Convert dataset to Parquet (#3)

- Convert dataset to Parquet (4f42950b88068daffdfeb6cdaddefcdf1766e410)
- Delete data file (eec29dee86fa696deab9d501d43670114d92d371)
- Delete data file (7bc81e1325c40461e46868f1996094c3562b293f)
- Delete loading script auxiliary file (ceed5c6fd35283875b53ac57159550e7b1a9dd44)
- Delete loading script (414fb68177d0be7e9cbf949ae10f71b734ae64c1)
- Delete data file (8d31d9744b24fd1199b78eb963d8c5c89f9feabb)
- Delete data file (d96e585635d36d47896f5787327520a18c7759b4)
- Delete data file (ab5b4d4f2884fa55d8b9db412cb0f2bebe6a3cf4)
- Delete data file (1878ee08cf261063cac413f699e7c1618e2b4123)

.gitignore

@@ -1,4 +0,0 @@
-assets
-corpus/spacy
-__pycache__/
-project.lock

README.md

@@ -1,34 +1,72 @@
 ---
+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
-language:
-- tl
-size_categories:
-- 1K<n<10K
 pretty_name: TLUnified-NER
 tags:
 - low-resource
 - named-entity-recognition
-annotations_creators:
-- expert-generated
-multilinguality:
-- monolingual
+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
+- 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) -->

corpus/iob/dev.iob → data/test-00000-of-00001.parquet

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-size 217710
+oid sha256:50602b8a71d436d297398adeeb4209b2306df63f54fccfbcfac1cd502c654252
+size 101856

corpus/iob/test.iob → data/train-00000-of-00001.parquet

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-size 216121
+oid sha256:f64e43b6019ae35b4055371b89c12b180510152893155975427d6946d6678a61
+size 767881

corpus/iob/train.iob → data/validation-00000-of-00001.parquet

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-size 1715843
+oid sha256:ea9bb253e5e6b9827b7e47dfb31edb27d5eafec223cecc3decd13ae2017576c6
+size 101302

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

requirements.txt

@@ -1,5 +0,0 @@
-spacy
-typer
-datasets
-huggingface_hub
-wasabi

spacy_to_iob.py

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-from pathlib import Path
-
-import spacy
-import typer
-from spacy.tokens import DocBin
-from wasabi import msg
-
-DELIMITER = "-DOCSTART- -X- O O"
-
-
-def spacy_to_iob(
-    # fmt: off
-    spacy_indir: Path = typer.Argument(..., help="Path to the directory containing the spaCy files."),
-    iob_outdir: Path = typer.Argument(..., help="Path to the directory to save the IOB files."),
-    lang: str = typer.Option("tl", "-l", "--lang", help="Language code for the spaCy vocab."),
-    verbose: bool = typer.Option(False, "-v", "--verbose", help="Print additional information."),
-    delimiter: str = typer.Option(DELIMITER, "-d", "--delimiter", help="Delimiter between examples.")
-    # fmt: on
-):
-    """Convert spaCy files into IOB-formatted files."""
-    nlp = spacy.blank(lang)
-    for spacy_file in spacy_indir.glob(f"*.spacy"):
-        msg.text(f"Converting {str(spacy_file)}", show=verbose)
-        doc_bin = DocBin().from_disk(spacy_file)
-        docs = doc_bin.get_docs(nlp.vocab)
-
-        lines = []  # container for the IOB lines later on
-        for doc in docs:
-            lines.append(delimiter)
-            lines.append("\n\n")
-            for token in doc:
-                label = (
-                    f"{token.ent_iob_}-{token.ent_type_}"
-                    if token.ent_iob_ != "O"
-                    else "O"
-                )
-                line = f"{token.text}\t{label}"
-                lines.append(line)
-                lines.append("\n")
-            lines.append("\n")
-
-        iob_file = iob_outdir / f"{spacy_file.stem}.iob"
-        with open(iob_file, "w", encoding="utf-8") as f:
-            f.writelines(lines)
-
-        msg.good(f"Saved to {iob_file}")
-
-
-if __name__ == "__main__":
-    typer.run(spacy_to_iob)

tlunified-ner.py

@@ -1,95 +0,0 @@
-from typing import List
-
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = """
-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).
-"""
-_LICENSE = """GNU GPL v3.0"""
-_URL = "https://huggingface.co/ljvmiranda921/tlunified-ner"
-_CLASSES = ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
-_VERSION = "1.0.0"
-
-
-class TLUnifiedNERConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(TLUnifiedNER, self).__init__(**kwargs)
-
-
-class TLUnifiedNER(datasets.GeneratorBasedBuilder):
-    """Contains an annotated version of the TLUnified dataset from Cruz and Cheng (2021)."""
-
-    VERSION = datasets.Version(_VERSION)
-
-    def _info(self) -> "datasets.DatasetInfo":
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(names=_CLASSES)
-                    ),
-                }
-            ),
-            homepage=_URL,
-            supervised_keys=None,
-        )
-
-    def _split_generators(
-        self, dl_manager: "datasets.builder.DownloadManager"
-    ) -> List["datasets.SplitGenerator"]:
-        """Return a list of SplitGenerators that organizes the splits."""
-        # The file extracts into {train,dev,test}.spacy files. The _generate_examples function
-        # below will define how these files are parsed.
-        data_files = {
-            "train": dl_manager.download_and_extract("corpus/iob/train.iob"),
-            "dev": dl_manager.download_and_extract("corpus/iob/dev.iob"),
-            "test": dl_manager.download_and_extract("corpus/iob/test.iob"),
-        }
-
-        return [
-            # fmt: off
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
-            # fmt: on
-        ]
-
-    def _generate_examples(self, filepath: str):
-        """Defines how examples are parsed from the IOB file."""
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            guid = 0
-            tokens = []
-            ner_tags = []
-            for line in f:
-                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        ner_tags = []
-                else:
-                    # TLUnified-NER iob are separated by \t
-                    token, ner_tag = line.split("\t")
-                    tokens.append(token)
-                    ner_tags.append(ner_tag.rstrip())
-            # Last example
-            if tokens:
-                yield guid, {
-                    "id": str(guid),
-                    "tokens": tokens,
-                    "ner_tags": ner_tags,
-                }