Datasets:

rmyeid
/

polyglot_ner

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dataset_infos.json ADDED Viewed

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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
+"""The Polyglot-NER Dataset."""
+from __future__ import absolute_import, division, print_function
+import os
+import datasets
+_CITATION = """\
+@article{polyglotner,
+         author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven},
+         title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition},
+         journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, Canada, April 30- May 2, 2015}},
+         month     = {April},
+         year      = {2015},
+         publisher = {SIAM},
+}
+"""
+_LANGUAGES = [
+    "ca",
+    "de",
+    "es",
+    "fi",
+    "hi",
+    "id",
+    "ko",
+    "ms",
+    "pl",
+    "ru",
+    "sr",
+    "tl",
+    "vi",
+    "ar",
+    "cs",
+    "el",
+    "et",
+    "fr",
+    "hr",
+    "it",
+    "lt",
+    "nl",
+    "pt",
+    "sk",
+    "sv",
+    "tr",
+    "zh",
+    "bg",
+    "da",
+    "en",
+    "fa",
+    "he",
+    "hu",
+    "ja",
+    "lv",
+    "no",
+    "ro",
+    "sl",
+    "th",
+    "uk",
+]
+_LANG_FILEPATHS = {
+    lang: os.path.join(
+        "acl_datasets",
+        lang,
+        "data" if lang != "zh" else "",  # they're all lang/data/lang_wiki.conll except "zh"
+        f"{lang}_wiki.conll",
+    )
+    for lang in _LANGUAGES
+}
+_DESCRIPTION = """\
+Polyglot-NER
+A training dataset automatically generated from Wikipedia and Freebase the task
+of named entity recognition. The dataset contains the basic Wikipedia based
+training data for 40 languages we have (with coreference resolution) for the task of
+named entity recognition. The details of the procedure of generating them is outlined in
+Section 3 of the paper (https://arxiv.org/abs/1410.3791). Each config contains the data
+corresponding to a different language. For example, "es" includes only spanish examples.
+"""
+_DATA_URL = "http://cs.stonybrook.edu/~polyglot/ner2/emnlp_datasets.tgz"
+_HOMEPAGE_URL = "https://sites.google.com/site/rmyeid/projects/polylgot-ner"
+_VERSION = "1.0.0"
+class PolyglotNERConfig(datasets.BuilderConfig):
+    def __init__(self, *args, languages=None, **kwargs):
+        super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs)
+        self.languages = languages
+    @property
+    def filepaths(self):
+        return [_LANG_FILEPATHS[lang] for lang in self.languages]
+class PolyglotNER(datasets.GeneratorBasedBuilder):
+    """The Polyglot-NER Dataset"""
+    BUILDER_CONFIGS = [
+        PolyglotNERConfig(name=lang, languages=[lang], description=f"Polyglot-NER examples in {lang}.")
+        for lang in _LANGUAGES
+    ] + [
+        PolyglotNERConfig(
+            name="combined", languages=_LANGUAGES, description=f"Complete Polyglot-NER dataset with all languages."
+        )
+    ]
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "lang": datasets.Value("string"),
+                    "words": datasets.Sequence(datasets.Value("string")),
+                    "ner": datasets.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        path = dl_manager.download_and_extract(_DATA_URL)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"datapath": path})]
+    def _generate_examples(self, datapath):
+        sentence_counter = 0
+        for filepath, lang in zip(self.config.filepaths, self.config.languages):
+            filepath = os.path.join(datapath, filepath)
+            with open(filepath, encoding="utf-8") as f:
+                current_words = []
+                current_ner = []
+                for row in f:
+                    row = row.rstrip()
+                    if row:
+                        token, label = row.split("\t")
+                        current_words.append(token)
+                        current_ner.append(label)
+                    else:
+                        # New sentence
+                        if not current_words:
+                            # Consecutive empty lines will cause empty sentences
+                            continue
+                        assert len(current_words) == len(current_ner), "💔 between len of words & ner"
+                        sentence = (
+                            sentence_counter,
+                            {
+                                "id": str(sentence_counter),
+                                "lang": lang,
+                                "words": current_words,
+                                "ner": current_ner,
+                            },
+                        )
+                        sentence_counter += 1
+                        current_words = []
+                        current_ner = []
+                        yield sentence
+                # Don't forget last sentence in dataset 🧐
+                if current_words:
+                    yield sentence_counter, {
+                        "id": str(sentence_counter),
+                        "lang": lang,
+                        "words": current_words,
+                        "ner": current_ner,
+                    }