{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":84208,"offset":84198,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"1. Luxurious - having a high level of comfort and quality, often including deluxe amenities and upscale features\n2. Basic - offering only the most essential features and amenities\n3. Well-equipped - containing all the necessary facilities, tools, or equipment for a particular purpose\n4. State-of-the-art - incorporating the latest and most advanced technology and design\n5. Comprehensive - featuring a wide range of amenities and services, often catering to the needs of a specific group or industry\n6. Modern - using the latest techniques and technology, often featuring streamlined and minimalist design\n7. Comfortable - creating a relaxed and inviting atmosphere, often featuring plush furnishings and cozy amenities\n8. Convenient - offering easy access to necessary facilities and services\n9. Well-stocked - containing a plentiful selection of goods or resources, typically for use by visitors or guests\n10. Accessible - designed to be easily reached, entered, or used by people with disabilities or mobility issues\n11. Multi-functional - offering multiple uses or purposes for a given space or amenity\n12. Recreational - designed for leisure and enjoyment, often featuring amenities such as pools, spas, or game rooms\n13. Family-friendly - catering to the needs and interests of families, often featuring child-friendly amenities and activities\n14. High-end - offering superior quality, service, and features, often at a premium price\n15. Sustainable - designed to minimize waste and environmental impact, often featuring eco-friendly amenities and practices.","behavior":"degenerate_repetition","conversation_hash":"137e3997bb941a34c6f5b86495583278","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0301","n_chars":1614,"n_turns_in_conversation":6,"n_words":242,"source_row_offset":84198,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:07 GMT","turn_id":"ada26b5661288825e31c5adf","turn_index":5,"user_text":"IELTS vocabulary to describe a place's amenities"},{"assistant_initiates":false,"asst_text":"The Python code has been run through pylint, a Python code analysis tool, which generates a report with a score of 7.18 out of 10. The report highlights several issues in the code such as line too long, missing docstrings, too many local variables, and invalid variable naming. These issues can affect the code's readability, maintainability, and reliability. The report recommends making improvements and adhering to PEP 8 style guide recommendations for cleaner and more consistent code.","behavior":"degenerate_repetition","conversation_hash":"76fb5fa3b2c229201a82698901bb6214","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0301","n_chars":4282,"n_turns_in_conversation":3,"n_words":382,"source_row_offset":84199,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:07 GMT","turn_id":"20947e8177d382e70522438b","turn_index":0,"user_text":"(heart_venv) PS C:\\Users\\lpoti\\Documents\\DS_21\\DS12-1-develop\\src> pylint heart\n************* Module heart.train\nheart\\train.py:41:0: C0301: Line too long (102/100) (line-too-long)\nheart\\train.py:109:0: C0304: Final newline missing (missing-final-newline)\nheart\\train.py:14:0: E0401: Unable to import 'entities' (import-error)\nheart\\train.py:15:0: E0401: Unable to import 'data' (import-error)\nheart\\train.py:16:0: E0401: Unable to import 'features' (import-error)\nheart\\train.py:17:0: E0401: Unable to import 'models' (import-error)\nheart\\train.py:35:0: R0914: Too many local variables (19/15) (too-many-locals)\n************* Module heart.data.make_dataset\nheart\\data\\make_dataset.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n************* Module heart.data\nheart\\data\\__init__.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n************* Module heart.entities.config\nheart\\entities\\config.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n************* Module heart.entities.feature\nheart\\entities\\feature.