Upload clmet_3_1.py

Dataloader for clmet_3_1 dataset

clmet_3_1.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, 
+Susanne Flach, Hans-Jürgen Diller and Jukka Tyrkkö, as an offshoot of a bigger project developing a database
+ of text descriptors (Diller, De Smet & Tyrkkö 2011). CLMET3.1 is a principled collection of public domain 
+ texts drawn from various online archiving projects. """
+
+import os
+import xml.etree.ElementTree as ET
+import datasets
+from bs4 import BeautifulSoup
+
+
+_CITATION = """@article{de2015corpus,
+  title={Corpus of Late Modern English texts (version 3.1)},
+  author={De Smet, Hendrik and Flach, Susanne and Tyrkk{\"o}, Jukka and Diller, Hans-J{\"u}rgen},
+  year={2015}
+}
+"""
+
+_DESCRIPTION = """The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, 
+Susanne Flach, Hans-Jürgen Diller and Jukka Tyrkkö, as an offshoot of a bigger project developing a database of text 
+descriptors (Diller, De Smet & Tyrkkö 2011). CLMET3.1 is a principled collection of public domain texts drawn from 
+various online archiving projects. This dataset can be used for part-of-speech tagging, NER and text classification
+"""
+
+_HOMEPAGE = "http://fedora.clarin-d.uni-saarland.de/clmet/clmet.html"
+
+_LICENSE = "Creative Commons Attribution Non Commercial Share Alike 4.0 International"
+
+_DATASETNAME = "clmet"
+
+_URLS = {
+    _DATASETNAME: "http://fedora.clarin-d.uni-saarland.de/clmet/clmet3_1.zip",
+}
+
+_POS_LIST = [
+    "CC",
+    "CD",
+    "DT",
+    "EX",
+    "FW",
+    "IN",
+    "JJ",
+    "JJR",
+    "JJS",
+    "MD",
+    "NN",
+    "NNS",
+    "NP",
+    "NPS",
+    "PDT",
+    "POS",
+    "PP",
+    "PP$",
+    "RB",
+    "RBR",
+    "RBS",
+    "RP",
+    "SENT",
+    "SYM",
+    "TO",
+    "UH",
+    "VB",
+    "VBD",
+    "VBG",
+    "VBN",
+    "VBZ",
+    "VBP",
+    "WDT",
+    "WP",
+    "WP$",
+    "WRB",
+    "XX0",
+    "CURR",
+    "PUN",
+    "LQUO",
+    "RQUO",
+    "BRL",
+    "BRR",
+    "LS",
+]
+_POS_LOOKUP = {tag: idx for idx, tag in enumerate(_POS_LIST)}
+_CLASS_LIST = [
+    "ADJ",
+    "ADV",
+    "ART",
+    "CONJ",
+    "INTJ",
+    "PREP",
+    "PRON",
+    "PUNC",
+    "SUBST",
+    "SYM",
+    "UNC",
+    "VERB",
+    "QUOT"
+]
+_CLASS_LOOKUP = {tag: idx for idx, tag in enumerate(_CLASS_LIST)}
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+class CLMET_3_1(datasets.GeneratorBasedBuilder):
+    """"""
+
+    VERSION = datasets.Version("3.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain",
+            version=VERSION,
+            description="This format contains text as single string and the classifications",
+        ),
+        datasets.BuilderConfig(
+            name="class",
+            version=VERSION,
+            description="This format contains the text as a list of tokens, annotated according to the simplified Oxford wordclass tags",
+        ),
+        datasets.BuilderConfig(
+            name="pos",
+            version=VERSION,
+            description="This format contains the text as a list of tokens, annotated according to the Penn Treebank POS tags",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "plain"
+
+    def _info(self):
+        if self.config.name == "plain":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "genre": datasets.Value("string"),
+                    "subgenre": datasets.Value("string"),
+                    "year": datasets.Value("string"),
+                    "quarter_cent": datasets.Value("string"),
+                    "decade": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "author": datasets.Value("string"),
+                    "notes": datasets.Value("string"),
+                    "comments": datasets.Value("string"),
+                    "period": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "class":
+            logger.warn(f"CLASS tags are as follows: {_CLASS_LIST}")
+            features = datasets.Features(
+                {
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(datasets.Value("int32")),
+                    "genre": datasets.Value("string"),
+                    "subgenre": datasets.Value("string"),
+                    "year": datasets.Value("string"),
+                    "quarter_cent": datasets.Value("string"),
