Update README.md

README.md

@@ -30,3 +30,111 @@ configs:
   - split: ja
     path: data/ja-*
 ---
+
+
+```python
+
+from datasets import load_dataset, Dataset
+from itertools import islice
+from operator import itemgetter
+import re
+
+def extract_dump_date(dump_str):
+    """Extract date from dump string like 'CC-MAIN-2024-10'"""
+    if not dump_str:
+        return '0'  # For items without dump field
+    match = re.search(r'(\d{4}-\d{2})', dump_str)
+    return match.group(1) if match else '0'
+
+def load_and_verify_datasets():
+    # Load streaming datasets
+    datasets = {
+        "ko": load_dataset("HuggingFaceFW/fineweb-2", "kor_Hang", split="test", streaming=True),
+        "zh": load_dataset("HuggingFaceFW/fineweb-2", "cmn_Hani", split="test", streaming=True),
+        "en": load_dataset("HuggingFaceFW/fineweb-edu", "CC-MAIN-2024-10", split="train", streaming=True),
+        "ja": load_dataset("HuggingFaceFW/fineweb-2", "jpn_Jpan", split="test", streaming=True),
+    }
+    
+    processed_datasets = {}
+    
+    for lang, ds in datasets.items():
+        print(f"\nProcessing {lang} dataset...")
+        
+        # Collect items with their dumps
+        items_with_dumps = []
+        for item in islice(ds, 100000):  # Collect first 100K items
+            dump = item.get('dump', '')
+            items_with_dumps.append((item, dump))
+            
+        # Sort by dump date in descending order (most recent first)
+        items_with_dumps.sort(key=lambda x: extract_dump_date(x[1]), reverse=True)
+        
+        # Print dump distribution of sorted data
+        print("\nDump distribution (most recent first):")
+        dump_counts = {}
+        for _, dump in items_with_dumps[:1000]:  # Check first 1000 items
+            dump_counts[dump] = dump_counts.get(dump, 0) + 1
+        for dump, count in sorted(dump_counts.items(), key=lambda x: extract_dump_date(x[0]), reverse=True):
+            print(f"  {dump}: {count} items")
+        
+        texts_set = set()
+        filtered_texts = []
+        
+        # Process sorted items
+        for item, dump in items_with_dumps:
+            text = item.get('text', item.get('content', '')).strip()
+            
+            # Basic quality filters
+            if text and len(text) > 50:  # Only keep non-empty texts with reasonable length
+                texts_set.add(text)
+                if len(texts_set) >= 15000:
+                    break
+        
+        # Convert set to list and create dataset
+        filtered_texts = list(texts_set)
+        processed_datasets[lang] = Dataset.from_dict({"text": filtered_texts})
+        print(f"\n{lang} dataset final size: {len(processed_datasets[lang])} examples")
+        
+        # Print sample texts with their dumps
+        print("\nSample texts from most recent dump:")
+        samples_shown = 0
+        for item, dump in items_with_dumps:
+            if samples_shown >= 2:
+                break
+            text = item.get('text', item.get('content', '')).strip()
+            if text in texts_set:
+                print(f"Dump: {dump}")
+                print(f"Length: {len(text)}")
+                print(f"Text preview: {text[:100]}...")
+                print("---")
+                samples_shown += 1
+    
+    return processed_datasets
+
+def main():
+    try:
+        datasets = load_and_verify_datasets()
+        print("\nDatasets processed with the following sizes:")
+        for lang, ds in datasets.items():
+            print(f"{lang}: {len(ds)} examples")
+
+        # Create DatasetDict
+        print("\nCreating DatasetDict...")
+        dataset_dict = create_dataset_dict(datasets)
+
+        # Upload to Hugging Face
+        # Replace with your values
+        REPO_NAME = "yourname/reponame"
+        HF_TOKEN = "your_api_key"  # Don't share this token!
+
+        print("\nUploading to Hugging Face Hub...")
+        upload_to_huggingface(dataset_dict, REPO_NAME, HF_TOKEN)
+        print(f"\nDataset uploaded successfully to {REPO_NAME}")
+
+
+    except Exception as e:
+        print(f"Error processing datasets: {str(e)}")
+
+if __name__ == "__main__":
+    main()
+```