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n************* Module heart.entities.models\nheart\\entities\\models.py:1:0: C0114: Missing module docstring (missing-module-docstring)\nheart\\entities\\models.py:20:4: C0103: Attribute name \"C\" doesn't conform to snake_case naming style (invalid-name)\n************* Module heart.entities\nheart\\entities\\__init__.py:1:0: C0114: Missing module docstring (missing-module-docstring)\nheart\\entities\\__init__.py:8:4: E0603: Undefined variable name 'ModelConfigRFConfigLogregConfig' in __all__ (undefined-all-variable)\n************* Module heart.features.build_features\nheart\\features\\build_features.py:1:0: C0114: Missing module docstring (missing-module-docstring)\nheart\\features\\build_features.py:96:30: C0103: Argument name \"x\" doesn't conform to snake_case naming style (invalid-name)\nheart\\features\\build_features.py:112:18: C0103: Argument name \"x\" doesn't conform to snake_case naming style (invalid-name)\nheart\\features\\build_features.py:112:21: C0103: Argument name \"y\" doesn't conform to snake_case naming style (invalid-name)\nheart\\features\\build_features.py:112:18: W0613: Unused argument 'x' (unused-argument)\nheart\\features\\build_features.py:112:21: W0613: Unused argument 'y' (unused-argument)\nheart\\features\\build_features.py:122:24: C0103: Argument name \"x\" doesn't conform to snake_case naming style (invalid-name)\n************* Module heart.features.__init__\nheart\\features\\__init__.py:7:0: C0304: Final newline missing (missing-final-newline)\nheart\\features\\__init__.py:6:0: C0301: Line too long (107/100) (line-too-long)\n************* Module heart.features\nheart\\features\\__init__.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n************* Module heart.models.fit\nheart\\models\\fit.py:38:0: C0304: Final newline missing (missing-final-newline)\nheart\\models\\fit.py:1:0: C0114: Missing module docstring (missing-module-docstring)\nheart\\models\\fit.py:35:4: C0415: Import outside toplevel (pickle) (import-outside-toplevel)\nheart\\models\\fit.py:36:31: C0103: Variable name \"f\" doesn't conform to snake_case naming style (invalid-name)\n************* Module heart.models.predict\nheart\\models\\predict.py:38:0: C0304: Final newline missing (missing-final-newline)\nheart\\models\\predict.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n************* Module heart.models.__init__\nheart\\models\\__init__.py:16:0: C0304: Final newline missing (missing-final-newline)\n************* Module heart.models\nheart\\models\\__init__.py:1:0: C0114: Missing module docstring (missing-module-docstring)\n\n------------------------------------------------------------------\nYour code has been rated at 7.18/10 (previous run: 4.64/10, +2.54)"},{"assistant_initiates":false,"asst_text":"\u041a\u043e\u043d\u0435\u0447\u043d\u043e, \u044f \u043c\u043e\u0433\u0443 \u043f\u043e\u043c\u043e\u0447\u044c \u0438\u0441\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u043d\u0430\u0439\u0434\u0435\u043d\u043d\u044b\u0435 \u043e\u0448\u0438\u0431\u043a\u0438. \u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043d\u0430\u0447\u043d\u0435\u043c \u0441 \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u043a\u0440\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043f\u0440\u043e\u0431\u043b\u0435\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0433\u0443\u0442 \u043f\u043e\u0432\u043b\u0438\u044f\u0442\u044c \u043d\u0430 \u0440\u0430\u0431\u043e\u0442\u0443 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b. \n\n1. \u041e\u0448\u0438\u0431\u043a\u0430 \u0438\u043c\u043f\u043e\u0440\u0442\u0430\n\n\u0412 \u0444\u0430\u0439\u043b\u0435 `heart/train.py` \u0435\u0441\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0441\u0442\u0440\u043e\u043a\u0438:\n\n```python\nimport entities\nimport data\nimport features\nimport models\n```\n\n\u041e\u043d\u0438 \u0432\u044b\u0437\u044b\u0432\u0430\u044e\u0442 \u043e\u0448\u0438\u0431\u043a\u0443 \u0438\u043c\u043f\u043e\u0440\u0442\u0430 \u0438 \u0432 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435 \u0434\u0430\u043b\u044c\u043d\u0435\u0439\u0448\u0435\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u043a\u043e\u0434\u0430 \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0441\u044f \u043d\u0435\u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u043c.\n\n\u0418\u0441\u043f\u0440\u0430\u0432\u0438\u043c \u0438\u0445 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:\n\n```python\nfrom heart.entities import *\nfrom heart.data import *\nfrom heart.features import *\nfrom heart.models import *\n```\n\n2. \u0421\u043b\u0438\u0448\u043a\u043e\u043c \u0434\u043b\u0438\u043d\u043d\u0430\u044f \u0441\u0442\u0440\u043e\u043a\u0430\n\n\u0412 \u0444\u0430\u0439\u043b\u0435 `heart/train.py` \u0435\u0441\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0430, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043f\u0440\u0435\u0432\u044b\u0448\u0430\u0435\u0442 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0443\u044e \u0434\u043b\u0438\u043d\u0443 \u0441\u0442\u0440\u043e\u043a\u0438:\n\n```python\ndef train_and_evaluate(X_train: pd.DataFrame, X_test: pd.DataFrame, y_train: pd.DataFrame, y_test: pd.DataFrame, model_type: