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README.md

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 ---
+pretty_name: WikiHow-ES
 license: cc-by-nc-sa-3.0
+size_categories: 1K<n<10K
+language: es
+multilinguality: monolingual
+task_categories:
+- text-classification
+- question-answering
+- conversational
+- summarization
+tags:
+- Spanish
+- WikiHow
+- Wiki Articles
+- Tutorials
+- Step-By-Step
+- Instruction Tuning
 ---
+
+### Dataset Summary
+
+Articles retrieved from the [Spanish WikiHow website](https://es.wikihow.com) on September 2023.
+
+Each article contains a tutorial about a specific topic. The format is always a "How to" question 
+followed by a detailed step-by-step explanation. In some cases, the response includes several methods. 
+
+The main idea is to use this data for instruction tuning of Spanish LLMs, but given its nature it 
+could also be used for other tasks such as text classification or summarization.
+
+### Languages
+
+- Spanish (ES)
+
+### Usage
+
+To load the full dataset:
+```python
+from datasets import load_dataset
+
+all_articles = load_dataset("mapama247/wikihow_es")
+print(all_articles.num_rows) # output: {'train': 7380}
+```
+
+To load only examples from a specific category:
+```python
+from datasets import load_dataset
+
+sports_articles = load_dataset("mapama247/wikihow_es", "deportes")
+print(sports_articles.num_rows) # output: {'train': 201}
+```
+
+List of available categories, with the repective number of examples:
+```
+computadoras-y-electrónica       821
+salud                            804
+pasatiempos                      729
+cuidado-y-estilo-personal        724
+carreras-y-educación             564
+en-la-casa-y-el-jardín           496
+finanzas-y-negocios              459
+comida-y-diversión               454
+relaciones                       388
+mascotas-y-animales              338
+filosofía-y-religión             264
+arte-y-entretenimiento           254
+en-el-trabajo                    211
+adolescentes                     201
+deportes                         201
+vida-familiar                    147
+viajes                           139
+automóviles-y-otros-vehículos    100
+días-de-fiesta-y-tradiciones      86
+```
+
+### Supported Tasks
+
+This dataset can be used to train a model for...
+
+- `instruction-tuning`
+- `text-classification`
+- `question-answering`
+- `conversational`
+- `summarization`
+
+## Dataset Structure
+
+### Data Instances
+
+```python
+{
+    'category': str,
+    'question': str,
+    'introduction': str,
+    'answers': List[str],
+    'short_answers': List[str],
+    'url': str,
+    'num_answers': int,
+    'num_refs': int,
+    'expert_author': bool,
+}
+```
+
+### Data Fields
+
+- `category`: The category (from [this list](https://es.wikihow.com/Especial:CategoryListing)) to which the example belongs to.
+- `label`: Numerical representation of the category, for text classification purposes.
+- `question`: The article's title, which always starts with "¿Cómo ...".
+- `introduction`: Introductory text that precedes the step-by-step explanation.
+- `answers`: List of complete answers, with the full explanation of each step.
+- `short_answers`: List of shorter answers that only contain one-sentence steps.
+- `num_answers`: The number of alternative answers provided (e.g. length of `answers`).
+- `num_ref`: Number of references provided in the article.
+- `expert_authors`: Whether the article's author claims to be an expert on the topic or not.
+- `url`: The URL address of the original article.
+
+### Data Splits
+
+There is only one split (`train`) that contains a total of 7,380 examples.
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created for language model alignment to end tasks and user preferences.
+
+### Source Data
+
+How-To questions with detailed step-by-step answers, retrieved from the WikiHow website.
+
+#### Data Collection and Normalization
+
+All articles available in September 2023 were extracted, no filters applied.
+
+Along with the article's content, some metadata was retrieved as well.
+
+#### Source language producers
+
+WikiHow users. All the content is human-generated.
+
+### Personal and Sensitive Information
+
+The data does not include personal or sensitive information.
+
+## Considerations
+
+### Social Impact
+
+The Spanish community can benefit from the high-quality data provided by this dataset.
+
+### Bias
+
+No post-processing steps have been applied to mitigate potential social biases.
+
+## Additional Information
+
+### Curators
+
+Marc Pàmes @ Barcelona Supercomputing Center.
+
+### License
+
+This dataset is licensed under a **Creative Commons CC BY-NC-SA 3.0** license.
+
+Quote from [WikiHow's Terms of Use](https://www.wikihow.com/wikiHow:Terms-of-Use):
+
+> All text posted by Users to the Service is sub-licensed by wikiHow to other Users under a Creative Commons license as 
+> provided herein. The Creative Commons license allows such user generated text content to be used freely for personal, 
+> non-commercial purposes, so long as it is used and attributed to the original author as specified under the terms of 
+> the license. Allowing free republication of our articles helps wikiHow achieve its mission by providing instruction 
+> on solving the problems of everyday life to more people for free. In order to support this goal, wikiHow hereby grants 
+> each User of the Service a license to all text content that Users contribute to the Service under the terms and 
+> conditions of a Creative Commons CC BY-NC-SA 3.0 License. Please be sure to read the terms of the license carefully. 
+> You continue to own all right, title, and interest in and to your User Content, and you are free to distribute it as 
+> you wish, whether for commercial or non-commercial purposes.

