upload dataset

README.md

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+---
+languages:
+- ca
+---
+# ViquiQuAD, An extractive QA dataset for catalan, from the Wikipedia
+
+## BibTeX  citation
+
+If you use any of these resources (datasets or models) in your work, please cite our latest paper:
+
+```bibtex
+@inproceedings{armengol-estape-etal-2021-multilingual,
+    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
+    author = "Armengol-Estap{\'e}, Jordi  and
+      Carrino, Casimiro Pio  and
+      Rodriguez-Penagos, Carlos  and
+      de Gibert Bonet, Ona  and
+      Armentano-Oller, Carme  and
+      Gonzalez-Agirre, Aitor  and
+      Melero, Maite  and
+      Villegas, Marta",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.437",
+    doi = "10.18653/v1/2021.findings-acl.437",
+    pages = "4933--4946",
+}
+```
+
+
+# Digital Object Identifier (DOI) and access to dataset files
+
+https://doi.org/10.5281/zenodo.4562345
+
+
+## Introduction
+
+This dataset contains 3111 contexts extracted from a set of 597 high quality original (no translations) articles in the Catalan Wikipedia "Viquipèdia" (ca.wikipedia.org), and 1 to 5 questions with their answer for each fragment.
+
+Viquipedia articles are used under [CC-by-sa] (https://creativecommons.org/licenses/by-sa/3.0/legalcode) licence. 
+
+This dataset can be used to fine-tune and evaluate extractive-QA and Language Models. It is part of the Catalan Language Understanding Benchmark (CLUB) as presented in: 
+
+Armengol-Estapé J., Carrino CP., Rodriguez-Penagos C., de Gibert Bonet O., Armentano-Oller C., Gonzalez-Agirre A., Melero M. and Villegas M.,Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan". Findings of ACL 2021 (ACL-IJCNLP 2021).
+
+### Supported Tasks and Leaderboards
+
+Extractive-QA, Language Model
+
+### Languages
+
+CA- Catalan
+
+### Directory structure
+
+* README
+* dev.json
+* test.json
+* train.json
+* viquiquad.py
+
+## Dataset Structure
+
+### Data Instances
+
+json files
+
+### Data Fields
+
+Follows ((Rajpurkar, Pranav et al., 2016) for squad v1 datasets. (see below for full reference)
+
+### Example:
+<pre>
+{
+  "data": [
+    {
+      "title": "Frederick W. Mote",
+      "paragraphs": [
+        {
+          "context": "L'historiador Frederick W. Mote va escriure que l'ús del terme \\\\\\\\\\\\\\\\"classes socials\\\\\\\\\\\\\\\\" per a aquest sistema era enganyós i que la posició de les persones dins del sistema de quatre classes no era una indicació del seu poder social i riquesa reals, sinó que només implicava \\\\\\\\\\\\\\\\"graus de privilegi\\\\\\\\\\\\\\\\" als quals tenien dret institucionalment i legalment, de manera que la posició d'una persona dins de les classes no era una garantia de la seva posició, ja que hi havia xinesos rics i amb bona reputació social, però alhora hi havia menys mongols i semu rics que mongols i semu que vivien en la pobresa i eren maltractats.",
+          "qas": [
+            {
+              "answers": [
+                {
+                  "text": "Frederick W. Mote",
+                  "answer_start": 14
+                }
+              ],
+              "id": "5728848cff5b5019007da298",
+              "question": "Qui creia que el sistema de classes socials de Yuan no s’hauria d’anomenar classes socials?"
+            },
+            ...
+          ]
+        }
+      ]
+    }, 
+    ...
+   ]
+} 
+
+</pre>
+
+### Data Splits
+
+train.development,test
+
+## Content analysis
+
+### Number of articles, paragraphs and questions
+
+* Number of articles: 597
+* Number of contexts: 3111
+* Number of questions: 15153
+* Questions/context: 4.87
+* Number of sentences in contexts: 15100
+* Sentences/context: 4.85
+
+### Number of tokens
+
+* tokens in context: 469335
+* tokens/context 150.86
+* tokens in questions: 145249
+* tokens/questions: 9.58
+* tokens in answers: 63246
+* tokens/answers: 4.17
+
+### Lexical variation
+
+After filtering (tokenization, stopwords, punctuation, case), 83,88% of the words in the question can be found in the Context
+
+### Question type 
+
+| Question | Count | % |
+|--------|-----|------|
+| què |  4220 | 27.85 % |
+| qui |  2239 | 14.78 % |
+| com |  1964 | 12.96 % |
+| quan |  1133 | 7.48 % |
+| on |  1580 | 10.43 % |
+| quant |  925 | 6.1 % |
+| quin |  3399 | 22.43 % |
+| no question mark | 21 | 0.14 % |
+
+### Question-answer relationships
+
+From 100 randomly selected samples:
+
+* Lexical variation: 33.0%
+* World knowledge: 16.0%
+* Syntactic variation: 35.0%
+* Multiple sentence: 17.0%
+
+## Dataset Creation
+
+### Methodology
+
+From a set of high quality, non-translation, articles in the Catalan Wikipedia (ca.wikipedia.org), 597 were randomly chosen, and from them 3111, 5-8 sentence contexts were extracted. We commissioned creation of between 1 and 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 [Rajpurkar, Pranav et al. “SQuAD: 100, 000+ Questions for Machine Comprehension of Text.” EMNLP (2016)], (http://arxiv.org/abs/1606.05250). In total, 15153 pairs of a question and an extracted fragment that contains the answer were created.
+
+### Curation Rationale
+
+For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines. 
+
+### Source Data
+
+- https://ca.wikipedia.org
+
+#### Initial Data Collection and Normalization
+
+The source data are scraped articles from the Catalan wikipedia site (https://ca.wikipedia.org). 
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+We commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 (Rajpurkar, Pranav et al. “SQuAD: 100, 000+ Questions for Machine Comprehension of Text.” EMNLP (2016)), http://arxiv.org/abs/1606.05250.
+
+#### Who are the annotators?
+
+Native language speakers.
+
+### Dataset Curators
+
+Carlos Rodríguez and Carme Armentano, from BSC-CNS
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+
+## Contact
+
+Carlos Rodríguez-Penagos (carlos.rodriguez1@bsc.es) and Carme Armentano-Oller (carme.armentano@bsc.es)
+
+
+## License
+
+<a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/"><img alt="Attribution-ShareAlike 4.0 International License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>.

