init

README.md

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+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: MIT Movie
+---
+
+# Dataset Card for "tner/mit_movie_trivia"
+
+## Dataset Description
+
+- **Repository:** [T-NER](https://github.com/asahi417/tner)
+- **Dataset:** MIT Movie
+- **Domain:** Movie
+- **Number of Entity:** 12
+
+### Dataset Summary
+MIT Movie NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
+
+- Entity Types: `Actor`, `Plot`, `Opinion`, `Award`, `Year`, `Genre`, `Origin`, `Director`, `Soundtrack`, `Relationship`, `Character_Name`, `Quote`
+
+## Dataset Structure
+
+### Data Instances
+An example of `train` looks as follows.
+
+```
+{
+    'tags': [0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 4, 0],
+    'tokens': ['can', 'you', 'find', 'the', 'phone', 'number', 'for', 'the', 'closest', 'family', 'style', 'restaurant']
+}
+```
+
+### Label ID
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/mit_movie_trivia/raw/main/dataset/label.json).
+```python
+{
+    "O": 0,
+    "B-Actor": 1,
+    "I-Actor": 2,
+    "B-Plot": 3,
+    "I-Plot": 4,
+    "B-Opinion": 5,
+    "I-Opinion": 6,
+    "B-Award": 7,
+    "I-Award": 8,
+    "B-Year": 9,
+    "B-Genre": 10,
+    "B-Origin": 11,
+    "I-Origin": 12,
+    "B-Director": 13,
+    "I-Director": 14,
+    "I-Genre": 15,
+    "I-Year": 16,
+    "B-Soundtrack": 17,
+    "I-Soundtrack": 18,
+    "B-Relationship": 19,
+    "I-Relationship": 20,
+    "B-Character_Name": 21,
+    "I-Character_Name": 22,
+    "B-Quote": 23,
+    "I-Quote": 24
+}
+```
+
+### Data Splits
+
+|  name   |train|validation|test|
+|---------|----:|---------:|---:|
+|mit_movie_trivia      |6900  |       760| 1521|

dataset/label.json

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+{"O": 0, "B-Actor": 1, "I-Actor": 2, "B-Plot": 3, "I-Plot": 4, "B-Opinion": 5, "I-Opinion": 6, "B-Award": 7, "I-Award": 8, "B-Year": 9, "B-Genre": 10, "B-Origin": 11, "I-Origin": 12, "B-Director": 13, "I-Director": 14, "I-Genre": 15, "I-Year": 16, "B-Soundtrack": 17, "I-Soundtrack": 18, "B-Relationship": 19, "I-Relationship": 20, "B-Character_Name": 21, "I-Character_Name": 22, "B-Quote": 23, "I-Quote": 24}

mit_movie_trivia.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,
+        )