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# 🪐 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` &rarr; `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. |

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### 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}
}
```
task_categories:token-classification task_ids:named-entity-recognition annotations_creators:expert-generated multilinguality:monolingual size_categories:1K<n<10K language:tl license:gpl-3.0 low-resource named-entity-recognition arxiv:2311.07161 doi:10.57967/hf/0969 region:us
{"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"], "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"}]}]}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Tagalog #license-gpl-3.0 #low-resource #named-entity-recognition #arxiv-2311.07161 #doi-10.57967/hf/0969 #region-us 

spaCy Project: TLUnified-NER Corpus
===================================


* Homepage: Github
* Repository: Github
* Point of Contact: ljvmiranda@URL


### 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).



### 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).


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 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., 'URL'). We're also shipping the IOB file so that it's
easier to access.


URL
---


The 'URL' defines the data assets required by the
project, as well as the available commands and workflows. For details, see the
spaCy projects documentation.


### ⏯ Commands


The following commands are defined by the project. They
can be executed using ['spacy project run [name]'](URL
Commands are only re-run if their inputs have changed.



### ⏭ Workflows


The following workflows are defined by the project. They
can be executed using ['spacy project run [name]'](URL
and will run the specified commands in order. Commands are only re-run if their
inputs have changed.



### Assets


The following assets are defined by the project. They can
be fetched by running 'spacy project assets'
in the project directory.


File: 'assets/URL', Source: URL, Description: Annotated TLUnified corpora in spaCy format with train, dev, and test splits.


You can cite this dataset as:



### 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). ### 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).


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 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., 'URL'). We're also shipping the IOB file so that it's
easier to access.


URL
---


The 'URL' defines the data assets required by the
project, as well as the available commands and workflows. For details, see the
spaCy projects documentation. ### ⏯ Commands


The following commands are defined by the project. They
can be executed using ['spacy project run [name]'](URL
Commands are only re-run if their inputs have changed. ### ⏭ Workflows


The following workflows are defined by the project. They
can be executed using ['spacy project run [name]'](URL
and will run the specified commands in order. Commands are only re-run if their
inputs have changed. ### Assets


The following assets are defined by the project. They can
be fetched by running 'spacy project assets'
in the project directory.


File: 'assets/URL', Source: URL, Description: Annotated TLUnified corpora in spaCy format with train, dev, and test splits.


You can cite this dataset as:
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Tagalog #license-gpl-3.0 #low-resource #named-entity-recognition #arxiv-2311.07161 #doi-10.57967/hf/0969 #region-us 
 ### 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). ### 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).


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 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., 'URL'). We're also shipping the IOB file so that it's
easier to access.


URL
---


The 'URL' defines the data assets required by the
project, as well as the available commands and workflows. For details, see the
spaCy projects documentation. ### ⏯ Commands


The following commands are defined by the project. They
can be executed using ['spacy project run [name]'](URL
Commands are only re-run if their inputs have changed. ### ⏭ Workflows


The following workflows are defined by the project. They
can be executed using ['spacy project run [name]'](URL
and will run the specified commands in order. Commands are only re-run if their
inputs have changed. ### Assets


The following assets are defined by the project. They can
be fetched by running 'spacy project assets'
in the project directory.


File: 'assets/URL', Source: URL, Description: Annotated TLUnified corpora in spaCy format with train, dev, and test splits.


You can cite this dataset as:
passage: TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Tagalog #license-gpl-3.0 #low-resource #named-entity-recognition #arxiv-2311.07161 #doi-10.57967/hf/0969 #region-us 
### 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).### 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).


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