str, model_hyperparams: dict, model_path: str, scores_path: str, transformer: transformers.Transformer):\n```\n\n\u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442\u0441\u044f \u0440\u0430\u0437\u0431\u0438\u0442\u044c \u0435\u0435 \u043d\u0430 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u0440\u043e\u043a:\n\n```python\ndef train_and_evaluate(\n    X_train: pd.DataFrame, \n    X_test: pd.DataFrame, \n    y_train: pd.DataFrame, \n    y_test: pd.DataFrame, \n    model_type: str, \n    model_hyperparams: dict, \n    model_path: str, \n    scores_path: str, \n    transformer: transformers.Transformer\n):\n```\n\n3. \u041e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u0435 \u0444\u0438\u043d\u0430\u043b\u044c\u043d\u043e\u0439 \u043d\u043e\u0432\u043e\u0439 \u0441\u0442\u0440\u043e\u043a\u0438\n\n\u0412 \u0444\u0430\u0439\u043b\u0430\u0445 `heart/train.py`, `heart.models.fit.py`, `heart.models.predict.py`, `heart.features.__init__.py`, `heart.models.__init__.py`, `heart.data.make_dataset.py` \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u0444\u0438\u043d\u0430\u043b\u044c\u043d\u0430\u044f \u043d\u043e\u0432\u0430\u044f \u0441\u0442\u0440\u043e\u043a\u0430.\n\n\u0414\u043e\u0431\u0430\u0432\u0438\u043c \u0435\u0435 \u0432 \u043a\u0430\u0436\u0434\u044b\u0439 \u0438\u0437 \u044d\u0442\u0438\u0445 \u0444\u0430\u0439\u043b\u043e\u0432 \u0434\u043b\u044f \u0442\u043e\u0433\u043e, \u0447\u0442\u043e\u0431\u044b \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c \u043e\u0448\u0438\u0431\u043a\u0438 \u043b\u0438\u043d\u0442\u0435\u0440\u0430:\n\n```python\n# add a blank line at the end of the file\n```\n\n4. \u041e\u0447\u0435\u043d\u044c \u043c\u043d\u043e\u0433\u043e \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u044b\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445\n\n\u0412 \u0444\u0430\u0439\u043b\u0435 `heart/train.py` \u0435\u0441\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044f `train_and_evaluate`, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0438\u043c\u0435\u0435\u0442 \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u043c\u043d\u043e\u0433\u043e \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u044b\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445, \u0447\u0442\u043e \u0437\u0430\u0442\u0440\u0443\u0434\u043d\u044f\u0435\u0442 \u043f\u043e\u043d\u0438\u043c\u0430\u043d\u0438\u0435 \u043a\u043e\u0434\u0430.\n\n\u041f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u044e \u0432\u044b\u0434\u0435\u043b\u0438\u0442\u044c \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u043d\u0430 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0431\u043e\u043b\u0435\u0435 \u043f\u0440\u043e\u0441\u0442\u044b\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439, \u0447\u0442\u043e\u0431\u044b \u0441\u043e\u043a\u0440\u0430\u0442\u0438\u0442\u044c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u044b\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445.\n\n5. \u041d\u0435\u0432\u0435\u0440\u043d\u043e\u0435 \u0438\u043c\u0435\u043d\u043e\u0432\u0430\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445\n\n\u041d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u043d\u0435 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0442 \u043f\u0440\u0438\u043d\u044f\u0442\u043e\u043c\u0443 \u0432 Python \u0441\u0442\u0438\u043b\u044e \u0438\u043c\u0435\u043d\u043e\u0432\u0430\u043d\u0438\u044f. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, `ModelConfigRFConfigLogregConfig` \u0432 `heart.entities.__init__.py`, `f` \u0432 `heart.models.fit.py`.\n\n\u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442\u0441\u044f \u043f\u0435\u0440\u0435\u0438\u043c\u0435\u043d\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0432 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u0438 \u0441 \u043f\u0440\u0438\u043d\u044f\u0442\u044b\u043c \u0441\u0442\u0438\u043b\u0435\u043c \u0438\u043c\u0435\u043d\u043e\u0432\u0430\u043d\u0438\u044f \u0432 Python. \n\n6. \u041e\u0448\u0438\u0431\u043a\u0438 \u0432 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439\n\n\u0412 \u0444\u0430\u0439\u043b\u0435 `heart/features/build_features.py` \u0435\u0441\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044f `normalize_data`, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u0435\u0442 \u0434\u0432\u0430 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430, `x` \u0438 `y`, \u043e\u0434\u043d\u0430\u043a\u043e \u043e\u043d\u0438 \u043d\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442\u0441\u044f \u0432\u043d\u0443\u0442\u0440\u0438 \u0444\u0443\u043d\u043a\u0446\u0438\u0438. \u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442\u0441\u044f \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0438\u0445:\n\n```python\ndef normalize_data(df: pd.DataFrame) -> pd.DataFrame:\n    ...