add mgsm datasets (#1081)

* add mgsm datasets

* fix lint

* fix lint

* update mgsm

* update mgsm

* ease code spell

* update

* update

* update

---------
Co-authored-by: Leymore <zfz-960727@163.com>

configs/datasets/mgsm/README.md

+# MGSM
+
+## Introduction
+
+The following introduction comes from the abstract in [Language models are multilingual chain-of-thought reasoners](https://arxiv.org/abs/2210.03057)
+
+```
+We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset into ten typologically diverse languages.
+```
+
+## Official link
+
+### Paper
+
+[Language models are multilingual chain-of-thought reasoners](https://arxiv.org/abs/2210.03057)
+
+### Repository
+
+[MGSM](https://github.com/google-research/url-nlp)
+
+## Examples
+
+Input example I:
+
+```
+Solve this math problem. Give the reasoning steps before giving the final answer on the last line by itself in the format of "Answer:". Do not add anything other than the integer answer after "Answer:".
+
+Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?
+```
+
+Output example I (from GPT-4):
+
+```
+Answer: 18
+```
+
+## Evaluation results
+
+```
+dataset         version    metric         mode      llama-3-8b-instruct-hf
+--------------  ---------  -------------  ------  ------------------------
+mgsm_bn         b65151     accuracy       gen                        14.4
+mgsm_de         2cc8ae     accuracy       gen                        60
+mgsm_en         5de71e     accuracy       gen                        76
+mgsm_es         d6b459     accuracy       gen                        61.6
+mgsm_fr         813e3c     accuracy       gen                        54.4
+mgsm_ja         04424f     accuracy       gen                        42.8
+mgsm_ru         400469     accuracy       gen                        62.8
+mgsm_sw         9e41ed     accuracy       gen                         0.8
+mgsm_te         346d97     accuracy       gen                         0
+mgsm_th         e70bee     accuracy       gen                        44
+mgsm_zh         d5cf30     accuracy       gen                        28.4
+mgsm_latin      -          naive_average  gen                        50.56
+mgsm_non_latin  -          naive_average  gen                        32.07
+mgsm            -          naive_average  gen                        40.47
+```
+
+## Reference
+
+```
+@article{shi2022language,
+  title={Language models are multilingual chain-of-thought reasoners},
+  author={Shi, Freda and Suzgun, Mirac and Freitag, Markus and Wang, Xuezhi and Srivats, Suraj and Vosoughi, Soroush and Chung, Hyung Won and Tay, Yi and Ruder, Sebastian and Zhou, Denny and others},
+  journal={arXiv preprint arXiv:2210.03057},
+  year={2022}
+}
+```

configs/datasets/mgsm/mgsm_gen.py

+from mmengine.config import read_base
+
+with read_base():
+    from .mgsm_gen_d967bc import mgsm_datasets

