{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"terminal-bench-2-1","formal_name":"Terminal-Bench 2.1","introduction":"ターミナル環境で作業を遂行するエージェントの能力を評価するベンチマークです。各課題に作業指示と環境設定があり、2.0とは別の版として扱います。\n\nTerminal-Bench 2.1 evaluates agents performing tasks in terminal environments. Each task supplies instructions and environment configuration, and version 2.1 is tracked separately from 2.0.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://github.com/harbor-framework/terminal-bench-2-1","indexing_mode":"noindex"},"task_id":"190dbbfb-9c10-5a54-825b-4acf5febe254","task_key":"tasks--train~2dfasttext","task_revision_id":"1","upstream_id":"train-fasttext","short_description":"Please train a fasttext model on the yelp data in the data/ folder.","config":"","split":"tasks","body":"{\"instruction\":\"Please train a fasttext model on the yelp data in the data/ folder.\\n\\nThe final model size needs to be less than 150MB but get at least 0.62 accuracy on a private test set that comes from the same yelp review distribution.\\n\\nThe model should be saved as /app/model.bin\\n\"}","display_format":"text","language":"","answer_status":"unknown","assets":[],"source_url":"https://github.com/harbor-framework/terminal-bench-2-1","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}