Igortin/github-datasets-lib-issues

1---2language:3- en4pretty_name: github-datasets-issues5size_categories:6- 10K<n<100K7task_categories:8- question-answering9- text-classification10---11# Dataset Card for GitHub Issues12 13## Dataset Description14 15### Dataset Summary16 17GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.18 19### Supported Tasks and Leaderboards20 21For 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`).22 23- `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](https://huggingface.co/metrics/metric_name). The ([model name](https://huggingface.co/model_name) or [model class](https://huggingface.co/transformers/model_doc/model_class.html)) 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](https://huggingface.co/metrics/metric_name) while also reporting [other metric name](https://huggingface.co/metrics/other_metric_name).24 25### Languages26 27Provide 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,...28 29When relevant, please provide [BCP-47 codes](https://tools.ietf.org/html/bcp47), which consist of a [primary language subtag](https://tools.ietf.org/html/bcp47#section-2.2.1), with a [script subtag](https://tools.ietf.org/html/bcp47#section-2.2.3) and/or [region subtag](https://tools.ietf.org/html/bcp47#section-2.2.4) if available.30 31## Dataset Structure32 33### Data Instances34 35Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.36 37```38{39  'example_field': ...,40  ...41}42```43 44Provide 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.45 46### Data Fields47 48List 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.49 50- `example_field`: description of `example_field`51 52Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [tagging app](https://github.com/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.53 54### Data Splits55 56Describe and name the splits in the dataset if there are more than one.57 58Describe any criteria for splitting the data, if used. If their 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.59 60Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length.  For example:61 62|                            | Tain   | Valid | Test |63| -----                      | ------ | ----- | ---- |64| Input Sentences            |        |       |      |65| Average Sentence Length    |        |       |      |66 67## Dataset Creation68 69### Curation Rationale70 71What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?72 73### Source Data74 75This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)76 77#### Initial Data Collection and Normalization78 79Describe 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.80 81If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).82 83If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.84 85#### Who are the source language producers?86 87State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.88 89If 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](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.90 91Describe 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.92 93Describe other people represented or mentioned in the data. Where possible, link to references for the information.94 95### Annotations96 97If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.98 99#### Annotation process100 101If 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.102 103#### Who are the annotators?104 105If 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.106 107Describe the people or systems who originally created the annotations and their selection criteria if applicable.108 109If 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](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.110 111Describe 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.112 113### Personal and Sensitive Information114 115State 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](https://www.aclweb.org/anthology/W17-1601.pdf) 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).116 117State 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).  118 119If efforts were made to anonymize the data, describe the anonymization process.120 121## Considerations for Using the Data122 123### Social Impact of Dataset124 125Please discuss some of the ways you believe the use of this dataset will impact society.126 127The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.128 129Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.130 131### Discussion of Biases132 133Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.134 135For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.136 137If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.138 139### Other Known Limitations140 141If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.142 143## Additional Information144 145### Dataset Curators146 147List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.148 149### Licensing Information150 151Provide the license and link to the license webpage if available.