Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Portuguese
Size:
10K<n<100K
Tags:
legal
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - expert-generated | |
| language: | |
| - pt | |
| license: | |
| - unknown | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - token-classification | |
| task_ids: | |
| - named-entity-recognition | |
| paperswithcode_id: lener-br | |
| pretty_name: leNER-br | |
| dataset_info: | |
| features: | |
| - name: id | |
| dtype: string | |
| - name: tokens | |
| sequence: string | |
| - name: ner_tags | |
| sequence: | |
| class_label: | |
| names: | |
| '0': O | |
| '1': B-ORGANIZACAO | |
| '2': I-ORGANIZACAO | |
| '3': B-PESSOA | |
| '4': I-PESSOA | |
| '5': B-TEMPO | |
| '6': I-TEMPO | |
| '7': B-LOCAL | |
| '8': I-LOCAL | |
| '9': B-LEGISLACAO | |
| '10': I-LEGISLACAO | |
| '11': B-JURISPRUDENCIA | |
| '12': I-JURISPRUDENCIA | |
| config_name: lener_br | |
| splits: | |
| - name: train | |
| num_bytes: 3984189 | |
| num_examples: 7828 | |
| - name: validation | |
| num_bytes: 719433 | |
| num_examples: 1177 | |
| - name: test | |
| num_bytes: 823708 | |
| num_examples: 1390 | |
| download_size: 2983137 | |
| dataset_size: 5527330 | |
| tags: | |
| - legal | |
| # Dataset Card for leNER-br | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [leNER-BR homepage](https://cic.unb.br/~teodecampos/LeNER-Br/) | |
| - **Repository:** [leNER-BR repository](https://github.com/peluz/lener-br) | |
| - **Paper:** [leNER-BR: Long Form Question Answering](https://cic.unb.br/~teodecampos/LeNER-Br/luz_etal_propor2018.pdf) | |
| - **Point of Contact:** [Pedro H. Luz de Araujo](mailto:pedrohluzaraujo@gmail.com) | |
| ### Dataset Summary | |
| LeNER-Br is a Portuguese language dataset for named entity recognition | |
| applied to legal documents. LeNER-Br consists entirely of manually annotated | |
| legislation and legal cases texts and contains tags for persons, locations, | |
| time entities, organizations, legislation and legal cases. | |
| To compose the dataset, 66 legal documents from several Brazilian Courts were | |
| collected. Courts of superior and state levels were considered, such as Supremo | |
| Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas | |
| Gerais and Tribunal de Contas da União. In addition, four legislation documents | |
| were collected, such as "Lei Maria da Penha", giving a total of 70 documents | |
| ### Supported Tasks and Leaderboards | |
| [More Information Needed] | |
| ### Languages | |
| The language supported is Portuguese. | |
| ## Dataset Structure | |
| ### Data Instances | |
| An example from the dataset looks as follows: | |
| ``` | |
| { | |
| "id": "0", | |
| "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0], | |
| "tokens": [ | |
| "EMENTA", ":", "APELAÇÃO", "CÍVEL", "-", "AÇÃO", "DE", "INDENIZAÇÃO", "POR", "DANOS", "MORAIS", "-", "PRELIMINAR", "-", "ARGUIDA", "PELO", "MINISTÉRIO", "PÚBLICO", "EM", "GRAU", "RECURSAL"] | |
| } | |
| ``` | |
| ### Data Fields | |
| - `id`: id of the sample | |
| - `tokens`: the tokens of the example text | |
| - `ner_tags`: the NER tags of each token | |
| The NER tags correspond to this list: | |
| ``` | |
| "O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-TEMPO", "I-TEMPO", "B-LOCAL", "I-LOCAL", "B-LEGISLACAO", "I-LEGISLACAO", "B-JURISPRUDENCIA", "I-JURISPRUDENCIA" | |
| ``` | |
| The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. | |
| ### Data Splits | |
| The data is split into train, validation and test set. The split sizes are as follow: | |
| | Train | Val | Test | | |
| | ------ | ----- | ---- | | |
| | 7828 | 1177 | 1390 | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [More Information Needed] | |
| #### Who are the source language producers? | |
| [More Information Needed] | |
| ### Annotations | |
| #### Annotation process | |
| [More Information Needed] | |
| #### Who are the annotators? | |
| [More Information Needed] | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed] | |
| ### Licensing Information | |
| [More Information Needed] | |
| ### Citation Information | |
| ``` | |
| @inproceedings{luz_etal_propor2018, | |
| author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and | |
| Renato R. R. {de Oliveira} and Matheus Stauffer and | |
| Samuel Couto and Paulo Bermejo}, | |
| title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text}, | |
| booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})}, | |
| publisher = {Springer}, | |
| series = {Lecture Notes on Computer Science ({LNCS})}, | |
| pages = {313--323}, | |
| year = {2018}, | |
| month = {September 24-26}, | |
| address = {Canela, RS, Brazil}, | |
| doi = {10.1007/978-3-319-99722-3_32}, | |
| url = {https://cic.unb.br/~teodecampos/LeNER-Br/}, | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. |