viquiquad.py

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+# Loading script for the ViquiQuAD dataset.
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+               Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021). 
+               ViquiQuAD: an extractive QA dataset from Catalan Wikipedia (Version ViquiQuad_v.1.0.1) 
+               [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4761412
+            """
+
+_DESCRIPTION = """
+                  ViquiQuAD: an extractive QA dataset from Catalan Wikipedia.
+                  This dataset contains 3111 contexts extracted from a set of 597 high quality original (no translations) 
+                  articles in the Catalan Wikipedia "Viquipèdia" (ca.wikipedia.org), and 1 to 5 questions with their
+                  answer for each fragment. Viquipedia articles are used under CC-by-sa licence. 
+                  This dataset can be used to build extractive-QA and Language Models.
+                  Funded by the Generalitat de Catalunya, Departament de Polítiques Digitals i Administració Pública (AINA),
+                  MT4ALL and Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
+               """
+
+_HOMEPAGE = """https://zenodo.org/record/4562345#.YK41aqGxWUk"""
+
+_URL = "https://huggingface.co/datasets/bsc/viquiquad/resolve/main/"
+_TRAINING_FILE = "train.json"
+_DEV_FILE = "dev.json"
+_TEST_FILE = "test.json"
+
+
+class ViquiQuADConfig(datasets.BuilderConfig):
+    """ Builder config for the ViquiQuAD dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for ViquiQuAD.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(ViquiQuADConfig, self).__init__(**kwargs)
+
+
+class ViquiQuAD(datasets.GeneratorBasedBuilder):
+    """ViquiQuAD Dataset."""
+
+    BUILDER_CONFIGS = [
+        ViquiQuADConfig(
+            name="ViquiQuAD",
+            version=datasets.Version("1.0.1"),
+            description="ViquiQuAD dataset",
+        ),
+    ]
+
+    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":[
+
+                        {
+
+                            "text": datasets.Value("string"),
+
+                            "answer_start": datasets.Value("int32"),
+
+                        }
+
+                    ]
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        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"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            viquiquad = json.load(f, encoding="utf-8")
+            for article in viquiquad["data"]:
+                title = article.get("title", "").strip()
+                for paragraph in article["paragraphs"]:
+                    context = paragraph["context"].strip()
+                    for qa in paragraph["qas"]:
+                        question = qa["question"].strip()
+                        id_ = qa["id"]
+
+                    #    answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                    #    answers = [answer["text"].strip() for answer in qa["answers"]]
+                    
+                        text = qa["answers"][0]["text"]
+                        answer_start = qa["answers"][0]["answer_start"]
+
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": [{"text": text, "answer_start": answer_start}]
+                        }