Closes #92 | Create Jail break data loader (#390)

* feat: jailbreak dataloader

* fix: minor errors

* refactor: styling

* refactor: remove main entry

* refactor: class name

* refactor: remove unused loop

* fix: separate text column into different subsets

* Create __init__.py

seacrowd/sea_datasets/xl_jailbreak/xl_jailbreak.py

@@ -0,0 +1,195 @@
+# 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 dataclasses import dataclass
+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 TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = """\
+@misc{deng2023multilingual,
+title={Multilingual Jailbreak Challenges in Large Language Models},
+author={Yue Deng and Wenxuan Zhang and Sinno Jialin Pan and Lidong Bing},
+year={2023},
+eprint={2310.06474},
+archivePrefix={arXiv},
+primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "xl_jailbreak"
+
+_DESCRIPTION = """\
+This dataset contains the data for the paper "Multilingual Jailbreak Challenges in Large Language Models".
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/DAMO-NLP-SG/MultiJail"
+
+_LANGUAGES = ["jav", "vie", "tha"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.MIT.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {"train": "https://huggingface.co/api/datasets/DAMO-NLP-SG/MultiJail/parquet/default/train/0.parquet"},
+}
+
+_SUPPORTED_TASKS = [Tasks.PROMPTING]
+_SUPPORTED_SCHEMA_STRINGS = [f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+_LANGUAGE_TO_COLUMN = {
+    "vie": "vi",
+    "tha": "th",
+    "jav": "jv",
+}
+
+
+@dataclass
+class XlJailbreakSeacrowdConfig(SEACrowdConfig):
+    """BuilderConfig for Nusantara."""
+
+    language: str = None
+
+
+class XlJailbreak(datasets.GeneratorBasedBuilder):
+    """This dataset contains the data for the paper "Multilingual Jailbreak Challenges in Large Language Models"."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+
+    for language in _LANGUAGES:
+        subset_id = language
+
+        BUILDER_CONFIGS.append(
+            XlJailbreakSeacrowdConfig(
+                name=f"{subset_id}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} {language} schema",
+                schema="source",
+                subset_id=subset_id,
+                language=language,
+            )
+        )
+
+    seacrowd_schema_config: list[SEACrowdConfig] = []
+
+    for seacrowd_schema in _SUPPORTED_SCHEMA_STRINGS:
+        for language in _LANGUAGES:
+            subset_id = language
+
+            seacrowd_schema_config.append(
+                XlJailbreakSeacrowdConfig(
+                    name=f"{subset_id}_{seacrowd_schema}",
+                    version=SEACROWD_VERSION,
+                    description=f"{_DATASETNAME} {seacrowd_schema} schema",
+                    schema=f"{seacrowd_schema}",
+                    subset_id=subset_id,
+                    language=language,
+                )
+            )
+
+    BUILDER_CONFIGS.extend(seacrowd_schema_config)
+
+    DEFAULT_CONFIG_NAME = f"{_LANGUAGES[0]}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value(dtype="int64"),
+                    "source": datasets.Value(dtype="string"),
+                    "tags": datasets.Value(dtype="string"),
+                    "en": datasets.Value(dtype="string"),
+                    "zh": datasets.Value(dtype="string"),
+                    "it": datasets.Value(dtype="string"),
+                    "vi": datasets.Value(dtype="string"),
+                    "ar": datasets.Value(dtype="string"),
+                    "ko": datasets.Value(dtype="string"),
+                    "th": datasets.Value(dtype="string"),
+                    "bn": datasets.Value(dtype="string"),
+                    "sw": datasets.Value(dtype="string"),
+                    "jv": datasets.Value(dtype="string"),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.PROMPTING]).lower()}":
+            features = schemas.ssp_features
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+        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."""
+
+        urls = _URLS[_DATASETNAME]
+        train_path = dl_manager.download_and_extract(urls["train"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_path,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+
+            df = pd.read_parquet(filepath)
+
+            for index, row in df.iterrows():
+                yield index, row.to_dict()
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.PROMPTING]).lower()}":
+            df = pd.read_parquet(filepath)
+
+            # Apply the function to each row and create a new column with the JSON string
+            df["text"] = df[_LANGUAGE_TO_COLUMN[self.config.language]]
+
+            df = df[["id", "text"]]
+
+            print(df)
+
+            for index, row in df.iterrows():
+                yield index, row.to_dict()
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")