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balita_nlp.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.
+
+import os
+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 = """\
+@inproceedings{bunagtransformer,
+  author={Bunag, Kenrick Lance T and Esquivel, Rosanna A}
+  title={Transformer-Based Conditional Language Models to Generate Filipino News Articles},
+  year = {2023},
+  publisher = {IEOM Society International},
+  url = {https://ieomsociety.org/proceedings/2023manila/595.pdf},
+  booktitle = {Proceedings of the International Conference on Industrial Engineering and Operations Management},
+  pages = {2231–2237},
+  numpages = {7},
+  location = {Manila, Philippines},
+}
+"""
+
+_DATASETNAME = "balita_nlp"
+
+_DESCRIPTION = """\
+BalitaNLP is a dataset for image-conditional language generation and text-conditional image generation. It consists of 300k Filipino news
+articles and images gathered from Filipino news outlets. News articles are categorized into five possible classes: News, Sports, Entertainment,
+Crime, and Other. Some articles were removed from the SEACrowd `imtext` schema, as their corresponding image files do not exist:
+- `train` split (262480 total articles): from the original 281403 articles, 18923 (~6.72%) had missing images
+- `test` split (32821 total articles): from the original 35177 articles, 2356 (~6.70%) had missing images
+- `validation` split (32806 total articles): from the original 35175 articles, 2369 (~6.73%) had missing images
+"""
+
+_HOMEPAGE = "https://github.com/KenrickLance/BalitaNLP-Dataset"
+
+_LANGUAGES = ["fil"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    "text": "https://storage.googleapis.com/public-kenricklancebunag/BalitaNLP/2022/BalitaNLP-Dataset.zip",
+    "images": {
+        "part1": "https://storage.googleapis.com/public-kenricklancebunag/BalitaNLP/2022/BalitaNLP-images_1.zip",
+        "part2": "https://storage.googleapis.com/public-kenricklancebunag/BalitaNLP/2022/BalitaNLP-images_2.zip",
+        "part3": "https://storage.googleapis.com/public-kenricklancebunag/BalitaNLP/2022/BalitaNLP-images_3.zip",
+        "part4": "https://storage.googleapis.com/public-kenricklancebunag/BalitaNLP/2022/BalitaNLP-images_4.zip",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class BalitaNLPDataset(datasets.GeneratorBasedBuilder):
+    """
+    BalitaNLP is an image-text dataset from https://github.com/KenrickLance/BalitaNLP-Dataset.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_imtext",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_imtext",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "body": datasets.Sequence(datasets.Value("string")),
+                    "title": datasets.Value("string"),
+                    "website": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "author": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "img_url": datasets.Value("string"),
+                    "img_path": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_imtext":
+            features = schemas.image_text_features()
+            features["metadata"] = {
+                "context": datasets.Value("string"),
+                "author": datasets.Value("string"),
+                "category": datasets.Value("string"),
+                "date": datasets.Value("string"),
+                "img_url": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "website": datasets.Value("string"),
+            }
+        else:
+            raise ValueError(f"Invalid schema: '{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.
+        """
+
+        text_path = dl_manager.download_and_extract(_URLS["text"])
+        img_paths = dl_manager.download_and_extract([v for k, v in _URLS["images"].items()])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "text_path": os.path.join(text_path, "train.json"),
+                    "img_paths": img_paths,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "text_path": os.path.join(text_path, "test.json"),
+                    "img_paths": img_paths,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "text_path": os.path.join(text_path, "validation.json"),
+                    "img_paths": img_paths,
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, text_path: Path, img_paths: Path, split: str) -> Tuple[int, Dict]:
+        """
+        Yields examples as (key, example) tuples.
+        """
+        text_data = pd.read_json(text_path)
+        data = text_data.to_records()
+
+        for idx, row in enumerate(data):
+
+            # Search for path of image file
+            img_path = ""
+            for idx_subpath, img_subpath in enumerate(img_paths):
+                candidate_filepath = os.path.join(img_subpath, "part" + str(idx_subpath + 1), row["img_path"])
+                if os.path.isfile(candidate_filepath):
+                    img_path = candidate_filepath
+
+            if self.config.schema == "source":
+                x = {
+                    "body": row["body"],
+                    "title": row["title"],
+                    "website": row["website"],
+                    "category": row["category"],
+                    "date": row["date"],
+                    "author": row["author"],
+                    "url": row["url"],
+                    "img_url": row["img_url"],
+                    "img_path": img_path,
+                }
+                yield idx, x
+
+            elif self.config.schema == "seacrowd_imtext":
+
+                # Remove examples with no existing image path
+                if img_path == "":
+                    continue
+
+                x = {
+                    "id": idx,
+                    "image_paths": [img_path],
+                    "texts": row["title"],
+                    "metadata": {
+                        "context": row["body"],
+                        "author": row["author"],
+                        "category": row["category"],
+                        "date": row["date"],
+                        "img_url": row["img_url"],
+                        "url": row["url"],
+                        "website": row["website"],
+                    },
+                }
+                yield idx, x
+
+            else:
+                raise ValueError(f"Invalid schema: '{self.config.schema}'")