import json def parse_balochi_eng_file(file_path): context_dict = { "Daily Vocabulary": [], "General Actions": [], "Geography and Locations": [], "Feelings and States": [], "Technical Terms": [] } with open(file_path, 'r', encoding='utf-8') as file: for line in file: balochi, english = line.strip().split('\t') # Categorize translations based on keywords if 'ءَ' in balochi or 'بابت' in balochi or 'بُرز' in balochi or 'درمُلک' in balochi: context_dict["Geography and Locations"].append({"balochi": balochi, "english": english}) elif 'بیرَگا' in balochi or 'چِنت' in balochi or 'گیشی' in balochi: context_dict["Feelings and States"].append({"balochi": balochi, "english": english}) elif 'سافٹ' in balochi or 'ہارڈ' in balochi: context_dict["Technical Terms"].append({"balochi": balochi, "english": english}) else: context_dict["General Actions"].append({"balochi": balochi, "english": english}) dataset_metadata = { "name": "Balochi to English Translation Corpus", "version": "1.0.0", "purpose": "Translation Dataset for Language Model Training", "languages": ["Balochi", "English"], "total_translations": sum(len(translations) for translations in context_dict.values()), "domains": ["general", "technical", "cultural", "literary", "historical"], "creation_date": "2024-12-14", "license": "CC-BY-SA 4.0" } dataset_structure = { "dataset_metadata": dataset_metadata, "translation_samples": [ {"context": context, "translations": translations} for context, translations in context_dict.items() ] } return dataset_structure # Example usage file_path = 'data_bal_en.tsv' dataset_json = parse_balochi_eng_file(file_path) # Save to a file output_file_path = 'balochi_translation_corpus.json' with open(output_file_path, 'w', encoding='utf-8') as json_file: json.dump(dataset_json, json_file, ensure_ascii=False, indent=2) output_file_path