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README.md
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@@ -40,18 +40,28 @@ However, users can ask a wide range of questions on stackoverflow; The "stackove
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posted on SO with an associated type. Following a previous [study](https://ieeexplore.ieee.org/document/6405249), each question was annotated with a type
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capturing the main concern of the user who posted the question. The questions were annotated with the given types:
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* *How to do it*: Providing a scenario and asking about how to implement it (sometimes with a given technology or API).
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* *Debug/corrective*: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour.
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* *Seeking different solutions*: The questioner has a working code yet is seeking a different approach to doing the job.
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* *Need to know*: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language).
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* *
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- Debug: the user wants to do "x", has a clear idea/solution "y" but it is not working, and is seeking a correction to "y".
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- Seeking-different-solution: the user wants to do "x", and found already a working solution "y", but is seeking an alternative "z".
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Naturally, some questions may have multiple concerns (i.e. could correspond to multiple categories).
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However, this dataset contains mainly questions for which we could assign a clear single category to each question.
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Currently, all questions annotated are a subset of the [stackoverflow_python](koutch/stackoverflow_python) dataset.
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- question_id: the unique id of the post
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- question_body: the (HTML) content of the question
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- question_type: the assigned category/type/label
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- "howto",
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- "debug",
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- "seeking",
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- "conceptual",
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- "other"
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### Data Splits
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Previous research looked into mining natural language-code pairs from stackoverflow.
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Two notable works yielded the [StaQC](https://arxiv.org/abs/1803.09371) and [ConaLA](https://arxiv.org/abs/1803.09371) datasets.
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Parts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at [staqc](https://huggingface.co/datasets/koutch/staqc),
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and [conala](https://huggingface.co/datasets/neulab/conala]). The questions were annotated as belonging to the "how to do it" category.
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To ease the annotation procedure, we used the [argilla platform](https://docs.argilla.io/en/latest/index.html)
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and multiple iterations of [few-shot training with a SetFit model](https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-setfit-zeroshot.html#%F0%9F%A6%BE-Train-a-few-shot-SetFit-model).
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### Other Known Limitations
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[More Information Needed]
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# Dataset Card for "stackoverflow_question_types"
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[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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posted on SO with an associated type. Following a previous [study](https://ieeexplore.ieee.org/document/6405249), each question was annotated with a type
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capturing the main concern of the user who posted the question. The questions were annotated with the given types:
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* *Need to know*: Questions regarding the possibility or availability of (doing) something. These questions normally show the lack of knowledge or uncertainty about some aspects of the technology (e.g. the presence of a feature in an API or a language).
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* *How to do it*: Providing a scenario and asking how to implement it (sometimes with a given technology or API).
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* *Debug/corrective*: Dealing with problems in the code under development, such as runtime errors and unexpected behaviour.
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* *Seeking different solutions*: The questioner has a working code yet seeks a different approach to doing the job.
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* *Other*: a question related to another aspect of programming, or even non-related to programming.
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### Remarks
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For this dataset, we are mainly interested in questions related to *programming*.
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For instance, for [this question](https://stackoverflow.com/questions/51142399/no-acceptable-c-compiler-found-in-path-installing-python-and-gcc),
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the user is "trying to install Python-3.6.5 on a machine that does not have any package manager installed" and is facing issues.
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Because it's not related to the concept of programming, we would classify it as "other" and not "debugging".
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Moreover, we note the following conceptual distinctions between the different categories:
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- Need to know: the user asks "is it possible to do x"
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- How to do it: the user wants to do "x", knows it's possible, but has no clear idea or solution/doesn't know how to do it -> wants any solution for solving "x".
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- Debug: the user wants to do "x", and has a clear idea/solution "y" but it is not working, and is seeking a correction to "y".
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- Seeking-different-solution: the user wants to do "x", and has found already a working solution "y", but is seeking an alternative "z".
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Sometimes, it's hard to truly understand the users' true intentions;
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the separating line between each category will be minor and might be subject to interpretation.
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Naturally, some questions may have multiple concerns (i.e. could correspond to multiple categories).
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However, this dataset contains mainly questions for which we could assign a clear single category to each question.
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Currently, all questions annotated are a subset of the [stackoverflow_python](koutch/stackoverflow_python) dataset.
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- question_id: the unique id of the post
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- question_body: the (HTML) content of the question
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- question_type: the assigned category/type/label
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- "needtoknow"
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- "howto",
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- "debug",
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- "seeking",
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- "other"
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### Data Splits
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Previous research looked into mining natural language-code pairs from stackoverflow.
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Two notable works yielded the [StaQC](https://arxiv.org/abs/1803.09371) and [ConaLA](https://arxiv.org/abs/1803.09371) datasets.
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Parts of the dataset used a subset of the manual annotations provided by the authors of the papers (available at [staqc](https://huggingface.co/datasets/koutch/staqc),
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and [conala](https://huggingface.co/datasets/neulab/conala])). The questions were annotated as belonging to the "how to do it" category.
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To ease the annotation procedure, we used the [argilla platform](https://docs.argilla.io/en/latest/index.html)
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and multiple iterations of [few-shot training with a SetFit model](https://docs.argilla.io/en/latest/tutorials/notebooks/labelling-textclassification-setfit-zeroshot.html#%F0%9F%A6%BE-Train-a-few-shot-SetFit-model).
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### Other Known Limitations
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[More Information Needed]
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