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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README.md

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+---
+annotations_creators: []
+language_creators: []
+languages:
+- de
+licenses:
+- other-ASL 2-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+# Dataset Card Creation Guide
+
+## 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:** None
+- **Repository:** https://github.com/tudarmstadt-lt/GermaNER
+- **Paper:** https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf
+- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
+- **Point of Contact:** Darina Benikova ([email protected],)
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+Here are some examples of questions and facts:
+
+* What American cartoonist is the creator of Andy Lippincott?
+  Fact: (andy_lippincott, character_created_by, garry_trudeau) 
+* Which forest is Fires Creek in?
+  Fact: (fires_creek, containedby, nantahala_national_forest)
+* What does Jimmy Neutron do?
+  Fact: (jimmy_neutron, fictional_character_occupation, inventor)
+* What dietary restriction is incompatible with kimchi?
+  Fact: (kimchi, incompatible_with_dietary_restrictions, veganism)
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "GermaNER is a freely available statistical German Named Entity Tagger based on conditional random fields(CRF). The tagger is trained and evaluated on the NoSta-D Named Entity dataset, which was used in the GermEval 2014 for named entity recognition. The tagger comes close to the performance of the best (proprietary) system in the competition with 77% F-measure (this is the latest result; the one reported in the paper is 76%) test set performance on the four standard NER classes (PERson, LOCation, ORGanisation and OTHer).\n\nWe describe a range of features and their influence on German NER classification and provide a comparative evaluation and some analysis of the results. The software components, the training data and all data used for feature generation are distributed under permissive licenses, thus this tagger can be used in academic and commercial settings without restrictions or fees. The tagger is available as a command-line tool and as an Apache UIMA component.\n", "citation": "GermaNER: Free Open German Named Entity Recognition Tool, Darina Benikova,Seid Muhie Yimam, Prabhakaran Santhanam and Chris Biemann, In: International Conference of the German Society for Computational Linguistics and Language Technology (GSCL-2015), 2015.", "homepage": "https://github.com/tudarmstadt-lt/GermaNER", "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": 9, "names": ["B-LOC", "B-ORG", "B-OTH", "B-PER", "I-LOC", "I-ORG", "I-OTH", "I-PER", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "germa_ner", "config_name": "default", "version": {"version_str": "0.9.1", "description": null, "major": 0, "minor": 9, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 9059606, "num_examples": 26200, "dataset_name": "germa_ner"}}, "download_checksums": {"https://raw.githubusercontent.com/tudarmstadt-lt/GermaNER/a206b554feca263d740302449fff0776c66d0040/data/v0.9.1/full_train.tsv": {"num_bytes": 4363657, "checksum": "7532d8372e4f40730383629205c24a12fddd04eb5dac121c75688f06c6ccb8a9"}}, "download_size": 4363657, "post_processing_size": null, "dataset_size": 9059606, "size_in_bytes": 13423263}}

dummy/0.9.1/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b4e88c313ed53a4120b0a0481819f9ea6981c4e580f7a1d5a8474209c4ed056
+size 295

germaner.py

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+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+import datasets
+
+
+_DESCRIPTION = """\
+GermaNER is a freely available statistical German Named Entity Tagger based on conditional random fields(CRF). The tagger is trained and evaluated on the NoSta-D Named Entity dataset, which was used in the GermEval 2014 for named entity recognition. The tagger comes close to the performance of the best (proprietary) system in the competition with 77% F-measure (this is the latest result; the one reported in the paper is 76%) test set performance on the four standard NER classes (PERson, LOCation, ORGanisation and OTHer).
+
+We describe a range of features and their influence on German NER classification and provide a comparative evaluation and some analysis of the results. The software components, the training data and all data used for feature generation are distributed under permissive licenses, thus this tagger can be used in academic and commercial settings without restrictions or fees. The tagger is available as a command-line tool and as an Apache UIMA component.
+"""
+_HOMEPAGE_URL = "https://github.com/tudarmstadt-lt/GermaNER"
+_URL = "https://raw.githubusercontent.com/tudarmstadt-lt/GermaNER/a206b554feca263d740302449fff0776c66d0040/data/v0.9.1/full_train.tsv"
+_CITATION = """\
+@inproceedings{Benikova2015GermaNERFO,
+  title={GermaNER: Free Open German Named Entity Recognition Tool},
+  author={Darina Benikova and S. Yimam and Prabhakaran Santhanam and Chris Biemann},
+  booktitle={GSCL},
+  year={2015}
+}
+"""
+
+
+class GermaNER(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.9.1")
+
+    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=[
+                                "B-LOC",
+                                "B-ORG",
+                                "B-OTH",
+                                "B-PER",
+                                "I-LOC",
+                                "I-ORG",
+                                "I-OTH",
+                                "I-PER",
+                                "O",
+                            ]
+                        )
+                    ),
+                },
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"datapath": path},
+            )
+        ]
+
+    def _generate_examples(self, datapath):
+        sentence_counter = 0
+        with open(datapath, encoding="utf-8") as f:
+            current_words = []
+            current_labels = []
+            for row in f:
+                row = row.rstrip()
+                row_split = row.split()
+                if len(row_split) == 2:
+                    token, label = row_split
+                    current_words.append(token)
+                    current_labels.append(label)
+                else:
+                    if not current_words:
+                        continue
+                    assert len(current_words) == len(current_labels), "word len doesnt match label length"
+                    sentence = (
+                        sentence_counter,
+                        {
+                            "id": str(sentence_counter),
+                            "tokens": current_words,
+                            "ner_tags": current_labels,
+                        },
+                    )
+                    sentence_counter += 1
+                    current_words = []
+                    current_labels = []
+                    yield sentence
+
+            # if something remains:
+            if current_words:
+                sentence = (
+                    sentence_counter,
+                    {
+                        "id": str(sentence_counter),
+                        "tokens": current_words,
+                        "ner_tags": current_labels,
+                    },
+                )
+                yield sentence