Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Tagalog
Size:
1K - 10K
ArXiv:
DOI:
License:
File size: 5,252 Bytes
3281e02 371f072 3281e02 371f072 3281e02 790e34d 3281e02 371f072 790e34d 371f072 3281e02 1b0f91f 790e34d 1b0f91f 790e34d 1b0f91f 790e34d 3281e02 53d8523 3281e02 1b0f91f 790e34d 3281e02 1b0f91f 3281e02 1b0f91f 442fac5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
---
annotations_creators:
- expert-generated
language:
- tl
license: gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TLUnified-NER
tags:
- low-resource
- named-entity-recognition
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
splits:
- name: train
num_bytes: 3380392
num_examples: 6252
- name: validation
num_bytes: 427069
num_examples: 782
- name: test
num_bytes: 426247
num_examples: 782
download_size: 971039
dataset_size: 4233708
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
train-eval-index:
- config: conllpp
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
<!-- SPACY PROJECT: AUTO-GENERATED DOCS START (do not remove) -->
# 🪐 spaCy Project: TLUnified-NER Corpus
- **Homepage:** [Github](https://github.com/ljvmiranda921/calamanCy)
- **Repository:** [Github](https://github.com/ljvmiranda921/calamanCy)
- **Point of Contact:** ljvmiranda@gmail.com
### Dataset Summary
This dataset contains the annotated TLUnified corpora from Cruz and Cheng
(2021). It is a curated sample of around 7,000 documents for the
named entity recognition (NER) task. The majority of the corpus are news
reports in Tagalog, resembling the domain of the original ConLL 2003. There
are three entity types: Person (PER), Organization (ORG), and Location (LOC).
| Dataset | Examples | PER | ORG | LOC |
|-------------|----------|------|------|------|
| Train | 6252 | 6418 | 3121 | 3296 |
| Development | 782 | 793 | 392 | 409 |
| Test | 782 | 818 | 423 | 438 |
### Data Fields
The data fields are the same among all splits:
- `id`: a `string` feature
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4), `B-LOC` (5), `I-LOC` (6)
### Annotation process
The author, together with two more annotators, labeled curated portions of
TLUnified in the course of four months. All annotators are native speakers of
Tagalog. For each annotation round, the annotators resolved disagreements,
updated the annotation guidelines, and corrected past annotations. They
followed the process prescribed by [Reiters
(2017)](https://nilsreiter.de/blog/2017/howto-annotation).
They also measured the inter-annotator agreement (IAA) by computing pairwise
comparisons and averaging the results:
- Cohen's Kappa (all tokens): 0.81
- Cohen's Kappa (annotated tokens only): 0.65
- F1-score: 0.91
### About this repository
This repository is a [spaCy project](https://spacy.io/usage/projects) for
converting the annotated spaCy files into IOB. The process goes like this: we
download the raw corpus from Google Cloud Storage (GCS), convert the spaCy
files into a readable IOB format, and parse that using our loading script
(i.e., `tlunified-ner.py`). We're also shipping the IOB file so that it's
easier to access.
## 📋 project.yml
The [`project.yml`](project.yml) defines the data assets required by the
project, as well as the available commands and workflows. For details, see the
[spaCy projects documentation](https://spacy.io/usage/projects).
### ⏯ Commands
The following commands are defined by the project. They
can be executed using [`spacy project run [name]`](https://spacy.io/api/cli#project-run).
Commands are only re-run if their inputs have changed.
| Command | Description |
| --- | --- |
| `setup-data` | Prepare the Tagalog corpora used for training various spaCy components |
| `upload-to-hf` | Upload dataset to HuggingFace Hub |
### ⏭ Workflows
The following workflows are defined by the project. They
can be executed using [`spacy project run [name]`](https://spacy.io/api/cli#project-run)
and will run the specified commands in order. Commands are only re-run if their
inputs have changed.
| Workflow | Steps |
| --- | --- |
| `all` | `setup-data` → `upload-to-hf` |
### 🗂 Assets
The following assets are defined by the project. They can
be fetched by running [`spacy project assets`](https://spacy.io/api/cli#project-assets)
in the project directory.
| File | Source | Description |
| --- | --- | --- |
| `assets/corpus.tar.gz` | URL | Annotated TLUnified corpora in spaCy format with train, dev, and test splits. |
<!-- SPACY PROJECT: AUTO-GENERATED DOCS END (do not remove) -->
### Citation
You can cite this dataset as:
```
@misc{miranda2023developing,
title={Developing a Named Entity Recognition Dataset for Tagalog},
author={Lester James V. Miranda},
year={2023},
eprint={2311.07161},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |