Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- pt
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+# Dataset Card for leNER-br
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## 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/},
+}	
+```

dataset_infos.json

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+{"lener_br": {"description": "\nLeNER-Br is a Portuguese language dataset for named entity recognition \napplied to legal documents. LeNER-Br consists entirely of manually annotated \nlegislation and legal cases texts and contains tags for persons, locations, \ntime entities, organizations, legislation and legal cases.\nTo compose the dataset, 66 legal documents from several Brazilian Courts were\ncollected. Courts of superior and state levels were considered, such as Supremo\nTribunal Federal, Superior Tribunal de Justi\u00e7a, Tribunal de Justi\u00e7a de Minas\nGerais and Tribunal de Contas da Uni\u00e3o. In addition, four legislation documents\nwere collected, such as \"Lei Maria da Penha\", giving a total of 70 documents\n", "citation": "\n@inproceedings{luz_etal_propor2018,\n    author = {Pedro H. {Luz de Araujo} and Te'{o}filo E. {de Campos} and\n    Renato R. R. {de Oliveira} and Matheus Stauffer and\n    Samuel Couto and Paulo Bermejo},\n    title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},\n    booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},\n    publisher = {Springer},\n    series = {Lecture Notes on Computer Science ({LNCS})},\n    pages = {313--323},\n    year = {2018},\n    month = {September 24-26},\n    address = {Canela, RS, Brazil},\t  \n    doi = {10.1007/978-3-319-99722-3_32},\n    url = {https://cic.unb.br/~teodecampos/LeNER-Br/},\n}\n", "homepage": "https://cic.unb.br/~teodecampos/LeNER-Br/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 13, "names": ["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"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "lener_br", "config_name": "lener_br", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3984189, "num_examples": 7828, "dataset_name": "lener_br"}, "validation": {"name": "validation", "num_bytes": 719433, "num_examples": 1177, "dataset_name": "lener_br"}, "test": {"name": "test", "num_bytes": 823708, "num_examples": 1390, "dataset_name": "lener_br"}}, "download_checksums": {"https://github.com/peluz/lener-br/raw/master/leNER-Br/train/train.conll": {"num_bytes": 2142199, "checksum": "6fdf9066333c84565f9e3d28ee8f0f519336bece69b63f8d78b8de0fe96dcd47"}, "https://github.com/peluz/lener-br/raw/master/leNER-Br/dev/dev.conll": {"num_bytes": 402497, "checksum": "7e350feb828198031e57c21d6aadbf8dac92b19a684e45d7081c6cb491e2063b"}, "https://github.com/peluz/lener-br/raw/master/leNER-Br/test/test.conll": {"num_bytes": 438441, "checksum": "f90cd26a31afc2d1f132c4473d40c26d2283a98b374025fa5b5985b723dce825"}}, "download_size": 2983137, "post_processing_size": null, "dataset_size": 5527330, "size_in_bytes": 8510467}}

dummy/lener_br/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:68278d9bf7fc56e7acc135ae56409e068426e0020d8188dd07c2c33bf387aac9
+size 1290

lener_br.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LeNER-Br dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import logging
+
+import datasets
+
+
+_CITATION = """
+@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/},
+}
+"""
+
+_DESCRIPTION = """
+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
+"""
+
+_HOMEPAGE = "https://cic.unb.br/~teodecampos/LeNER-Br/"
+
+_URL = "https://github.com/peluz/lener-br/raw/master/leNER-Br/"
+_TRAINING_FILE = "train/train.conll"
+_DEV_FILE = "dev/dev.conll"
+_TEST_FILE = "test/test.conll"
+
+
+class LenerBr(datasets.GeneratorBasedBuilder):
+    """LeNER-Br dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="lener_br", version=VERSION, description="LeNER-Br dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "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",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://cic.unb.br/~teodecampos/LeNER-Br/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_files["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": downloaded_files["test"], "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        logging.info("⏳ Generating examples from = %s", filepath)
+
+        with open(filepath, encoding="utf-8") as f:
+
+            guid = 0
+            tokens = []
+            ner_tags = []
+
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[1].rstrip())
+
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }