Convert dataset to Parquet (#9)

- Convert dataset to Parquet (549279667653b12a7377001295615ea6cec3d369)
- Delete loading script (c8c9ca6ed35d48368a8e835afa15626ae6bbfd9e)

README.md

@@ -1,17 +1,47 @@
 ---
-license: other
-license_name: europarl-custom
-license_link: https://www.statmt.org/europarl/
-task_categories:
-- token-classification
 language:
 - en
 - it
 - de
 - es
+license: other
+size_categories:
+- 1K<n<10K
+task_categories:
+- token-classification
+license_name: europarl-custom
+license_link: https://www.statmt.org/europarl/
 tags:
 - NER
 - Europarl
-size_categories:
-- 1K<n<10K
----
+dataset_info:
+  config_name: en
+  features:
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-PER
+          '2': I-PER
+          '3': B-ORG
+          '4': I-ORG
+          '5': B-LOC
+          '6': I-LOC
+          '7': B-MISC
+          '8': I-MISC
+  splits:
+  - name: test
+    num_bytes: 374649
+    num_examples: 799
+  download_size: 64713
+  dataset_size: 374649
+configs:
+- config_name: en
+  data_files:
+  - split: test
+    path: en/test-*
+  default: true
+---

en/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac5dd43f41ca544dfbcd4b1ad643626b8d6973313b2c5bf6655e22798b35f742
+size 64713

europarl-ner.py

@@ -1,137 +0,0 @@
-# coding=utf-8
-
-"""The HF Datasets adapter for Evaluation Corpus for Named Entity Recognition using Europarl"""
-
-import datasets
-
-_CITATION = """@inproceedings{agerri-etal-2018-building,
-    title = "Building Named Entity Recognition Taggers via Parallel Corpora",
-    author = "Agerri, Rodrigo  and
-      Chung, Yiling  and
-      Aldabe, Itziar  and
-      Aranberri, Nora  and
-      Labaka, Gorka  and
-      Rigau, German",
-    editor = "Calzolari, Nicoletta  and
-      Choukri, Khalid  and
-      Cieri, Christopher  and
-      Declerck, Thierry  and
-      Goggi, Sara  and
-      Hasida, Koiti  and
-      Isahara, Hitoshi  and
-      Maegaard, Bente  and
-      Mariani, Joseph  and
-      Mazo, H{\'e}l{\`e}ne  and
-      Moreno, Asuncion  and
-      Odijk, Jan  and
-      Piperidis, Stelios  and
-      Tokunaga, Takenobu",
-    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
-    month = may,
-    year = "2018",
-    address = "Miyazaki, Japan",
-    publisher = "European Language Resources Association (ELRA)",
-    url = "https://aclanthology.org/L18-1557",
-}"""
-
-_DESCRIPTION = """This dataset contains a gold-standard test set created from the
-Europarl corpus. The test set consists of 799 sentences manually annotated using
-four entity types and following the CoNLL 2002 and 2003 guidelines for 4 languages:
-English, German, Italian and Spanish."""
-
-_DATA_URLs = {
-    "en": "https://github.com/ixa-ehu/ner-evaluation-corpus-europarl/raw/master/en-europarl.test.conll02",
-    "de": "https://github.com/ixa-ehu/ner-evaluation-corpus-europarl/raw/master/de-europarl.test.conll02",
-    "es": "https://github.com/ixa-ehu/ner-evaluation-corpus-europarl/raw/master/es-europarl.test.conll02",
-    "it": "https://github.com/ixa-ehu/ner-evaluation-corpus-europarl/raw/master/it-europarl.test.conll02",
-}
-_HOMEPAGE = "https://github.com/ixa-ehu/ner-evaluation-corpus-europarl"
-_VERSION = "1.0.0"
-_LANGS = ["en", "de", "es", "it"]
-
-
-class EuroparlNERConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(EuroparlNERConfig, self).__init__(
-            version=datasets.Version(_VERSION, ""), **kwargs
-        )
-
-
-class EuroparlNER(datasets.GeneratorBasedBuilder):
-    """EuroparlNER is a multilingual named entity recognition dataset consisting of
-    manualy anotated part of the European Parliament Proceedings Parallel Corpus
-    1996-2011 with LOC, PER, ORG and MISC tags"""
-
-    VERSION = datasets.Version(_VERSION)
-    BUILDER_CONFIGS = [
-        EuroparlNERConfig(
-            name=lang, description=f"EuroparlNER examples in language {lang}"
-        )
-        for lang in _LANGS
-    ]
-    DEFAULT_CONFIG_NAME = "en"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "ner_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=[
-                            "O",
-                            "B-PER",
-                            "I-PER",
-                            "B-ORG",
-                            "I-ORG",
-                            "B-LOC",
-                            "I-LOC",
-                            "B-MISC",
-                            "I-MISC",
-                        ]
-                    )
-                ),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        lang = self.config.name
-        dl_dir = dl_manager.download(_DATA_URLs[lang])
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": dl_dir},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        guid_index = 1
-        with open(filepath, encoding="utf-8") as f:
-            tokens = []
-            ner_tags = []
-            for line in f:
-                if line == "" or line == "\n":
-                    if tokens:
-                        yield guid_index, {
-                            "tokens": tokens,
-                            "ner_tags": ner_tags,
-                        }
-                        guid_index += 1
-                        tokens = []
-                        ner_tags = []
-                else:
-                    # EuroparlNER data is tab separated
-                    splits = line.split("\t")
-                    tokens.append(splits[0])
-                    if len(splits) > 1:
-                        ner_tags.append(splits[1].replace("\n", ""))
-                    else:
-                        # examples have no label in test set
-                        ner_tags.append("O")