Datasets:

stukenov
/

s-openbench-eval

- {"results": {"kk_history_of_kazakhstan_unt_mc": {"acc,none": 0.20889570552147238, "acc_stderr,none": 0.0, "alias": "kk_history_of_kazakhstan_unt_mc"}, "kk_biology_unt_mc": {"acc,none": 0.18098958333333334, "acc_stderr,none": 0.0, "alias": "kk_biology_unt_mc"}, "kk_constitution_mc": {"acc,none": 0.18115942028985507, "acc_stderr,none": 0.0, "alias": "kk_constitution_mc"}, "kk_dastur_mc": {"acc,none": 0.15223880597014924, "acc_stderr,none": 0.0, "alias": "kk_dastur_mc"}, "kk_english_unt_mc": {"acc,none": 0.16431497496586253, "acc_stderr,none": 0.0, "alias": "kk_english_unt_mc"}, "kk_geography_unt_mc": {"acc,none": 0.15133876600698487, "acc_stderr,none": 0.0, "alias": "kk_geography_unt_mc"}, "kk_world_history_unt_mc": {"acc,none": 0.12133550488599348, "acc_stderr,none": 0.0, "alias": "kk_world_history_unt_mc"}, "kazakh_and_literature_unt_mc": {"acc,none": 0.18835324232081913, "acc_stderr,none": 0.0, "alias": "kazakh_and_literature_unt_mc"}, "mmlu_translated_kk": {"acc,none": 0.24755062050947094, "acc_stderr,none": 0.0, "alias": "mmlu_translated_kk"}, "kk_human_society_rights_unt_mc": {"acc,none": 0.1891891891891892, "acc_stderr,none": 0.0, "alias": "kk_human_society_rights_unt_mc"}}, "group_subtasks": {"kazakh_and_literature_unt_mc": [], "kk_biology_unt_mc": [], "kk_constitution_mc": [], "kk_dastur_mc": [], "kk_english_unt_mc": [], "kk_geography_unt_mc": [], "kk_history_of_kazakhstan_unt_mc": [], "kk_human_society_rights_unt_mc": [], "kk_world_history_unt_mc": [], "mmlu_translated_kk": []}, "configs": {"kazakh_and_literature_unt_mc": {"task": "kazakh_and_literature_unt_mc", "dataset_path": "kz-transformers/kazakh-unified-national-testing-mc", "training_split": "kazakh_and_literature", "validation_split": "kazakh_and_literature", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['correct_answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"{s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"{s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"{s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"{gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "kk_biology_unt_mc": {"task": "kk_biology_unt_mc", "dataset_path": "kz-transformers/kazakh-unified-national-testing-mc", "training_split": "biology", "validation_split": "biology", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['correct_answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"{s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"{s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"{s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"{gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "kk_constitution_mc": {"task": "kk_constitution_mc", "dataset_path": "kz-transformers/kazakh-constitution-mc", "training_split": "test", "validation_split": "test", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['Correct Answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"Option {s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"Option {s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"Option {s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"Option {s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"Option {s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"Option {gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{Question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "kk_dastur_mc": {"task": "kk_dastur_mc", "dataset_path": "kz-transformers/kazakh-dastur-mc", "training_split": "test", "validation_split": "test", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['Correct Answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"Option {s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"Option {s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"Option {s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"Option {s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"Option {s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"Option {gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{Question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "kk_english_unt_mc": {"task": "kk_english_unt_mc", "dataset_path": "kz-transformers/kazakh-unified-national-testing-mc", "training_split": "english", "validation_split": "english", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['correct_answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"{s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"{s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"{s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"{gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "kk_geography_unt_mc": {"task": "kk_geography_unt_mc", "dataset_path": "kz-transformers/kazakh-unified-national-testing-mc", "training_split": "geography", "validation_split": "geography", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['correct_answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"{s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"{s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"{s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"{gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "kk_history_of_kazakhstan_unt_mc": {"task": "kk_history_of_kazakhstan_unt_mc", "dataset_path": "kz-transformers/kazakh-unified-national-testing-mc", "training_split": "history_of_kazakhstan", "validation_split": "history_of_kazakhstan", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['correct_answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"{s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"{s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"{s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"{gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "kk_human_society_rights_unt_mc": {"task": "kk_human_society_rights_unt_mc", "dataset_path": "kz-transformers/kazakh-unified-national-testing-mc", "training_split": "human_society_rights", "validation_split": "human_society_rights", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['correct_answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"{s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"{s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"{s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"{gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "kk_world_history_unt_mc": {"task": "kk_world_history_unt_mc", "dataset_path": "kz-transformers/kazakh-unified-national-testing-mc", "training_split": "world_history", "validation_split": "world_history", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['correct_answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"{s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"{s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"{s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"{s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"{s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"{gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}, "mmlu_translated_kk": {"task": "mmlu_translated_kk", "dataset_path": "kz-transformers/mmlu-translated-kk", "training_split": "validation", "validation_split": "validation", "process_docs": "def process_docs(dataset: Dataset):\n def _helper(doc,index):\n field = doc['Correct Answer']\n fi = None\n if field == 'answerA' or field in ['A','\u0410']:\n fi = \"A\"\n if field == 'answerB' or field in ['B','\u0411']:\n fi = \"B\"\n if field == 'answerC' or field in ['C','\u0421']:\n fi = \"C\"\n if field == 'answerD' or field in ['D','\u0414']:\n fi = \"D\"\n if fi:\n doc[\"choices\"] = [doc[f\"Option {s}\"] for s in list('ABCD')]\n\n else:\n for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n if field == val:\n fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n doc[\"choices\"] = [doc[f\"Option {s}\"] for s in list('ABCD')]\n if index == 0:\n next_doc1 = dataset[index + 1]\n next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n doc[\"choices\"].extend([next_doc1[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc2[f\"Option {s}\"] for s in list('ABCD')])\n elif index == len(dataset) - 1:\n prev_doc1 = dataset[index - 1]\n prev_doc2 = dataset[index - 2]\n doc[\"choices\"].extend([prev_doc1[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([prev_doc2[f\"Option {s}\"] for s in list('ABCD')])\n else:\n prev_doc = dataset[index - 1]\n next_doc = dataset[index + 1]\n doc[\"choices\"].extend([prev_doc[f\"Option {s}\"] for s in list('ABCD')])\n doc[\"choices\"].extend([next_doc[f\"Option {s}\"] for s in list('ABCD')])\n\n label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n correct_label = label_map.get(fi)\n if not correct_label:\n return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n random.shuffle(doc[\"choices\"])\n gold = fi\n shuffled_label = doc[\"choices\"].index(doc[f\"Option {gold}\"])\n doc[\"label\"] = inv_label_map[shuffled_label]\n doc[\"gold\"] = inv_label_map[shuffled_label]\n\n return doc\n ds = dataset.map(_helper,with_indices=True)\n return ds.filter(lambda x: