init

README.md

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+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: Ontonotes5
+---
+
+# Dataset Card for "tner/ontonotes5"
+
+## Dataset Description
+
+- **Repository:** [T-NER](https://github.com/asahi417/tner)
+- **Paper:** [https://aclanthology.org/N06-2015/](https://aclanthology.org/N06-2015/)
+- **Dataset:** Ontonotes5
+- **Domain:** News
+- **Number of Entity:** `8 (`CARDINAL`, `DATE`, `PERSON`, `NORP`, `GPE`, `LAW`, `PERCENT`, `ORDINAL`, `MONEY`, `WORK_OF_ART`, `FAC`, `TIME`, `QUANTITY`, `PRODUCT`, `LANGUAGE`, `ORG`, `LOC`, `EVENT`)
+
+### Dataset Summary
+Ontonotes5 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
+
+## Dataset Structure
+
+### Data Instances
+An example of `train` looks as follows.
+
+```
+{
+    'tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0, 0, 0, 11, 12, 12, 12, 12, 0, 0, 7, 0, 0, 0, 0, 0],
+    'tokens': ['``', 'It', "'s", 'very', 'costly', 'and', 'time', '-', 'consuming', ',', "''", 'says', 'Phil', 'Rosen', ',', 'a', 'partner', 'in', 'Fleet', '&', 'Leasing', 'Management', 'Inc.', ',', 'a', 'Boston', 'car', '-', 'leasing', 'company', '.']
+}
+```
+
+### Label ID
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/onotonotes5/raw/main/dataset/label.json).
+```python
+{
+    "O": 0,
+    "B-CARDINAL": 1,
+    "B-DATE": 2,
+    "I-DATE": 3,
+    "B-PERSON": 4,
+    "I-PERSON": 5,
+    "B-NORP": 6,
+    "B-GPE": 7,
+    "I-GPE": 8,
+    "B-LAW": 9,
+    "I-LAW": 10,
+    "B-ORG": 11,
+    "I-ORG": 12, 
+    "B-PERCENT": 13,
+    "I-PERCENT": 14, 
+    "B-ORDINAL": 15, 
+    "B-MONEY": 16, 
+    "I-MONEY": 17, 
+    "B-WORK_OF_ART": 18, 
+    "I-WORK_OF_ART": 19, 
+    "B-FAC": 20, 
+    "B-TIME": 21, 
+    "I-CARDINAL": 22, 
+    "B-LOC": 23, 
+    "B-QUANTITY": 24, 
+    "I-QUANTITY": 25, 
+    "I-NORP": 26, 
+    "I-LOC": 27, 
+    "B-PRODUCT": 28, 
+    "I-TIME": 29, 
+    "B-EVENT": 30,
+    "I-EVENT": 31,
+    "I-FAC": 32,
+    "B-LANGUAGE": 33,
+    "I-PRODUCT": 34,
+    "I-ORDINAL": 35,
+    "I-LANGUAGE": 36
+}
+```
+
+### Data Splits
+
+|  name   |train|validation|test|
+|---------|----:|---------:|---:|
+|ontonotes5|59924|      8528|8262|
+
+### Citation Information
+
+```
+@inproceedings{hovy-etal-2006-ontonotes,
+    title = "{O}nto{N}otes: The 90{\%} Solution",
+    author = "Hovy, Eduard  and
+      Marcus, Mitchell  and
+      Palmer, Martha  and
+      Ramshaw, Lance  and
+      Weischedel, Ralph",
+    booktitle = "Proceedings of the Human Language Technology Conference of the {NAACL}, Companion Volume: Short Papers",
+    month = jun,
+    year = "2006",
+    address = "New York City, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N06-2015",
+    pages = "57--60",
+}
+```

dataset/label.json

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+{"O": 0, "I-ORG": 1, "I-LOC": 2, "I-PER": 3, "I-MISC": 4}

fin.py

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+""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """
+import json
+from itertools import chain
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """[FIN NER dataset](https://aclanthology.org/U15-1010.pdf)"""
+_NAME = "fin"
+_VERSION = "1.0.0"
+_CITATION = """
+@inproceedings{salinas-alvarado-etal-2015-domain,
+    title = "Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment",
+    author = "Salinas Alvarado, Julio Cesar  and
+      Verspoor, Karin  and
+      Baldwin, Timothy",
+    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
+    month = dec,
+    year = "2015",
+    address = "Parramatta, Australia",
+    url = "https://aclanthology.org/U15-1010",
+    pages = "84--90",
+}
+"""
+
+_HOME_PAGE = "https://github.com/asahi417/tner"
+_URL = f'https://huggingface.co/datasets/tner/{_NAME}/raw/main/dataset'
+_URLS = {
+    str(datasets.Split.TEST): [f'{_URL}/test.json'],
+    str(datasets.Split.TRAIN): [f'{_URL}/train.json'],
+    str(datasets.Split.VALIDATION): [f'{_URL}/valid.json'],
+}
+
+
+class FinConfig(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FinConfig, self).__init__(**kwargs)
+
+
+class Fin(datasets.GeneratorBasedBuilder):
+    """Dataset."""
+
+    BUILDER_CONFIGS = [
+        FinConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
+    ]
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URLS)
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
+
+    def _generate_examples(self, filepaths):
+        _key = 0
+        for filepath in filepaths:
+            logger.info(f"generating examples from = {filepath}")
+            with open(filepath, encoding="utf-8") as f:
+                _list = [i for i in f.read().split('\n') if len(i) > 0]
+                for i in _list:
+                    data = json.loads(i)
+                    yield _key, data
+                    _key += 1
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "tags": datasets.Sequence(datasets.Value("int32")),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOME_PAGE,
+            citation=_CITATION,
+        )