configs/datasets/mgsm/mgsm_gen_d967bc.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.openicl.icl_evaluator import JiebaRougeEvaluator
+from opencompass.datasets import MGSMSDataset, MGSM_Evaluator, mgsm_postprocess
+
+
+ALL_LANGUAGES = ["bn", "de", "en", "es", "fr", "ja", "ru", "sw", "te", "th", "zh"]
+
+LANG_TO_INSTRUCTIONS = {
+    "en": """Solve this math problem. Give the reasoning steps before giving the final answer on the last line by itself in the format of "Answer:". Do not add anything other than the integer answer after "Answer:".\n\n{question}""",
+    "bn": """এই গণিতের সমস্যাটি সমাধান করুন। চূড়ান্ত উত্তর দেওয়ার আগে যুক্তিসম্পন্ন পদক্ষেপ প্রদান করুন। চূড়ান্ত উত্তরটি একক সংখ্যা হিসাবে "উত্তর:" এর পরে শেষ লাইনে দিন। "উত্তর:" এর পরে অন্য কিছু যুক্ত করবেন না।.\n\n{question}""",
+    "de": """Löse dieses Mathematikproblem. Gib die Schritte zur Begründung an, bevor du die endgültige Antwort in der letzten Zeile alleine im Format "Antwort:" gibst. Füge nichts anderes als die ganzzahlige Antwort nach "Antwort:" hinzu.\n\n{question}""",
+    "es": """Resuelve este problema matemático. Proporciona los pasos de razonamiento antes de dar la respuesta final en la última línea por sí misma en el formato de "Respuesta:". No añadas nada más que la respuesta entera después de "Respuesta:".\n\n{question}""",
+    "fr": """Résolvez ce problème de mathématiques. Donnez les étapes de raisonnement avant de fournir la réponse finale sur la dernière ligne elle-même dans le format de "Réponse:". N'ajoutez rien d'autre que la réponse entière après "Réponse:".\n\n{question}""",
+    "ja": """の数学の問題を解いてください。最終的な答えを出す前に、解答の推論過程を記述してください。そして最後の行には "答え:" の形式で答えを記述し、その後には整数の答え以外何も追加しないでください。\n\n{question}""",
+    "ru": """Решите эту математическую задачу. Объясните шаги рассуждения перед тем, как дать окончательный ответ в последней строке сам по себе в формате "Ответ:". Не добавляйте ничего, кроме целочисленного ответа после "Ответ:".\n\n{question}""",
+    "sw": """Suluhisha tatizo hili la hesabu. Toa hatua za mantiki kabla ya kutoa jibu la mwisho kwenye mstari wa mwisho peke yake katika muundo wa "Jibu:". Usiongeze chochote kingine isipokuwa jibu la integer baada ya "Jibu:".\n\n{question}""",
+    "te": """ఈ గణిత సమస్యను పరిష్కరించండి. చివరి సమాధానాన్ని ఇవ్వదానికి ముందు తర్కాత్మక అదుగులను ఇవ్వండి. చివరి పంక్తిలో మాత్రమే 'సమాధానం:' అనే ఆకారంలో చివరి సమాధానాద్ని ఇవ్వండి సమాధానం: తర్వాత పూర్ణాంక సమాధానానికి తప్పించి ఎదేనా చేర్చవద్దు.\n\n{question}""",
+    "th": """แก้ปัญหาคณิตศาสตร์นี้ ให้ให้ขั้นตอนการใช้เหตุผลก่อนที่จะให้คำตอบสุดท้ายในบรรทัดสุดท้ายโดยอยู่ในรูปแบบ "คำตอบ:" ไม่ควรเพิ่มอะไรนอกจากคำตอบที่เป็นจำนวนเต็มหลังจาก "คำตอบ:"\n\n{question}""",
+    "zh": """解决这个数学问题。在最后一行给出答案前，请提供推理步骤。最后一行应该以 "答案: " 的形式独立给出答案。在 "答案：" 后不要添加除整数答案之外的任何内容。\n\n{question}""",
+}
+
+mgsm_datasets = []
+for lang in ALL_LANGUAGES:
+    mgsm_reader_cfg = dict(input_columns=["question"], output_column="answer")
+
+    mgsm_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                round=[
+                    dict(role="HUMAN", prompt=LANG_TO_INSTRUCTIONS[lang]),
+                ]
+            ),
+        ),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=512),
+    )
+
+    mgsm_eval_cfg = dict(
+        evaluator=dict(type=MGSM_Evaluator),
+        pred_role="BOT",
+        pred_postprocessor=dict(type=mgsm_postprocess, lang=lang),
+    )
+
+    mgsm_datasets.append(
+        dict(
+            type=MGSMSDataset,
+            abbr=f"mgsm_{lang}",
+            path=f"data/mgsm/mgsm_{lang}.tsv",
+            reader_cfg=mgsm_reader_cfg,
+            infer_cfg=mgsm_infer_cfg,
+            eval_cfg=mgsm_eval_cfg,
+        )
+    )

configs/summarizers/example.py

@@ -11,6 +11,7 @@ with read_base():
     from .groups.tydiqa import tydiqa_summary_groups
     from .groups.xiezhi import xiezhi_summary_groups
     from .groups.scibench import scibench_summary_groups
+    from .groups.mgsm import mgsm_summary_groups
 
