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seaeval.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@article{SeaEval2023,
+  title={SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning},
+  author={Wang, Bin and Liu, Zhengyuan and Huang, Xin and Jiao, Fangkai and Ding, Yang and Aw, Ai Ti and Chen, Nancy F.},
+  journal={arXiv preprint arXiv:2309.04766},
+  year={2023},
+  url={https://github.com/SeaEval/SeaEval}
+}
+"""
+
+_DATASETNAME = "seaeval"
+
+_DESCRIPTION = """\
+SeaEval is a benchmark toolkit for evaluating multilingual LLMs. The benchmark contains 28 datasets,
+covering 7 languages. It contains 2 datasets for cross-lingual consistency, each containing parallel
+questions for the 7 represented languages. It alsoc ontains 4 datasets for cultural reasoning
+(multiple choice Q&A) that are in English but focused on regions including Singapore and Philipines.
+
+This dataloader provides examples for Indonesia, Vietnamese, Malay, and Filipino.
+"""
+
+_HOMEPAGE = "https://github.com/SeaEval/SeaEval"
+
+_LANGUAGES = {"ind": "Indonesian", "vie": "Vietnamese", "zlm": "Malay", "fil": "Filipino"}
+
+_LICENSE = Licenses.CC_BY_NC_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "cross_mmlu": "https://huggingface.co/datasets/SeaEval/SeaEval_datasets/raw/main/cross_mmlu.json",
+    "cross_logiqa": "https://huggingface.co/datasets/SeaEval/SeaEval_datasets/raw/main/cross_logiqa.json",
+    "sg_eval": "https://huggingface.co/datasets/SeaEval/SeaEval_datasets/raw/main/sg_eval.json",
+    "ph_eval": "https://huggingface.co/datasets/SeaEval/SeaEval_datasets/raw/main/ph_eval.json",
+}
+
+_SUPPORTED_TASKS = [Tasks.COMMONSENSE_REASONING, Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class SeaEvalDataset(datasets.GeneratorBasedBuilder):
+    """
+    SeaEval is a benchmark for evaluating multilingual LLMs from https://github.com/SeaEval/SeaEval.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    LANGUAGES_EXCHANGED = dict((v, k) for k, v in _LANGUAGES.items())
+    SUBSETS_CROSS_MMLU = ["cross_mmlu_" + lang for lang in _LANGUAGES.keys()]
+    SUBSETS_CROSS_LOGIQA = ["cross_logiqa_" + lang for lang in _LANGUAGES.keys()]
+    SUBSETS = SUBSETS_CROSS_MMLU + SUBSETS_CROSS_LOGIQA + ["sg_eval_eng", "ph_eval_eng"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME}_{subset} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ]
+
+    BUILDER_CONFIGS += [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_seacrowd_qa",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME}_{subset} SEACrowd schema",
+            schema="seacrowd_qa",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source" and self.config.subset_id not in ["cross_logiqa", "ph_eval"]:
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "choices": datasets.Sequence(datasets.Value("string")),
+                    "answer": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "source" and self.config.subset_id == "cross_logiqa":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "choices": datasets.Sequence(datasets.Value("string")),
+                    "answer": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "source" and self.config.subset_id == "ph_eval":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "choices": datasets.Sequence(datasets.Value("string")),
+                    "answer": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+        else:
+            raise ValueError(f"Unexpected schema received! {self.config.schema}")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """
+        Returns SplitGenerators.
+        """
+
+        data = {key: dl_manager.download_and_extract(value) for key, value in _URLS.items()}
+
+        paths = {}
+        file = self.config.subset_id.split("_")
+        file = "_".join(file[1:3])
+        paths[self.config.subset_id] = data[file]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "paths": paths,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, paths: Path, split: str) -> Tuple[int, Dict]:
+        """
+        Yields examples as (key, example) tuples.
+        """
+
+        language = self.config.subset_id.split("_")[3]
+        examples = None
+
+        for key, path in paths.items():
+            if "cross" in key:
+                data = pd.read_json(path).rename(columns=self.LANGUAGES_EXCHANGED)
+                data = pd.melt(data, id_vars=["id"], value_vars=_LANGUAGES.keys(), var_name="language")
+                data_flattened = pd.json_normalize(data["value"])
+                data_merged = pd.merge(data, data_flattened, left_index=True, right_index=True)
+                data_filtered = data_merged[data_merged["language"] == language].drop(columns=["value", "language"])
+                examples = data_filtered.to_records()
+            elif "eval" in key:
+                data = pd.read_json(path)
+                examples = data.to_records()
+
+        idx = 0
+        if self.config.schema == "source" and self.config.subset_id not in ["cross_logiqa", "ph_eval"]:
+            for row in examples:
+                x = {
+                    "id": row["id"],
+                    "question": row["question"],
+                    "choices": row["choices"],
+                    "answer": row["answer"],
+                }
+                yield idx, x
+                idx += 1
+        elif self.config.schema == "source" and self.config.subset_id == "cross_logiqa":
+            for row in examples:
+                x = {
+                    "id": row["id"],
+                    "question": row["question"],
+                    "context": row["context"] if "context" in row else None,
+                    "choices": row["choices"],
+                    "answer": row["answer"],
+                }
+                yield idx, x
+                idx += 1
+        elif self.config.schema == "source" and self.config.subset_id == "ph_eval":
+            for row in examples:
+                x = {
+                    "id": row["id"],
+                    "question": row["question"],
+                    "choices": row["choices"],
+                    "answer": row["answer"],
+                    "category": row["category"] if "category" in row else None,
+                }
+                yield idx, x
+                idx += 1
+        elif self.config.schema == "seacrowd_qa":
+            for row in examples:
+                x = {
+                    "id": idx,
+                    "question_id": row["id"],
+                    "document_id": row["id"],
+                    "question": row["question"],
+                    "type": "multiple_choice",
+                    "choices": row["choices"],
+                    "context": row["context"] if "context" in row else None,
+                    "answer": [row["answer"]],
+                    "meta": {},
+                }
+                yield idx, x
+                idx += 1
+        else:
+            raise ValueError(f"Invalid schema: {self.config.schema}")