add script

cngv1.py

@@ -0,0 +1,193 @@
+import os
+from pathlib import Path
+
+import datasets
+
+from bs4 import BeautifulSoup
+
+_CITATION = """\
+@article{ite2003corpas,
+  title={Corpas Náisiúnta na Gaeilge/National Corpus of Irish, Volume 1},
+  author={Institiúid Teangeolaíochta Éireann},
+  journal={Dublin: ITÉ},
+  year={2003}
+}
+"""
+
+_DESCRIPTION = """\
+Corpus of written Irish.
+"""
+
+_TEXTDIRS = [
+    "fiction", "information", "instruction", "non_fiction", "official"
+]
+
+class CNGDataset(datasets.GeneratorBasedBuilder):
+    """National Corpus of Irish."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="documents", version=VERSION, description="Plain text portion of the corpus: whole documents"),
+        datasets.BuilderConfig(name="paragraphs", version=VERSION, description="Plain text portion of the corpus: paragraphs"),
+        datasets.BuilderConfig(name="pos", version=VERSION, description="Part-of-speech tagging subset"),
+    ]
+
+    def _info(self):
+        if self.config.name == "documents" or self.config.name == "paragraphs":
+            features = datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "doc_id": datasets.Value("string"),
+                    "author": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "classes": datasets.Sequence(datasets.Value("string"))
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "doc_id": datasets.Value("string"),
+                    "author": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "classes": datasets.Sequence(datasets.Value("string")),
+                    "words": datasets.Sequence(datasets.Value("string")),
+                    "pos": datasets.Sequence(datasets.Value("string"))
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(manual_dir):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('jimregan/ncg', data_dir=...)` with the path to the corpus directory".format(
+                    manual_dir
+                )
+            )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": manual_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, data_dir, split
+    ):
+        """ Yields examples as (key, example) tuples. """
+
+        if self.config.name == "documents" or self.config.name == "paragraphs":
+            dirs = _TEXTDIRS
+        else:
+            dirs = ["pos"]
+
+        cng_path = Path(data_dir)
+
+        _id = 1
+        for dir in dirs:
+            dir_path = cng_path / dir
+            for filepath in dir_path.glob('*.SGM'):
+                with open(filepath, encoding="utf-16-le") as f:
+                    fid = filepath.stem
+                    content = f.read()
+                    soup = BeautifulSoup(content, 'html.parser')
+                    title = _get_title(soup)
+                    author = _get_author(soup)
+                    classes = _get_categories(content)
+                    date = _get_creation(soup)
+                    if self.config.name == "pos":
+                        for sent in _get_pos(soup):
+                            words = [tok["word"] for tok in sent]
+                            tags = [tok["msd"] for tok in sent]
+                            yield _id, {
+                                "title": title,
+                                "doc_id": fid,
+                                "author": author,
+                                "date": date,
+                                "classes": classes,
+                                "words": words,
+                                "pos": tags
+                            }
+                            _id += 1
+                    else:
+                        text = _get_paragraphs(soup)
+                        if self.config.name == "documents":
+                            text = ["\n".join(text)]
+                        for para in text:
+                            yield _id, {
+                                "title": title,
+                                "doc_id": fid,
+                                "author": author,
+                                "date": date,
+                                "classes": classes,
+                                "text": para
+                            }
+                            _id += 1
+
+                        
+def _get_title(soup):
+    title = soup.find("title")
+    if title.text and title.text.strip() != "":
+        return title.text.strip()
+
+
+def _get_author(soup):
+    author = soup.find("author")
+    if author.text and author.text.strip() != "":
+        return author.text.strip()
+
+
+def _get_creation(soup):
+    creation = soup.find("creation")
+    if creation.text and creation.text.strip() != "":
+        return creation.text.strip()
+
+
+def _get_paragraphs(soup):
+    import re
+    out = []
+    body = soup.find('body')
+    for p in body.find_all(['p', 'head']):
+        text = p.text.strip()
+        text = text.replace('\n', ' ')
+        text = re.sub('  +', ' ', text)
+        if text:
+            out.append(text)
+    return out
+
+
+def _get_categories(text):
+    import re
+    out = []
+    for cat in re.findall('<catRef target="([^"]+)">', text):
+        out.append(cat)
+    return out
+
+
+def _get_pos(soup):
+    out = []
+    for sent in soup.find_all('s'):
+        words = []
+        for word in sent.find_all('w'):
+            if word.text:
+                text = word.text.strip()
+            msd = word.get('msd')
+            if msd and text:
+                words.append({"msd": msd, "word": text})
+        out.append(words)
+    return out