Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
< 1K
ArXiv:
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: crossner
pretty_name: CrossNER-LITERATURE
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-book
'2': I-book
'3': B-writer
'4': I-writer
'5': B-award
'6': I-award
'7': B-poem
'8': I-poem
'9': B-event
'10': I-event
'11': B-magazine
'12': I-magazine
'13': B-literarygenre
'14': I-literarygenre
'15': B-country
'16': I-country
'17': B-person
'18': I-person
'19': B-location
'20': I-location
'21': B-organisation
'22': I-organisation
'23': B-misc
'24': I-misc
splits:
- name: train
num_bytes: 10000
num_examples: 100
- name: validation
num_bytes: 40000
num_examples: 400
- name: test
num_bytes: 41600
num_examples: 416
CrossNER LITERATURE Dataset
An NER dataset for cross-domain evaluation, read more.
This split contains labeled data from the LITERATURE domain.
Features
- tokens: A list of words in the sentence
- ner_tags: A list of NER labels (as integers) corresponding to each token
Label Mapping
The dataset uses the following 25 labels:
| Index | Label |
|---|---|
| 0 | O |
| 1 | B-book |
| 2 | I-book |
| 3 | B-writer |
| 4 | I-writer |
| 5 | B-award |
| 6 | I-award |
| 7 | B-poem |
| 8 | I-poem |
| 9 | B-event |
| 10 | I-event |
| 11 | B-magazine |
| 12 | I-magazine |
| 13 | B-literarygenre |
| 14 | I-literarygenre |
| 15 | B-country |
| 16 | I-country |
| 17 | B-person |
| 18 | I-person |
| 19 | B-location |
| 20 | I-location |
| 21 | B-organisation |
| 22 | I-organisation |
| 23 | B-misc |
| 24 | I-misc |
Usage
from datasets import load_dataset
dataset = load_dataset("eesuhn/crossner-literature")