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project.yml

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-title: "TLUnified-NER Corpus"
-description: |
-
-  - **Homepage:** [Github](https://github.com/ljvmiranda921/calamanCy)
-  - **Repository:** [Github](https://github.com/ljvmiranda921/calamanCy)
-  - **Point of Contact:** [email protected]
-
-  ### 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.
-
-directories: ["assets", "corpus/spacy", "corpus/iob"]
-
-vars:
-  version: 1.0
-
-assets:
-  - dest: assets/corpus.tar.gz
-    description: "Annotated TLUnified corpora in spaCy format with train, dev, and test splits."
-    url: "https://storage.googleapis.com/ljvmiranda/calamanCy/tl_tlunified_gold/v${vars.version}/corpus.tar.gz"
-
-workflows:
-  all:
-    - "setup-data"
-    - "upload-to-hf"
-
-commands:
-  - name: "setup-data"
-    help: "Prepare the Tagalog corpora used for training various spaCy components"
-    script:
-      - mkdir -p corpus/spacy
-      - tar -xzvf assets/corpus.tar.gz -C corpus/spacy
-      - python -m spacy_to_iob corpus/spacy/ corpus/iob/
-    outputs:
-      - corpus/iob/train.iob
-      - corpus/iob/dev.iob
-      - corpus/iob/test.iob
-
-  - name: "upload-to-hf"
-    help: "Upload dataset to HuggingFace Hub"
-    script:
-      - git push
-    deps:
-      - corpus/iob/train.iob
-      - corpus/iob/dev.iob
-      - corpus/iob/test.iob