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@@ -57,3 +57,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+normalization/merged_transliteration_sampled.jsonl filter=lfs diff=lfs merge=lfs -text
+normalization/punctuation_test_merged.jsonl filter=lfs diff=lfs merge=lfs -text
+normalization/sampled_100k_norm.jsonl filter=lfs diff=lfs merge=lfs -text
+normalization/sampled_100k_translit.jsonl filter=lfs diff=lfs merge=lfs -text
+normalization/test_split_translit.jsonl filter=lfs diff=lfs merge=lfs -text
+normalization/train_set_punct_2.02M_ready.jsonl filter=lfs diff=lfs merge=lfs -text
+normalization/train_set_punct_final_no_leak.jsonl filter=lfs diff=lfs merge=lfs -text
+normalization/transliteration_sampled_850k.jsonl filter=lfs diff=lfs merge=lfs -text
+normalization/transliteration_sampled_850k_no_leak.jsonl filter=lfs diff=lfs merge=lfs -text

normalization/merged_transliteration_sampled.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9366505499dee8ed19ff27998ef3fb5ddd502ffa3d60d28bf9fbd054d12be81f
+size 7410537492

normalization/punctuation_test_merged.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11b392c4ebad7a6f47ef2fc88aac92143ecfeec94276d0f44e78f0f44d7ef5cc
+size 214068415

normalization/sample_test_split.py

@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+"""
+Sample 1000 samples per language from merged_transliteration_sampled.jsonl
+with constraints:
+- Each sample must have < 100 words (counting both input_text and output_text)
+- Samples must not exist in sampled_100k_translit.jsonl (train split)
+"""
+
+import json
+import random
+from collections import defaultdict
+from typing import Set, Dict, List, Tuple
+
+def count_words(text: str) -> int:
+    """Count words in a text string."""
+    if not text:
+        return 0
+    return len(text.split())
+
+def load_train_split(train_file: str) -> Set[Tuple[str, str]]:
+    """
+    Load train split and create a set of (input_text, output_text) tuples
+    for fast lookup to avoid duplicates.
+    """
+    train_samples = set()
+    print(f"Loading train split from {train_file}...")
+    with open(train_file, 'r', encoding='utf-8') as f:
+        for line_num, line in enumerate(f, 1):
+            if line_num % 10000 == 0:
+                print(f"  Processed {line_num} lines...")
+            try:
+                data = json.loads(line.strip())
+                input_text = data.get('input_text', '').strip()
+                output_text = data.get('output_text', '').strip()
+                # Create a tuple for deduplication
+                train_samples.add((input_text, output_text))
+            except json.JSONDecodeError:
+                continue
+    print(f"Loaded {len(train_samples)} samples from train split")
+    return train_samples
+
+def sample_test_split(
+    source_file: str,
+    train_samples: Set[Tuple[str, str]],
+    samples_per_language: int = 1000,
+    max_words: int = 100
+) -> Dict[str, List[Dict]]:
+    """
+    Sample test split from source file.
+    
+    Returns:
+        Dictionary mapping language to list of sampled samples
+    """
+    # Group samples by language
+    samples_by_language = defaultdict(list)
+    
+    print(f"\nReading source file: {source_file}")
+    with open(source_file, 'r', encoding='utf-8') as f:
+        for line_num, line in enumerate(f, 1):
+            if line_num % 100000 == 0:
+                print(f"  Processed {line_num} lines...")
+            try:
+                data = json.loads(line.strip())
+                input_text = data.get('input_text', '').strip()
+                output_text = data.get('output_text', '').strip()
+                language = data.get('language', '').strip()
+                
+                if not language or not input_text or not output_text:
+                    continue
+                
+                # Check word count
+                total_words = count_words(input_text) + count_words(output_text)
+                if total_words >= max_words:
+                    continue
+                
+                # Check if sample exists in train split
+                sample_tuple = (input_text, output_text)
+                if sample_tuple in train_samples:
+                    continue
+                
+                # Add to language group
+                samples_by_language[language].append(data)
+            except json.JSONDecodeError:
+                continue
+    
+    print(f"\nFound samples by language:")
+    for lang, samples in samples_by_language.items():
+        print(f"  {lang}: {len(samples)} samples")
+    
+    # Sample from each language
+    sampled_data = {}
+    print(f"\nSampling {samples_per_language} samples per language...")
+    for language, samples in samples_by_language.items():
+        if len(samples) < samples_per_language:
+            print(f"  WARNING: {language} has only {len(samples)} samples, "
+                  f"requested {samples_per_language}. Using all available.")
+            sampled_data[language] = samples
+        else:
+            sampled_data[language] = random.sample(samples, samples_per_language)
+        print(f"  {language}: sampled {len(sampled_data[language])} samples")
+    
+    return sampled_data
+
+def write_output(sampled_data: Dict[str, List[Dict]], output_file: str):
+    """Write sampled data to output file."""
+    print(f"\nWriting output to {output_file}...")
+    total_samples = 0
+    with open(output_file, 'w', encoding='utf-8') as f:
+        for language, samples in sorted(sampled_data.items()):
+            for sample in samples:
+                f.write(json.dumps(sample, ensure_ascii=False) + '\n')
+                total_samples += 1
+    print(f"Written {total_samples} samples to {output_file}")
+
+def main():
+    source_file = "/projects/data/Embedding/IndicToolkit/datasets_final/data/merged_transliteration_sampled.jsonl"
+    train_file = "/projects/data/Embedding/IndicToolkit/datasets_final/data/sampled_100k_translit.jsonl"
+    output_file = "/projects/data/Embedding/IndicToolkit/datasets_final/data/test_split_translit.jsonl"
+    
+    # Set random seed for reproducibility
+    random.seed(42)
+    
+    # Load train split to avoid duplicates
+    train_samples = load_train_split(train_file)
+    
+    # Sample test split
+    sampled_data = sample_test_split(
+        source_file=source_file,
+        train_samples=train_samples,
+        samples_per_language=1000,
+        max_words=100
+    )
+    
+    # Write output
+    write_output(sampled_data, output_file)
+    
+    print("\nDone!")
+
+if __name__ == "__main__":
+    main()
+

normalization/sampled_100k_norm.jsonl

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+oid sha256:d8c9106fe66602bc8e970e8e884e265c32cba53fdc7bd5f092c623284125fa85
+size 195199499

normalization/sampled_100k_translit.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f3f1b401d00531fd55f4f63f67094a8b4ccb3a96a6bf833233096c601aafede
+size 294123706

normalization/test_split_translit.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f673d2ba5012a3110950811cc49c27390576399304922823937295019fd4eb2
+size 10589046

normalization/train_set_punct_2.02M_ready.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a6ce43128a1c2f0cfab9adad54a8c95813e171a40c314c9774ecddb8f1283f12
+size 24622765526

normalization/train_set_punct_final_no_leak.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:55e2015311cbd174340c6abbc20920b715a95582d39bed47ae80ef34eae0ad38
+size 8318667133

normalization/transliteration_sampled_850k.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5a13d77b258bdad3868c30da8190a46684d87db22ebee5da8b4427999662b1ed
+size 1644349336

normalization/transliteration_sampled_850k_no_leak.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9f0cdd6f7a0f9f10703b3634ecf5db27c9518eaf5511c127178e16bf6f316c03
+size 1638823710