Support latest HF datasets loading changes

README.md

@@ -28,9 +28,9 @@ configs:
   - config_name: default
     data_files:
       - split: train
-        path: "data/TiQuAD-v1-train-*"
+        path: "train.parquet"
       - split: validation
-        path: "data/TiQuAD-v1-dev-*"
+        path: "dev.parquet"
 ---
 
 # TiQuAD: Tigrinya Question Answering Dataset
@@ -74,22 +74,30 @@ The dataset was constructed through a careful multi-stage process:
 
 ## How to Load TiQuAD
 
+Install the `datasets` library installed by running `pip install -U datasets` in the terminal.
+
+> Make sure the latest `datasets` library is installed as older versions may not properly load the data.
+
+Then pull and load the dataset using Python, as follows:
+
 ```python
 from datasets import load_dataset
 
 # Load the dataset
-tiquad = load_dataset("fgaim/tiquad", trust_remote_code=True)
+tiquad = load_dataset("fgaim/tiquad")
 print(tiquad)
 ```
 
+That will print the dataset features:
+
 ```python
 DatasetDict({
     train: Dataset({
-        features: ['id', 'title', 'context', 'question', 'answers'],
+        features: ['id', 'question', 'context', 'answers', 'article_title', 'context_id'],
         num_rows: 4452
-    }),
+    })
     validation: Dataset({
-        features: ['id', 'title', 'context', 'question', 'answers'],
+        features: ['id', 'question', 'context', 'answers', 'article_title', 'context_id'],
         num_rows: 934
     })
 })
@@ -97,13 +105,14 @@ DatasetDict({
 
 ### Data Fields
 
-- **`id`**: Unique identifier for each question-answer pair
-- **`title`**: Title of the source news article
+- **`id`**: Unique identifier for each question
+- **`question`**: The question to be answered (in Tigrinya)
 - **`context`**: The paragraph containing the answer (in Tigrinya)
-- **`question`**: The question to be answered (in Tigrinya)  
-- **`answers`**: Dictionary containing:
-  - `text`: List of answer strings (multiple annotations for validation/test)
-  - `answer_start`: List of positions where answers begin in the context
+- **`answers`**: A list of dictionaries of candidate answers, each entry containing:
+  - `text`: An answer string (training data has one answer per question)
+  - `answer_start`: A starting position of answer string in the context
+- **`article_title`**: Title of the source article
+- **`context_id`**: Unique identifier of the context in the data split
 
 ### Sample Entry
 

data/TiQuAD-v1-dev.json → dev.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9091f3cb889b774778bcbef94543b5d58ccf8e33a5491e0f03b2c8ff7ef7dd08
-size 1131964
+oid sha256:10488d8c729d3bea063c08888a8751391f77c0c5305d8fd786aa8eb32dd6d117
+size 160568

tiquad.py

@@ -1,115 +0,0 @@
-"""TiQuAD: Tigrinya Question-Answering Dataset."""
-
-import json
-
-import datasets
-
-
-_HOMEPAGE = "https://github.com/fgaim/tiquad"
-
-_DESCRIPTION = """\
-TiQuAD is a manually annotated extractive Question-Answering (QA) dataset for the Tigrinya language.
-"""
-
-_CITATION = """\
-@inproceedings{gaim-etal-2023-tiquad,
-    title = "{Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya}",
-    author = "Fitsum Gaim and Wonsuk Yang and Hancheol Park and Jong C. Park",
-    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
-    month = jul,
-    year = "2023",
-    address = "Toronto, Canada",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2023.acl-long.661",
-    pages = "11857--11870",
-}
-"""
-
-_LICENSE = "Creative Commons Attribution-ShareAlike 4.0"
-
-_DATA_PATHS = {
-    "train": "data/TiQuAD-v1-train.json",
-    "dev": "data/TiQuAD-v1-dev.json",
-}
-
-
-class TiQuADConfig(datasets.BuilderConfig):
-    """BuilderConfig for TiQuAD"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for TiQuAD.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(TiQuADConfig, self).__init__(**kwargs)
-
-
-class TiQuAD(datasets.GeneratorBasedBuilder):
-    """TiQuAD dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        downloaded_files = dl_manager.download_and_extract(_DATA_PATHS)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": downloaded_files["train"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": downloaded_files["dev"]},
-            )
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields TiQuAD examples."""
-        with open(filepath, encoding="utf-8") as fin:
-            _quad = json.load(fin)
-            for example in _quad["data"]:
-                title = example.get("title", "")
-                for paragraph in example["paragraphs"]:
-                    context = paragraph["context"]
-                    for qa in paragraph["qas"]:
-                        _id = qa["id"]
-                        answers = [
-                            {
-                                "text": answer["text"],
-                                "answer_start": answer["answer_start"],
-                            }
-                            for answer in qa["answers"]
-                        ]
-                        yield (
-                            _id,
-                            {
-                                "id": _id,
-                                "title": title,
-                                "context": context,
-                                "question": qa["question"],
-                                "answers": answers,
-                            },
-                        )

data/TiQuAD-v1-train.json → train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:34543746774ee6371ee332b051dbfaea58b0cd78b827e5e4daf2795c316bf55d
-size 3099483
+oid sha256:def707dcc1bb14400a05b6e4e0acdec07f9d32a81433a5c04bd0c0de894679b2
+size 704162