\n```\n\n\u0422\u0430\u043a\u0436\u0435 \u0432 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 `build_transformer` \u0432 `heart/features/build_features.py` \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 `x` \u0434\u043e\u043b\u0436\u0435\u043d \u0431\u044b\u0442\u044c \u043f\u0435\u0440\u0435\u0438\u043c\u0435\u043d\u043e\u0432\u0430\u043d \u0432 `df`.\n\n```python\n# Bad code\ndef build_transformer(x: pd.DataFrame, features_params: Dict[str, Union[str, List[str]]]) -> Transformers:\n    ...\n    \n# Good code\ndef build_transformer(df: pd.DataFrame, features_params: Dict[str, Union[str, List[str]]]) -> Transformers:\n    ...\n```\n\n7. \u041e\u0448\u0438\u0431\u043a\u0438 \u0432 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 __all__\n\n\u0412 \u0444\u0430\u0439\u043b\u0430\u0445 `heart.entities.__init__.py` \u0438 `heart.transformers.__init__.py` \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f `__all__`, \u043d\u043e \u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043d\u0435 \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043d\u0430 \u0432\u0441\u0435 \u0438\u043c\u0435\u043d\u0430 \u043c\u043e\u0434\u0443\u043b\u0435\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0445\u043e\u0442\u044f\u0442 \u044d\u043a\u0441\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c.\n\n\u041f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u044e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0432\u0430\u0440\u0438\u0430\u043d\u0442:\n\n```python\n# heart/entities/__init__.py\n\nfrom .config import *\nfrom .feature import *\nfrom .models import *\n\n__all__ = [\n    'ModelConfig', \n    'RFConfig', \n    'LogregConfig',\n    'FeatureParams',\n    'Entity',\n    'DataFrameColumn',\n]\n\n# heart/transformers/__init__.py\n\nfrom .build_transformer import *\n\n__all__ = [\n    'Transformers',\n    'build_transformer',\n]\n```\n\n\u042d\u0442\u043e \u043f\u043e\u0437\u0432\u043e\u043b\u0438\u0442 \u044f\u0432\u043d\u043e \u0443\u043a\u0430\u0437\u0430\u0442\u044c, \u043a\u0430\u043a\u0438\u0435 \u0438\u043c\u0435\u043d\u043d\u043e \u043e\u0431\u044a\u0435\u043a\u0442\u044b \u0434\u043e\u043b\u0436\u043d\u044b \u0431\u044b\u0442\u044c \u044d\u043a\u0441\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u044b.\n\n\u041f\u043e\u0441\u043b\u0435 \u0438\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0432\u044b\u0448\u0435\u0443\u043a\u0430\u0437\u0430\u043d\u043d\u044b\u0445 \u043e\u0448\u0438\u0431\u043e\u043a \u043c\u043e\u0436\u043d\u043e \u0437\u0430\u043d\u043e\u0432\u043e \u0437\u0430\u043f\u0443\u0441\u0442\u0438\u0442\u044c \u043b\u0438\u043d\u0442\u0435\u0440 \u0438 \u0443\u0431\u0435\u0434\u0438\u0442\u044c\u0441\u044f, \u0447\u0442\u043e \u043e\u043d \u0431\u043e\u043b\u044c\u0448\u0435 \u043d\u0435 \u0432\u044b\u0434\u0430\u0435\u0442 \u043e\u0448\u0438\u0431\u043a\u0438.","behavior":"degenerate_repetition","conversation_hash":"76fb5fa3b2c229201a82698901bb6214","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0301","n_chars":3750,"n_turns_in_conversation":3,"n_words":418,"source_row_offset":84200,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:07 GMT","turn_id":"11910b5a45512f3bbd5cb16a","turn_index":1,"user_text":"\u043f\u043e\u043c\u043e\u0436\u0435\u0448\u044c \u043c\u043d\u0435 \u0438\u0441\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u043e\u0448\u0438\u0431\u043a\u0438?"},{"assistant_initiates":false,"asst_text":"\u0418\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u043d\u044b\u0439 \u043a\u043e\u0434:\n\n```python\n\"\"\"\n\u041c\u043e\u0434\u0443\u043b\u044c \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438 \u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043e\u0447\u043d\u043e\u0433\u043e \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0430.\n\"\"\"\n\nimport pickle\nimport os\nimport json\nimport logging\nimport logging.config\n\nimport hydra\nimport pandas as pd\n\nfrom heart.entities import TrainingPipelineConfig\nfrom heart.data import read_data, split_train_test_data\nfrom heart.features import build_transformer, make_features, extract_target\nfrom heart.models import train_model, predict_model, evaluate_model\n\nlogger = logging.getLogger(__name__)\n\n\ndef prepare_val_features_for_predict(train_features: pd.DataFrame, val_features: pd.DataFrame) -> pd.DataFrame:\n    \"\"\"\n    \u041f\u043e\u0434\u0433\u043e\u0442\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u0442 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0438 \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f.