[{"id": 616469, "created": "2025-08-06T04:11:05.816898", "project_id": 294, "task_id": 649009, "user_id": 315, "user_ip": null, "finish_time": "2025-08-06T04:12:23.697041", "timeout": null, "calibration": null, "external_uid": null, "media_url": null, "info": {"id": 2311, "src": "we use a conditional random field formalism to learn a model from labeled training data that can be applied to unseen data .", "target": "to predict labels , we train conditional random fields , which are directly optimized for splitting .", "clarity": "somewhat_unclear", "faithfulness": "faithful", "plausibility": "plausible", "informativeness": "very_informative"}}, {"id": 619524, "created": "2025-08-12T00:46:58.593894", "project_id": 294, "task_id": 649009, "user_id": 325, "user_ip": null, "finish_time": "2025-08-12T00:47:18.401120", "timeout": null, "calibration": null, "external_uid": null, "media_url": null, "info": {"id": 2311, "src": "we use a conditional random field formalism to learn a model from labeled training data that can be applied to unseen data .", "target": "to predict labels , we train conditional random fields , which are directly optimized for splitting .", "clarity": "clear", "faithfulness": "faithful", "plausibility": "plausible", "informativeness": "informative"}}]