Initial commit

.gitattributes

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README.md

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 ---
 license: cc-by-4.0
+task_categories:
+- token-classification
+language:
+- bn
+- de
+- en
+- es
+- fa
+- hi
+- ko
+- nl
+- ru
+- tr
+- zh
+- multilingual
+tags:
+- multiconer
+- ner
+- multilingual
+- named entity recognition
+size_categories:
+- 100K<n<1M
+dataset_info:
+- config_name: bn
+  features:
+  - name: id
+    dtype: int32
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
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+          '2': I-PER
+          '3': B-LOC
+          '4': I-LOC
+          '5': B-CORP
+          '6': I-CORP
+          '7': B-GRP
+          '8': I-GRP
+          '9': B-PROD
+          '10': I-PROD
+          '11': B-CW
+          '12': I-CW
+  splits:
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+    num_examples: 800
+  - name: test
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+    num_examples: 133119
+  download_size: 31446032
+  dataset_size: 27586463
+- config_name: de
+  features:
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+  - name: tokens
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+  - name: ner_tags
+    sequence:
+      class_label:
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+          '2': I-PER
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+          '8': I-GRP
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+  download_size: 44089736
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+- config_name: en
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+  - name: ner_tags
+    sequence:
+      class_label:
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+  - name: ner_tags
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+  - name: ner_tags
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+  - name: ner_tags
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+- config_name: ko
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+  - name: ner_tags
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 ---
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+oid sha256:9a9cb5d22926e78914d3951c83d380f7b07d945729ca977848bd842d5ed01b31
+size 5092482

multiconer.py

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+# coding=utf-8
+"""MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition"""
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@misc{malmasi2022multiconer,
+    title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, 
+    author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko},
+    year={2022},
+    eprint={2208.14536},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki \
+sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. \
+This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short \
+and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The \
+26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, \
+template extraction and slotting, and machine translation. We applied two NER models on our dataset: a baseline \
+XLM-RoBERTa model, and a state-of-the-art GEMNET model that leverages gazetteers. The baseline achieves moderate \
+performance (macro-F1=54%), highlighting the difficulty of our data. GEMNET, which uses gazetteers, improvement \
+significantly (average improvement of macro-F1=+30%). MultiCoNER poses challenges even for large pre-trained \
+language models, and we believe that it can help further research in building robust NER systems. MultiCoNER \
+is publicly available at https://registry.opendata.aws/multiconer/ and we hope that this resource will help \
+advance research in various aspects of NER. 
+"""
+
+subset_to_dir = {
+    "bn": "BN-Bangla",
+    "de": "DE-German",
+    "en": "EN-English",
+    "es": "ES-Spanish",
+    "fa": "FA-Farsi",
+    "hi": "HI-Hindi",
+    "ko": "KO-Korean",
+    "nl": "NL-Dutch",
+    "ru": "RU-Russian",
+    "tr": "TR-Turkish",
+    "zh": "ZH-Chinese",
+    "multi": "MULTI_Multilingual",
+    "mix": "MIX_Code_mixed",
+}
+
+
+class MultiCoNERConfig(datasets.BuilderConfig):
+    """BuilderConfig for MultiCoNER"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for MultiCoNER.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MultiCoNERConfig, self).__init__(**kwargs)
+
+
+class MultiCoNER(datasets.GeneratorBasedBuilder):
+    """MultiCoNER dataset."""
+
+    BUILDER_CONFIGS = [
+        MultiCoNERConfig(
+            name="bn",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Bangla dataset",
+        ),
+        MultiCoNERConfig(
+            name="de",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER German dataset",
+        ),
+        MultiCoNERConfig(
+            name="en",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER English dataset",
+        ),
+        MultiCoNERConfig(
+            name="es",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Spanish dataset",
+        ),
+        MultiCoNERConfig(
+            name="fa",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Farsi dataset",
+        ),
+        MultiCoNERConfig(
+            name="hi",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Hindi dataset",
+        ),
+        MultiCoNERConfig(
+            name="ko",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Korean dataset",
+        ),
+        MultiCoNERConfig(
+            name="nl",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Dutch dataset",
+        ),
+        MultiCoNERConfig(
+            name="ru",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Russian dataset",
+        ),
+        MultiCoNERConfig(
+            name="tr",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Turkish dataset",
+        ),
+        MultiCoNERConfig(
+            name="zh",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Chinese dataset",
+        ),
+        MultiCoNERConfig(
+            name="multi",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Multilingual dataset",
+        ),
+        MultiCoNERConfig(
+            name="mix",
+            version=datasets.Version("1.0.0"),
+            description="MultiCoNER Mixed dataset",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-PER",
+                                "I-PER",
+                                "B-LOC",
+                                "I-LOC",
+                                "B-CORP",
+                                "I-CORP",
+                                "B-GRP",
+                                "I-GRP",
+                                "B-PROD",
+                                "I-PROD",
+                                "B-CW",
+                                "I-CW",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{subset_to_dir[self.config.name].upper()}/{self.config.name}_train.conll",
+            "dev": f"{subset_to_dir[self.config.name].upper()}/{self.config.name}_dev.conll",
+            "test": f"{subset_to_dir[self.config.name].upper()}/{self.config.name}_test.conll",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": downloaded_files["dev"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": downloaded_files["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+
+        with open(filepath, "r", encoding="utf8") as f:
+            guid = -1
+            tokens = []
+            ner_tags = []
+
+            for line in f:
+                if line.strip().startswith("# id"):
+                    guid += 1
+                    tokens = []
+                    ner_tags = []
+                elif " _ _ " in line:
+                    # Separator is " _ _ "
+                    splits = line.split(" _ _ ")
+                    tokens.append(splits[0].strip())
+                    ner_tags.append(splits[1].strip())
+                elif len(line.strip()) == 0:
+                    if len(tokens) >= 1 and len(tokens) == len(ner_tags):
+                        yield guid, {
+                            "id": guid,
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        tokens = []
+                        ner_tags = []
+                    else:
+                        continue
+
+            if len(tokens) >= 1 and len(tokens) == len(ner_tags):
+                yield guid, {
+                    "id": guid,
+                    "tokens": tokens,
+                    "ner_tags": ner_tags,
+                }