\n\n    :param train_features: DataFrame \u0441 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u043c\u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c\u0438.\n    :param val_features: DataFrame \u0441 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u043c\u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c\u0438.\n    :return: DataFrame \u0441 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u043d\u044b\u043c\u0438 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u043c\u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c\u0438.\n    \"\"\"\n    train_features, val_features = train_features.align(val_features, join=\"left\", axis=1)\n    val_features = val_features.fillna(0)\n    return val_features\n\n\ndef train_pipeline(params: TrainingPipelineConfig) -> None:\n    \"\"\"E2E \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438\"\"\"\n    logger.info(\"\u041d\u0430\u0447\u0430\u043b\u043e \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0430\")\n    data = read_data(params.dataset.input_data_path)\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u0434\u0430\u043d\u043d\u044b\u0445: %s\", data.shape)\n\n    train_df, test_df = split_train_test_data(data, params.split.test_size, params.split.random_state)\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445: %s\", train_df.shape)\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445: %s\", test_df.shape)\n\n    logger.info(\"\u041d\u0430\u0447\u0430\u043b\u043e \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0439\")\n    transformer = build_transformer(\n        df=train_df,\n        categorical_features=params.feature.categorical_features,\n        numerical_features=params.feature.numerical_features,\n    )\n    transformer.fit(train_df)\n    train_features = make_features(transformer, train_df)\n    train_target = extract_target(train_df, params.feature.target_col)\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432: %s\", train_features.shape)\n\n    model = train_model(\n        model_params=params.model.model_params,\n        X_train=train_features, y_train=train_target\n    )\n\n    test_features = make_features(transformer, test_df)\n    test_target = extract_target(test_df, params.feature.target_col)\n\n    val_features_prepared = prepare_val_features_for_predict(\n        train_features, test_features\n    )\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432: %s\", val_features_prepared.shape)\n    predicts = predict_model(\n        model,\n        X_val=val_features_prepared\n    )\n\n    metrics = evaluate_model(\n        predicts,\n        test_target\n    )\n\n    metrics_output_dir = os.path.join(os.getcwd(), 'metrics')\n    model_output_dir = os.path.join(os.getcwd(), 'models')\n\n    os.makedirs(metrics_output_dir, exist_ok=True)\n    metrics_filepath = os.path.join(\n        metrics_output_dir, f\"{params.model.model_name}.json\"\n    )\n    with open(metrics_filepath, \"w\", encoding=\"utf-8\") as metric_file:\n        json.dump(metrics, metric_file)\n        logger.info(\"\u041c\u0435\u0442\u0440\u0438\u043a\u0438: %s\", metrics)\n    logger.info(\"\u041c\u0435\u0442\u0440\u0438\u043a\u0438 \u0441\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u044b \u0432 \u0444\u0430\u0439\u043b: %s\", metrics_filepath)\n\n    os.makedirs(model_output_dir, exist_ok=True)\n    models_filepath =","behavior":"degenerate_repetition","conversation_hash":"76fb5fa3b2c229201a82698901bb6214","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0301","n_chars":6885,"n_turns_in_conversation":3,"n_words":515,"source_row_offset":84201,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:07 GMT","turn_id":"c0e80b253f3fd91f226be8b9","turn_index":2,"user_text":"\u0412\u043e\u0442 \u0441\u0430\u043c \u043a\u043e\u0434: \"\"\"\n\u041c\u043e\u0434\u0443\u043b\u044c \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438 \u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043e\u0447\u043d\u043e\u0433\u043e \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0430.\n\"\"\"\n\nimport pickle\nimport os\nimport json\nimport logging\nimport logging.config\n\nimport hydra\nimport pandas as pd\n\nfrom entities import TrainingPipelineConfig\nfrom data import read_data, split_train_test_data\nfrom features import build_transformer, make_features, extract_target\nfrom models import train_model, predict_model, evaluate_model\n\nlogger = logging.getLogger(__name__)\n\ndef prepare_val_features_for_predict(\n    train_features: pd.DataFrame, val_features: pd.DataFrame\n) -> pd.DataFrame:\n    \"\"\"\n    \u041f\u043e\u0434\u0433\u043e\u0442\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u0442 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0438 \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f.