tomaarsen/span-marker-xlm-roberta-large-conllpp-doc-context
Token Classification
•
Updated
•
56
Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
License:
id
stringlengths 1
5
| document_id
int32 1
946
| sentence_id
int32 0
196
| tokens
sequence | pos_tags
sequence | chunk_tags
sequence | ner_tags
sequence |
|---|---|---|---|---|---|---|
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68 | 7 | 8 | [
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70 | 7 | 10 | [
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End of preview. Expand
in Dataset Viewer.
Dataset Card for "conllpp"
Dataset Summary
CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set have been manually corrected. The training set and development set from CoNLL2003 is included for completeness. One correction on the test set for example, is:
{
"tokens": ["SOCCER", "-", "JAPAN", "GET", "LUCKY", "WIN", ",", "CHINA", "IN", "SURPRISE", "DEFEAT", "."],
"original_ner_tags_in_conll2003": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-PER", "O", "O", "O", "O"],
"corrected_ner_tags_in_conllpp": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-LOC", "O", "O", "O", "O"],
}
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
conllpp
- Size of downloaded dataset files: 4.85 MB
- Size of the generated dataset: 10.26 MB
- Total amount of disk used: 15.11 MB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"id": "0",
"document_id": 1,
"sentence_id": 3,
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
}
Data Fields
The data fields are the same among all splits.
conllpp
id: astringfeature.document_id: anint32feature tracking which document the sample is from.sentence_id: anint32feature tracking which sentence in this document the sample is from.tokens: alistofstringfeatures.pos_tags: alistof classification labels, with possible values including"(0),''(1),#(2),$(3),((4).chunk_tags: alistof classification labels, with possible values includingO(0),B-ADJP(1),I-ADJP(2),B-ADVP(3),I-ADVP(4).ner_tags: alistof classification labels, with possible values includingO(0),B-PER(1),I-PER(2),B-ORG(3),I-ORG(4).
Data Splits
| name | train | validation | test |
|---|---|---|---|
| conll2003 | 14041 | 3250 | 3453 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@inproceedings{wang2019crossweigh,
title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={5157--5166},
year={2019}
}
Contributions
Thanks to @ZihanWangKi for adding this dataset.
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Homepage:
Github
Repository:
Github
Paper:
Aclweb
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Size of downloaded dataset files:
4.86 MB
Size of the auto-converted Parquet files:
1.86 MB
Number of rows:
20,744
Models trained or fine-tuned on tomaarsen/conllpp