wikihow_es.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d857896c0bff56784249ac4e8e5fcc4ed007739ff74dcb1dce556a3ca8734c4
+size 66088317

wikihow_es.py

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+import os
+import re
+import csv
+import json
+import datasets
+
+_DESCRIPTION = "Spanish articles from WikiHow"
+_HOMEPAGE = "https://www.wikihow.com"
+_LICENSE  = "CC BY-NC-SA 3.0"
+_VERSION = "1.1.0"
+
+_DATAPATH = "wikihow_es.jsonl"
+
+_CATEGORIES = [
+    "salud",
+    "viajes",
+    "deportes",
+    "relaciones",
+    "pasatiempos",
+    "adolescentes",
+    "vida-familiar",
+    "en-el-trabajo",
+    "comida-y-diversión",
+    "finanzas-y-negocios",
+    "mascotas-y-animales",
+    "carreras-y-educación",
+    "filosofía-y-religión",
+    "arte-y-entretenimiento",
+    "en-la-casa-y-el-jardín",
+    "cuidado-y-estilo-personal",
+    "computadoras-y-electrónica",
+    "días-de-fiesta-y-tradiciones",
+    "automóviles-y-otros-vehículos",
+]
+
+def format_methods(methods, short=False):
+    EOL = "\n" if short else "\n\n"
+    formatted = []
+    for method in methods:
+        if method["title"].lower() != "pasos":
+            content = f"Método {method['number']}: {method['title']}{EOL}"
+        else:
+            content = f"Sigue los siguientes pasos:{EOL}"
+        for step in method["steps"]:
+            step_content = re.sub(r"\n+", "\n", step).strip()
+            if short:
+                step_content = step_content.split("\n")[0]
+            content += step_content + EOL
+        formatted.append(content.strip())
+    return formatted
+
+
+class WikiHowEs(datasets.GeneratorBasedBuilder):
+    """ WikiHowEs: Collection of Spanish tutorials. """
+
+    VERSION = datasets.Version(_VERSION)
+
+    DEFAULT_CONFIG_NAME = "all"
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name="all", version=VERSION, description="All articles from WikiHow-ES.")]
+    for _CAT in _CATEGORIES:
+        BUILDER_CONFIGS.append(
+            datasets.BuilderConfig(name=_CAT, version=VERSION, description=f"Articles from the category {_CAT}")
+        )
+
+    @staticmethod
+    def _info():
+        features = datasets.Features(
+            {
+                "category": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "introduction": datasets.Value("string"),
+                "answers": datasets.features.Sequence(datasets.Value("string")),
+                "short_answers": datasets.features.Sequence(datasets.Value("string")),
+                "url": datasets.Value("string"),
+                "num_answers": datasets.Value("int32"),
+                "num_refs": datasets.Value("int32"),
+                "expert_author": datasets.Value("bool"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+    @staticmethod
+    def _split_generators(dl_manager):
+        data_dir = dl_manager.download_and_extract(_DATAPATH)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name in ["all", data["category"].lower()]:
+                    yield key, {
+                        "category": data["category"],
+                        "question": f"¿{data['title']}?",
+                        "introduction": data["intro"],
+                        "answers": format_methods(data["methods"], short=False),
+                        "short_answers": format_methods(data["methods"], short=True),
+                        "num_answers": data["num_methods"],
+                        "num_refs": data["num_refs"],
+                        "expert_author": data["expert_author"],
+                        "url": data["url"],
+                    }