\n\n    :param train_features: DataFrame \u0441 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u043c\u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c\u0438.\n    :param val_features: DataFrame \u0441 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u043c\u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c\u0438.\n    :return: DataFrame \u0441 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u043d\u044b\u043c\u0438 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u043c\u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c\u0438.\n    \"\"\"\n    train_features, val_features = train_features.align(val_features, join=\"left\", axis=1)\n    val_features = val_features.fillna(0)\n    return val_features\n\ndef train_pipeline(params: TrainingPipelineConfig) -> None:\n    \"\"\"E2E \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438\"\"\"\n    logger.info(\"\u041d\u0430\u0447\u0430\u043b\u043e \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0430\")\n    data = read_data(params.dataset.input_data_path)\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u0434\u0430\u043d\u043d\u044b\u0445: %s\", data.shape)\n\n    train_df, test_df = split_train_test_data(data, params.split.test_size, params.split.random_state)\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445: %s\", train_df.shape)\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445: %s\", test_df.shape)\n\n    logger.info(\"\u041d\u0430\u0447\u0430\u043b\u043e \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0439\")\n    transformer = build_transformer(\n        categorical_features=params.feature.categorical_features,\n        numerical_features=params.feature.numerical_features,\n    )\n    transformer.fit(train_df)\n    train_features = make_features(transformer, train_df)\n    train_target = extract_target(train_df, params.feature.target_col)\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432: %s\", train_features.shape)\n\n    model = train_model(\n        params.model.model_params,\n        train_features, train_target\n    )\n\n    test_features = make_features(transformer, test_df)\n    test_target = extract_target(test_df, params.feature.target_col)\n\n    val_features_prepared = prepare_val_features_for_predict(\n        train_features, test_features\n    )\n    logger.info(\"\u0420\u0430\u0437\u043c\u0435\u0440 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432: %s\", val_features_prepared.shape)\n    predicts = predict_model(\n        model,\n        val_features_prepared\n    )\n\n    metrics = evaluate_model(\n        predicts,\n        test_target\n    )\n\n    metrics_output_dir = os.path.join(os.getcwd(), 'metrics')\n    model_output_dir = os.path.join(os.getcwd(), 'models')\n\n    os.makedirs(metrics_output_dir, exist_ok=True)\n    metrics_filepath = os.path.join(\n        metrics_output_dir, f\"{params.model.model_name}.json\"\n    )\n    with open(metrics_filepath, \"w\", encoding=\"utf-8\") as metric_file:\n        json.dump(metrics, metric_file)\n        logger.info(\"\u041c\u0435\u0442\u0440\u0438\u043a\u0438: %s\", metrics)\n    logger.info(\"\u041c\u0435\u0442\u0440\u0438\u043a\u0438 \u0441\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u044b \u0432 \u0444\u0430\u0439\u043b: %s\", metrics_filepath)\n\n    os.makedirs(model_output_dir, exist_ok=True)\n    models_filepath = os.path.join(\n        model_output_dir, f\"{params.model.model_name}.pkl\"\n    )\n\n    # \u0421\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0432 \u0444\u0430\u0439\u043b\n    with open(models_filepath, \"wb\") as model_file:\n        pickle.dump(model, model_file)\n    logger.info(\"\u041c\u043e\u0434\u0435\u043b\u044c \u0441\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0430 \u0432 \u0444\u0430\u0439\u043b: %s\", models_filepath)\n\n    logger.info(\"\u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u043e\")\n\n\n@hydra.main(config_path=\"../configs\", config_name=\"config\", version_base=\"1.1\")\ndef main(cfg: TrainingPipelineConfig = None) -> None:\n    \"\"\"\u041e\u0441\u043d\u043e\u0432\u043d\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438 \u043b\u043e\u0433\u0433\u0435\u0440\u0430 \u0438 \u0437\u0430\u043f\u0443\u0441\u043a\u0430 train_pipeline\"\"\"\n    train_pipeline(cfg)\n\n\nif __name__ == \"__main__\":\n    main() \u0438\u0441\u043f\u0440\u0430\u0432\u044c \u043f\u043e\u0436\u0430\u043b\u0443\u0439\u0441\u0442\u0430"},{"assistant_initiates":false,"asst_text":"\u0422\u0438\u0442\u0430\u043d\u0438\u043a \u0437\u0430\u0442\u043e\u043d\u0443\u043b \u0432 \u0421\u0435\u0432\u0435\u0440\u043d\u043e\u0439 \u0410\u0442\u043b\u0430\u043d\u0442\u0438\u043a\u0435 \u0432 \u043d\u043e\u0447\u044c \u0441 14 \u043d\u0430 15 \u0430\u043f\u0440\u0435\u043b\u044f 1912 \u0433\u043e\u0434\u0430 \u043f\u043e\u0441\u043b\u0435 \u0441\u0442\u043e\u043b\u043a\u043d\u043e\u0432\u0435\u043d\u0438\u044f \u0441 \u0430\u0439\u0441\u0431\u0435\u0440\u0433\u043e\u043c. \u041a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u043c\u0435\u0441\u0442\u0430 \u043a\u0440\u0443\u0448\u0435\u043d\u0438\u044f: 41\u00b043\u203255\u2033N 49\u00b056\u203245\u2033W.","behavior":"degenerate_repetition","conversation_hash":"dd89fa6de3e1e74e9d4220b6a6829d15","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0301","n_chars":174,"n_turns_in_conversation":3,"n_words":26,"source_row_offset":84202,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:10 GMT","turn_id":"3466e624cd2301d9ef661127","turn_index":0,"user_text":"\u0433\u0434\u0435 \u0437\u0430\u0442\u043e\u043d\u0443\u043b \u0422\u0438\u0442\u0430\u043d\u0438\u043a?"},{"assistant_initiates":false,"asst_text":"\u0412 \u0421\u0435\u0432\u0435\u0440\u043d\u043e\u0439 \u0410\u0442\u043b\u0430\u043d\u0442\u0438\u043a\u0435, \u0433\u0434\u0435 \u0437\u0430\u0442\u043e\u043d\u0443\u043b \u0422\u0438\u0442\u0430\u043d\u0438\u043a, \u043e\u0431\u0438\u0442\u0430\u044e\u0442 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0435 \u0432\u0438\u0434\u044b \u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u0440\u044b\u0431, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043a\u0440\u0443\u043f\u043d\u0435\u0439\u0448\u0443\u044e \u043c\u043e\u0440\u0441\u043a\u0443\u044e \u0440\u044b\u0431\u0443 - \u0430\u043a\u0443\u043b\u0443-\u043c\u043e\u043b\u043e\u0442\u0430 \u0438 \u0433\u0438\u0433\u0430\u043d\u0442\u0441\u043a\u0438\u0439 \u043a\u0430\u0440\u043f. \u0422\u0430\u043a\u0436\u0435 \u0442\u0430\u043c \u0432\u0441\u0442\u0440\u0435\u0447\u0430\u044e\u0442\u0441\u044f \u0441\u0438\u043d\u0438\u0439 \u043c\u0430\u0440\u043b\u0438\u043d, \u043a\u0438\u0442\u044b, \u0433\u0438\u0433\u0430\u043d\u0442\u0441\u043a\u0438\u0435 \u0441\u043a\u0430\u0442\u044b \u0438 \u043c\u043d\u043e\u0433\u0438\u0435 \u0434\u0440\u0443\u0433\u0438\u0435 \u0432\u0438\u0434\u044b. \u041e\u0434\u043d\u0430\u043a\u043e \u0441\u0442\u043e\u0438\u0442 \u043e\u0442\u043c\u0435\u0442\u0438\u0442\u044c, \u0447\u0442\u043e \u0440\u0430\u0437\u043c\u0435\u0440\u044b \u0440\u044b\u0431, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0436\u043d\u043e \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u0442\u044c \u0432 \u0440\u0430\u0439\u043e\u043d\u0435 \u043a\u0440\u0443\u0448\u0435\u043d\u0438\u044f \u0422\u0438\u0442\u0430\u043d\u0438\u043a\u0430, \u0431\u043e\u043b\u044c\u0448\u0435 \u0441\u0432\u044f\u0437\u0430\u043d\u044b \u0441 \u0433\u0435\u043e\u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u043c \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u0435\u043c, \u0430 \u043d\u0435 \u0441\u043e \u0441\u0430\u043c\u043e\u0439 \u043a\u0430\u0442\u0430\u0441\u0442\u0440\u043e\u0444\u043e\u0439.","behavior":"degenerate_repetition","conversation_hash":"dd89fa6de3e1e74e9d4220b6a6829d15","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0301","n_chars":413,"n_turns_in_conversation":3,"n_words":59,"source_row_offset":84203,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:10 GMT","turn_id":"c23fd0be2a631c0a3f179979","turn_index":1,"user_text":"\u043a\u0430\u043a\u0438\u0435 \u0442\u0430\u043c \u0431\u043e\u043b\u044c\u0448\u0438\u0435 \u0440\u044b\u0431\u044b?"},{"assistant_initiates":false,"asst_text":"\u0420\u0430\u0437\u043c\u0435\u0440\u044b \u0440\u044b\u0431, \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u043c\u043e\u0436\u043d\u043e \u0432\u0441\u0442\u0440\u0435\u0442\u0438\u0442\u044c \u0432 \u0421\u0435\u0432\u0435\u0440\u043d\u043e\u0439 \u0410\u0442\u043b\u0430\u043d\u0442\u0438\u043a\u0435, \u043c\u043e\u0433\u0443\u0442 \u0432\u0430\u0440\u044c\u0438\u0440\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0432\u0438\u0434\u0430. