Add first dataset script

README.md

@@ -1,3 +1,31 @@
 ---
 license: cc-by-nc-sa-4.0
+dataset_info:
+  features:
+  - name: id_doc
+    dtype: string
+  - name: words
+    sequence:
+      sequence: string
+  - name: mentions
+    list:
+    - name: id_mention
+      dtype: string
+    - name: mention_data
+      struct:
+      - name: idx_sent
+        dtype: uint32
+      - name: word_indices
+        sequence: uint32
+      - name: global_word_indices
+        sequence: uint32
+  - name: coref_clusters
+    sequence:
+      sequence: string
+  splits:
+  - name: train
+    num_bytes: 413196
+    num_examples: 149
+  download_size: 463706
+  dataset_size: 413196
 ---

coref149.py

@@ -0,0 +1,135 @@
+""" Slovene corpus for coreference resolution coref149. """
+
+
+import os
+import xml.etree.ElementTree as ET
+import datasets
+
+
+_CITATION = """\
+@article{coref149, 
+	author={Žitnik, Slavko and Bajec, Marko},
+	title={Odkrivanje koreferenčnosti v slovenskem jeziku na označenih besedilih iz coref149},
+	journal={Slovenščina 2.0: empirične, aplikativne in interdisciplinarne raziskave},
+	number={1},
+	volume={6},
+	year={2018},
+	month={Jun.},
+	pages={37–67},
+	doi={10.4312/slo2.0.2018.1.37-67}
+}
+"""
+
+_DESCRIPTION = """\
+Slovene corpus for coreference resolution. Contains manually annotated coreferences.
+"""
+
+_HOMEPAGE = "http://hdl.handle.net/11356/1182"
+
+_LICENSE = "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"
+
+_URLS = {
+	"coref149": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1182/coref149_v1.0.zip"
+}
+
+
+class Coref149(datasets.GeneratorBasedBuilder):
+	"""Slovene corpus for coreference resolution."""
+
+	VERSION = datasets.Version("1.0.0")
+
+	def _info(self):
+		features = datasets.Features(
+			{
+				"id_doc": datasets.Value("string"),
+				"words": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+				"mentions": [{
+					"id_mention": datasets.Value("string"),
+					"mention_data": {
+						"idx_sent": datasets.Value("uint32"),
+						"word_indices": datasets.Sequence(datasets.Value("uint32")),
+						"global_word_indices": datasets.Sequence(datasets.Value("uint32"))
+					}
+				}],
+				"coref_clusters": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
+			}
+		)
+
+		return datasets.DatasetInfo(
+			description=_DESCRIPTION,
+			features=features,
+			homepage=_HOMEPAGE,
+			license=_LICENSE,
+			citation=_CITATION,
+		)
+
+	def _split_generators(self, dl_manager):
+		urls = _URLS["coref149"]
+		data_dir = dl_manager.download_and_extract(urls)
+		return [
+			datasets.SplitGenerator(
+				name=datasets.Split.TRAIN,
+				gen_kwargs={
+					"data_dir": data_dir
+				}
+			)
+		]
+
+	def _generate_examples(self, data_dir):
+		TC_NAMESPACE = "{http://www.dspin.de/data/textcorpus}"
+		all_files = sorted([fname for fname in os.listdir(data_dir) if fname.endswith(".tcf")],
+						   key=lambda _fname: int(_fname.split(".")[-2]))
+
+		for idx_file, curr_fname in enumerate(all_files):
+			curr_doc = ET.parse(os.path.join(data_dir, curr_fname))
+			root = curr_doc.getroot()
+			id_doc = curr_fname.split(os.path.sep)[-1]
+
+			token_tags = root.findall(f".//{TC_NAMESPACE}token")
+			id2tok, id2idx, id2globidx, id2sentidx = {}, {}, {}, {}
+			for idx_global, token in enumerate(token_tags):
+				id_token = token.attrib["ID"]
+				text_token = token.text.strip()
+
+				id2tok[id_token] = text_token
+				id2globidx[id_token] = idx_global
+
+			sent_tags = root.findall(f".//{TC_NAMESPACE}sentence")
+			words = []
+			for idx_sent, sent in enumerate(sent_tags):
+				token_ids = sent.attrib["tokenIDs"].split(" ")
+				for local_position, _id_tok in enumerate(token_ids):
+					id2sentidx[_id_tok] = idx_sent
+					id2idx[_id_tok] = local_position
+				words.append([id2tok[_id] for _id in token_ids])
+
+			mentions, clusters = [], []
+			for ent in root.findall(f".//{TC_NAMESPACE}entity"):
+				curr_cluster = []
+				for ref in ent.findall(f"{TC_NAMESPACE}reference"):
+					id_mention = f"{id_doc}.{ref.attrib['ID']}"
+					curr_cluster.append(id_mention)
+					curr_mention = {
+						"id_mention": id_mention,
+						"mention_data": {
+							"idx_sent": None,
+							"word_indices": [],
+							"global_word_indices": []
+						}
+					}
+
+					for id_token in ref.attrib['tokenIDs'].split(" "):
+						curr_mention["mention_data"]["idx_sent"] = id2sentidx[id_token]
+						curr_mention["mention_data"]["word_indices"].append(id2idx[id_token])
+						curr_mention["mention_data"]["global_word_indices"].append(id2globidx[id_token])
+
+					mentions.append(curr_mention)
+
+				clusters.append(curr_cluster)
+
+			yield idx_file, {
+				"id_doc": id_doc,
+				"words": words,
+				"mentions": mentions,
+				"coref_clusters": clusters
+			}