len(x['gold'])==1)\n", "doc_to_text": "{{Question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nI. {{choices[8]}}\nJ. {{choices[9]}}\nK. {{choices[10]}}\nL. {{choices[11]}}\n\u0416\u0430\u0443\u0430\u043f:", "doc_to_target": "label", "doc_to_choice": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"], "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [{"metric": "acc"}], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false}}, "versions": {"kazakh_and_literature_unt_mc": "Yaml", "kk_biology_unt_mc": "Yaml", "kk_constitution_mc": "Yaml", "kk_dastur_mc": "Yaml", "kk_english_unt_mc": "Yaml", "kk_geography_unt_mc": "Yaml", "kk_history_of_kazakhstan_unt_mc": "Yaml", "kk_human_society_rights_unt_mc": "Yaml", "kk_world_history_unt_mc": "Yaml", "mmlu_translated_kk": "Yaml"}, "n-shot": {"kazakh_and_literature_unt_mc": 0, "kk_biology_unt_mc": 0, "kk_constitution_mc": 0, "kk_dastur_mc": 0, "kk_english_unt_mc": 0, "kk_geography_unt_mc": 0, "kk_history_of_kazakhstan_unt_mc": 0, "kk_human_society_rights_unt_mc": 0, "kk_world_history_unt_mc": 0, "mmlu_translated_kk": 0}, "n-samples": {"mmlu_translated_kk": {"original": 1150, "effective": 1150}, "kk_world_history_unt_mc": {"original": 1055, "effective": 1055}, "kk_human_society_rights_unt_mc": {"original": 149, "effective": 149}, "kk_history_of_kazakhstan_unt_mc": {"original": 2409, "effective": 2409}, "kk_geography_unt_mc": {"original": 1001, "effective": 1001}, "kk_english_unt_mc": {"original": 1859, "effective": 1859}, "kk_dastur_mc": {"original": 532, "effective": 532}, "kk_constitution_mc": {"original": 240, "effective": 240}, "kk_biology_unt_mc": {"original": 1321, "effective": 1321}, "kazakh_and_literature_unt_mc": {"original": 3376, "effective": 3376}}, "config": {"model": "hf", "model_args": "pretrained=meta-llama/Llama-3.1-8B-Instruct", "model_num_parameters": 8030261248, "model_dtype": "torch.bfloat16", "model_revision": "main", "model_sha": "0e9e39f249a16976918f6564b8830bc894c89659", "batch_size": "1", "batch_sizes": [], "device": "cuda:0", "use_cache": null, "limit": null, "bootstrap_iters": 100000, "gen_kwargs": null, "random_seed": 0, "numpy_seed": 1234, "torch_seed": 1234, "fewshot_seed": 1234}, "git_hash": null, "date": 1734352265.6746726, "pretty_env_info": "PyTorch version: 2.5.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: 14.0.0-1ubuntu1.1\nCMake version: version 3.30.5\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 6 2024, 20:22:13) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-6.1.85+-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.140\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB\nNvidia driver version: 535.104.05\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 12\nOn-line CPU(s) list: 0-11\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) CPU @ 2.20GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 6\nSocket(s): 1\nStepping: 7\nBogoMIPS: 4400.38\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 192 KiB (6 instances)\nL1i cache: 192 KiB (6 instances)\nL2 cache: 6 MiB (6 instances)\nL3 cache: 38.5 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-11\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Vulnerable\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Vulnerable\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers\nVulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Vulnerable; BHI: Vulnerable (Syscall hardening enabled)\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Vulnerable\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] optree==0.13.1\n[pip3] torch==2.5.1+cu121\n[pip3] torchaudio==2.5.1+cu121\n[pip3] torchsummary==1.5.1\n[pip3] torchvision==0.20.1+cu121\n[conda] Could not collect", "transformers_version": "4.46.3", "upper_git_hash": null, "task_hashes": {}, "model_source": "hf", "model_name": "stukenov/sozkz-core-llama-150m-kk-instruct-v2", "model_name_sanitized": "stukenov__sozkz-core-llama-150m-kk-instruct-v2", "start_time": 1465.518727027, "end_time": 2624.160495532, "total_evaluation_time_seconds": "1158.6417685049998"}

+ {"model": "stukenov/sozkz-core-llama-150m-kk-instruct-v2", "mmlu_translated_kk": 0.24755062050947094, "kk_constitution_mc": 0.18115942028985507, "kk_dastur_mc": 0.15223880597014924, "kazakh_and_literature_unt_mc": 0.18835324232081913, "kk_geography_unt_mc": 0.15133876600698487, "kk_world_history_unt_mc": 0.12133550488599348, "kk_history_of_kazakhstan_unt_mc": 0.20889570552147238, "kk_english_unt_mc": 0.16431497496586253, "kk_biology_unt_mc": 0.18098958333333334, "kk_human_society_rights_unt_mc": 0.1891891891891892, "model_dtype": "torch.bfloat16", "ppl": 0}