Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
License:
File size: 1,780 Bytes
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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, 13, 14, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4],
'tokens': ['a', 'steven', 'spielberg', 'film', 'featuring', 'a', 'bluff', 'called', 'devil', 's', 'tower', 'and', 'a', 'spectacular', 'mothership']
}
```
### 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 |6816 | 1000| 1953|
|