 summarizer = dict(
     summary_groups=sum([v for k, v in locals().items() if k.endswith("_summary_groups")], []),

configs/summarizers/groups/mgsm.py

+ALL_LANGUAGES = ["bn", "de", "en", "es", "fr", "ja", "ru", "sw", "te", "th", "zh"]
+LATIN_LANGUAGES = ["de", "en", "es", "fr", "sw"]
+NON_LATIN_LANGUAGES = ["bn", "ja", "ru", "te", "th", "zh"]
+
+mgsm_summary_groups = [
+    {'name': 'mgsm_latin', 'subsets': [f'mgsm_{lang}' for lang in LATIN_LANGUAGES]},
+    {'name': 'mgsm_non_latin', 'subsets': [f'mgsm_{lang}' for lang in NON_LATIN_LANGUAGES]},
+    {'name': 'mgsm', 'subsets': [f'mgsm_{lang}' for lang in ALL_LANGUAGES]},
+]

opencompass/datasets/__init__.py

@@ -71,6 +71,7 @@ from .math_intern import *  # noqa: F401, F403
 from .mathbench import *  # noqa: F401, F403
 from .mbpp import *  # noqa: F401, F403
 from .medbench import *  # noqa: F401, F403
+from .mgsm import *  # noqa: F401, F403
 from .mmlu import *  # noqa: F401, F403
 from .multirc import *  # noqa: F401, F403
 from .narrativeqa import *  # noqa: F401, F403

opencompass/datasets/mgsm.py

+import re
+
+from datasets import Dataset
+
+from opencompass.openicl.icl_evaluator import BaseEvaluator
+from opencompass.registry import LOAD_DATASET
+
+from .base import BaseDataset
+
+
+@LOAD_DATASET.register_module()
+class MGSMSDataset(BaseDataset):
+
+    @staticmethod
+    def load(path: str):
+        src_lines = open(path, 'r', encoding='utf-8').readlines()
+        data = {'question': [], 'answer': []}
+        for lines in src_lines:
+            question, answer = lines.strip().split('\t')
+            data['question'].append(question)
+            data['answer'].append(answer)
+
+        dataset = Dataset.from_dict({
+            'question': data['question'],
+            'answer': data['answer']
+        })
+        return dataset
+
+
+LANG_TO_ANSWER_PREFIX = {
+    'en': 'Answer',
+    'bn': 'উত্তর',
+    'de': 'Antwort',
+    'es': 'Respuesta',
+    'fr': 'Réponse',
+    'ja': '答え',
+    'ru': 'Ответ',
+    'sw': 'Jibu',
+    'te': 'సమాధానం',
+    'th': 'คำตอบ',
+    'zh': '答案',
+}
+
+
+def mgsm_postprocess(text: str, lang: str) -> str:
+    answer_prefix = LANG_TO_ANSWER_PREFIX[lang]
+    if answer_prefix not in text:
+        return ''
+    answer_text = text.split(answer_prefix)[-1].strip()
+    numbers = re.findall(r'\d+\.?\d*', answer_text.replace(',', ''))
+    return numbers[-1].rstrip('.') if numbers else ''
+
+
+class MGSM_Evaluator(BaseEvaluator):
+
+    def score(self, predictions, references):
+        assert len(predictions) == len(references)
+
+        num_correct, total = 0, 0
+        details = {}
+        for index, (references_answer, predictions_answer) in enumerate(
+                zip(references, predictions)):
+            if references_answer == predictions_answer:
+                is_correct = True
+            else:
+                is_correct = False
+
+            num_correct += is_correct
+            total += 1
+            details[str(index)] = {
+                'references': references_answer,
+                'predictions': predictions_answer,
+                'correct': is_correct,
+            }
+
+        accuracy = num_correct / total * 100
+        final_result = {'accuracy': accuracy, 'details': details}
+        return final_result