Datasets:
add readme to dataset card
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README.md
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path: data/selective-*
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- split: total
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path: data/total-*
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---
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path: data/selective-*
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- split: total
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path: data/total-*
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license: mit
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task_categories:
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- token-classification
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language:
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- pt
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---
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# Segundo HAREM
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According to [Linguateca](https://www.linguateca.pt/) - a repository for Natural Language resources in Portuguese - HAREM is a "joint evaluation in the area of Named Entity Recognition in Portuguese".
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The sole goal of this dataset is to make [Segundo HAREM](https://www.linguateca.pt/HAREM/) generally available through the huggingface hub and the datasets library.
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Intended use: this dataset may be used to train and/or evaluate Named Entity Recognition models in Portuguese.
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The script used to convert the original xml file containing the annotations of Segundo HAREM to json, and subsequently to json, was derived from [this fork](https://github.com/fabiocapsouza/harem_preprocessing).
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If you'd like to reproduce the creation of the dataset, you can check [my own fork](https://github.com/marquesafonso/harem_preprocessing) from which this dataset was created.
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You may find the authors and relevant work of this dataset in the citations section below. All credit is theirs.
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## Citations
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@inproceedings{freitas-etal-2010-second,
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title = "Second {HAREM}: Advancing the State of the Art of Named Entity Recognition in {P}ortuguese",
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author = "Freitas, Cl{\'a}udia and
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Mota, Cristina and
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Santos, Diana and
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Oliveira, Hugo Gon{\c{c}}alo and
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Carvalho, Paula",
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booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
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month = may,
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year = "2010",
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address = "Valletta, Malta",
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publisher = "European Language Resources Association (ELRA)",
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url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/412_Paper.pdf"
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}
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@misc{fabiocapsouza2020,
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author = {fabiocapsouza},
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title = {harem_preprocessing},
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year = {2020},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/fabiocapsouza/harem_preprocessing}},
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commit = {30083a542390094d84ec45116085ab71ceb1442c}
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}
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