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0430\u043a\u0443\u043b\u0430-\u043c\u043e\u043b\u043e\u0442, \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u044f \u0443\u0436\u0435 \u0443\u043f\u043e\u043c\u0438\u043d\u0430\u043b, \u043c\u043e\u0436\u0435\u0442 \u0434\u043e\u0441\u0442\u0438\u0433\u0430\u0442\u044c \u0434\u043b\u0438\u043d\u044b \u0431\u043e\u043b\u0435\u0435 6 \u043c\u0435\u0442\u0440\u043e\u0432 \u0438 \u0432\u0435\u0441\u0438\u0442\u044c \u0431\u043e\u043b\u0435\u0435 1 \u0442\u043e\u043d\u043d\u044b. \u0421\u0438\u043d\u0438\u0439 \u043c\u0430\u0440\u043b\u0438\u043d, \u0441\u0447\u0438\u0442\u0430\u044e\u0449\u0438\u0439\u0441\u044f \u043e\u0434\u043d\u0438\u043c \u0438\u0437 \u0441\u0430\u043c\u044b\u0445 \u0431\u044b\u0441\u0442\u0440\u044b\u0445 \u0432\u0438\u0434\u043e\u0432 \u0440\u044b\u0431, \u043c\u043e\u0436\u0435\u0442 \u0434\u043e\u0441\u0442\u0438\u0433\u0430\u0442\u044c \u0434\u043b\u0438\u043d\u044b \u0431\u043e\u043b\u0435\u0435 4,5 \u043c\u0435\u0442\u0440\u043e\u0432 \u0438 \u0432\u0435\u0441\u0438\u0442\u044c \u0431\u043e\u043b\u0435\u0435 600 \u043a\u0433. \u0413\u0438\u0433\u0430\u043d\u0442\u0441\u043a\u0438\u0435 \u0441\u043a\u0430\u0442\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0442\u0430\u043a\u0436\u0435 \u043e\u0431\u0438\u0442\u0430\u044e\u0442 \u0432 \u044d\u0442\u043e\u043c \u0440\u0430\u0439\u043e\u043d\u0435, \u043c\u043e\u0433\u0443\u0442 \u0438\u043c\u0435\u0442\u044c \u0434\u0438\u0430\u043c\u0435\u0442\u0440 \u043a\u043e\u043b\u044c\u0446\u0430 \u0441\u0432\u043e\u0438\u0445 \"\u043a\u0440\u044b\u043b\u044c\u0435\u0432\" \u0431\u043e\u043b\u0435\u0435 7 \u043c\u0435\u0442\u0440\u043e\u0432. \u0422\u0430\u043a\u0436\u0435 \u0441\u0442\u043e\u0438\u0442 \u0443\u043f\u043e\u043c\u044f\u043d\u0443\u0442\u044c, \u0447\u0442\u043e \u043a\u043e\u043c\u043c\u0435\u0440\u0447\u0435\u0441\u043a\u0438\u0435 \u0440\u044b\u0431\u044b, \u0442\u0430\u043a\u0438\u0435 \u043a\u0430\u043a \u0442\u0440\u0435\u0441\u043a\u0430 \u0438 \u043c\u0430\u043a\u0440\u0443\u0440\u0443\u0441, \u0442\u0430\u043a\u0436\u0435 \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u044e\u0442 \u0432 \u044d\u0442\u043e\u043c \u0440\u0430\u0439\u043e\u043d\u0435 \u0438 \u043c\u043e\u0433\u0443\u0442 \u0434\u043e\u0441\u0442\u0438\u0433\u0430\u0442\u044c \u0437\u043d\u0430\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0440\u0430\u0437\u043c\u0435\u0440\u043e\u0432.","behavior":"degenerate_repetition","conversation_hash":"dd89fa6de3e1e74e9d4220b6a6829d15","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0301","n_chars":602,"n_turns_in_conversation":3,"n_words":90,"source_row_offset":84204,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:10 GMT","turn_id":"623d0a9f1b48eefcfd726249","turn_index":2,"user_text":"\u0440\u0430\u0437\u043c\u0435\u0440\u044b"},{"assistant_initiates":false,"asst_text":"As an AI language model, I cannot browse internet and identify every Big Room song with the title in Korean. However, a search on music streaming platforms or online music databases might reveal the answer to this query.","behavior":"degenerate_repetition","conversation_hash":"ce4ab1ed5a86fd0aaae1ff38a02b5e05","conversation_redacted":false,"conversation_toxic":false,"country":"Spain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0301","n_chars":275,"n_turns_in_conversation":2,"n_words":49,"source_row_offset":84205,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:10 GMT","turn_id":"50dfefe6351b090624b867c9","turn_index":0,"user_text":"Are there any Big Room songs with the title in Korean?"},{"assistant_initiates":false,"asst_text":"As an AI language model, I am aware that search engines like Bing AI have the capability to browse the internet. However, I am programmed to only provide text-based responses to queries and cannot perform internet browsing activities myself.","behavior":"degenerate_repetition","conversation_hash":"ce4ab1ed5a86fd0aaae1ff38a02b5e05","conversation_redacted":false,"conversation_toxic":false,"country":"Spain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0301","n_chars":294,"n_turns_in_conversation":2,"n_words":48,"source_row_offset":84206,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:10 GMT","turn_id":"999acd92a6d5b0c366d54361","turn_index":1,"user_text":"You can't browse internet? Don't worry, Bing AI can."},{"assistant_initiates":false,"asst_text":"The \"-i\" option in fastboot is used to specify the USB vendor ID of the device when multiple USB devices are connected to the computer. This helps to identify the specific device to which fastboot commands should be sent.","behavior":"degenerate_repetition","conversation_hash":"bfeb2b74a10436b587d05e1a7ce576db","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0301","n_chars":251,"n_turns_in_conversation":5,"n_words":45,"source_row_offset":84207,"source_shard":"turns-00005.parquet","timestamp":"Thu, 22 Jun 2023 11:36:19 GMT","turn_id":"cced00ed9f8e7aa539d5588f","turn_index":0,"user_text":"what does meen -i in fastboot"}],"shard":{"index":5,"name":"turns-00005.parquet","rows":102836}}