metadata

annotations_creators:
  - SLPL
language_creators:
  - SLPL
language:
  - fa
license:
  - mit
multilinguality:
  - monolingual
size_categories:
  - 200M<n<300M
source_datasets:
  - commoncrawl
task_categories:
  - language-modeling
  - masked-language-modeling
task_ids:
  - language-modeling
  - masked-language-modeling
pretty_name: naab (A ready-to-use plug-and-play corpus in Farsi)

naab: A ready-to-use plug-and-play corpus in Farsi

Dataset Card Creation Guide

Dataset Description

Homepage: Sharif Speech and Language Processing Lab
Repository: If the dataset is hosted on github or has a github homepage, add URL here
Paper: If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)
Leaderboard: If the dataset supports an active leaderboard, add link here
Point of Contact: If known, name and email of at least one person the reader can contact for questions about the dataset.

Dataset Summary

Briefly summarize the dataset, its intended use and the supported tasks. Give an overview of how and why the dataset was created. The summary should explicitly mention the languages present in the dataset (possibly in broad terms, e.g. translations between several pairs of European languages), and describe the domain, topic, or genre covered.

Supported Tasks and Leaderboards

For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the task-category-tag with an appropriate other:other-task-name).

task-category-tag: The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a high/low metric name. The (model name or model class) model currently achieves the following score. [IF A LEADERBOARD IS AVAILABLE]: This task has an active leaderboard which can be found at leaderboard url and ranks models based on metric name while also reporting other metric name.

Languages

Provide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...

When relevant, please provide BCP-47 codes, which consist of a primary language subtag, with a script subtag and/or region subtag if available.

Dataset Structure

Data Instances

Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.

{
  'example_field': ...,
  ...
}

Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.

Data Fields

List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.

example_field: description of example_field

Note that the descriptions can be initialized with the Show Markdown Data Fields output of the Datasets Tagging app, you will then only need to refine the generated descriptions.

Data Splits

Describe and name the splits in the dataset if there are more than one.

Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.

Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:

	train	test
Input Sentences
Average Sentence Length

Dataset Creation

Curation Rationale

What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?

Source Data

This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)

Initial Data Collection and Normalization

Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.

If data was collected from other pre-existing datasets, link to source here and to their Hugging Face version.

If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.

Who are the source language producers?

State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.

If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.

Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.

Describe other people represented or mentioned in the data. Where possible, link to references for the information.

Annotations

If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.

Annotation process

If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.

Who are the annotators?

If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.

Describe the people or systems who originally created the annotations and their selection criteria if applicable.

If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.

Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.

Personal and Sensitive Information

State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See Larson 2017 for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).

State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).

If efforts were made to anonymize the data, describe the anonymization process.

Considerations for Using the Data

Social Impact of Dataset

Farsi is a language used by millions of people, for thousands of years; therefore, there exists numerous resources for this language. However, no-one has ever published a big enough easy to use corpus of textual data. Our dataset eases the path of pre-training and fine-tuning Farsi Language Models (LMs) in self-supervised manner which can lead to better tools for retention and development of Farsi. As a matter of fact, the informal portion of naab contains various dialects including, Turkish, Luri, etc. which are under-represented languages. Although the amount of data is comparably small, but it can be helpful in training a multi-lingual Tokenizer for Farsi variations. As mentioned before, some parts of our dataset are crawled from social media which in result means it contains ethnic, religious, and gender biases.

Discussion of Biases

During Exploratory Data Analysis (EDA), we found samples of data including biased opinions about race, religion, and gender. Based on the result we saw in our samples, only a small portion of informal data can be considered biased. Therefore, we anticipate that it won’t affect the trained language model on this data. Furthermore, we decided to keep this small part of data as it may become helpful in training models for classifying harmful and hateful texts.

Other Known Limitations

If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.

Additional Information

Dataset Curators

List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.

Licensing Information

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Citation Information

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@article{article_id,
  author    = {Author List},
  title     = {Dataset Paper Title},
  journal   = {Publication Venue},
  year      = {2525}
}

If the dataset has a DOI, please provide it here.

Contributions

Thanks to @sadrasabouri and @elnazrahmati for adding this dataset.

Datasets:

SLPL
/

naab