+                    "decade": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "author": datasets.Value("string"),
+                    "notes": datasets.Value("string"),
+                    "comments": datasets.Value("string"),
+                    "period": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "pos":
+            logger.warn(f"POS tags are as follows: {_POS_LIST}")
+            features = datasets.Features(
+                {
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(datasets.Value("int32")),
+                    "genre": datasets.Value("string"),
+                    "subgenre": datasets.Value("string"),
+                    "year": datasets.Value("string"),
+                    "quarter_cent": datasets.Value("string"),
+                    "decade": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "author": datasets.Value("string"),
+                    "notes": datasets.Value("string"),
+                    "comments": datasets.Value("string"),
+                    "period": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+        data_dir = os.path.join(data_dir, "clmet", "corpus", "txt")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def parse_pos_text(self, content_parts, pos_type):
+        tokens = []
+        pos_tags = []
+        unknown_tag = False
+        malformed_token = False
+        for content_part in content_parts:
+            text = content_part.text.strip()
+            for text_part in text.split():
+                try:
+                    token, pos_tag = text_part.split("_")
+                    pos_tag = pos_tag.replace("\n", "").strip().upper()
+                    if pos_type == "pos":
+                        pos_tag_idx = _POS_LOOKUP.get(pos_tag,-1)
+                    else:
+                        pos_tag_idx = _CLASS_LOOKUP.get(pos_tag,-1)
+                    if pos_tag_idx==-1:
+                        unknown_tag = True
+                    tokens.append(token)
+                    pos_tags.append(pos_tag_idx)
+                except Exception as e:
+                    malformed_token = True
+        return tokens, pos_tags, unknown_tag, malformed_token
+
+    def parse_file(self, file, pos_type):
+        with open(file, "r", encoding="utf-8") as fp:
+            soup = BeautifulSoup(fp, features="html.parser")
+            id = soup.id.text
+            period = soup.period.text
+            quarter_cent = soup.quartcent.text
+            decade = soup.decade.text
+            year = soup.year.text
+            genre = soup.genre.text
+            subgenre = soup.subgenre.text
+            title = soup.title.text
+            notes = soup.notes.text
+            comments = soup.comments.text
+            author = soup.author.text
+            data_point = {
+                "id": id,
+                "period": period,
+                "genre": genre,
+                "subgenre": subgenre,
+                "decade": decade,
+                "quarter_cent": quarter_cent,
+                "title": title,
+                "notes": notes if notes else "",
+                "comments": comments if comments else "",
+                "author": author,
+                "year": year,
+            }
+            content_parts = soup.find("text").find_all("p")
+
+            if pos_type in ["pos", "class"]:
+                content = self.parse_pos_text(content_parts, pos_type)
+                if content[2]:
+                    logger.warn(f'Unknown tag in sample {id}')
+                if content[3]:
+                    logger.warn(f'Malformed token in sample {id}')
+                data_point["text"] = content[0]
+                data_point["pos_tags"] = content[1]
+            else:
+                content = []
+                for content_part in content_parts:
+                    content.append(content_part.text)
+                content = " ".join(content)
+                data_point["text"] = content
+            return (id, data_point)
+
+    def _generate_examples(self, data_dir, split):
+        final_data_dir = os.path.join(data_dir, self.config.name)
+        for file in os.listdir(final_data_dir):
+            id, data = self.parse_file(
+                os.path.join(final_data_dir, file), self.config.name
+            )
+            yield id, data