Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
License:
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| | |