
+    api = HfApi()
+    hf_entity = hf_entity or api.whoami()["name"]
+
+    print("\n📤 Uploading datasets to Hugging Face...\n")
+
+    for file in train_files + test_files:
+        dataset = process_file(file)
+        dataset_name = file.stem
+        repo_id = f"{hf_entity}/{dataset_name}"
+        hf_url = f"https://huggingface.co/datasets/{repo_id}"
+
+        print(f"✅ Dataset uploaded: {dataset_name}")
+        # print(f"🔗 Click to view: {hf_url}\n")  # 👈 PRINTS THE LINK
+
+        dataset.push_to_hub(repo_id)
+
+        api.upload_file(
+            path_or_fileobj=__file__,
+            path_in_repo="create_dataset.py",
+            repo_type="dataset",
+            repo_id=repo_id,
+        )
+
+        # Add RepoCard with Hugging Face link
+        repo_card = RepoCard(
+            content=f"""\
+# Multiplication Dataset - {dataset_name}
+
+This dataset contains multiplication problems for numbers up to 15x15.
+
+## Dataset Format
+
+- `messages`: User question and assistant answer.
+- `ground_truth`: Correct multiplication result.
+- `dataset`: "multiplication"
+
+## Hugging Face Dataset Link
+➡️ [View dataset on Hugging Face]({hf_url})
+"""
+        )
+        repo_card.push_to_hub(repo_id, repo_type="dataset")
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output_dir", type=str, default="math_data", help="Output directory")
+    parser.add_argument("--push_to_hub", action="store_true", help="Upload to Hugging Face")
+    parser.add_argument("--hf_entity", type=str, default=None, help="Hugging Face entity")
+    args = parser.parse_args()
+
+    train_files, test_files = prepare_datasets(args.output_dir)
+
+    if args.push_to_hub:
+        push_to_huggingface(train_files, test_files, args.hf_entity)
+
+if __name__ == "__main__":
+    main()