Datasets:

clarin-pl
/

polemo2-official

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polemo2-official.py ADDED Viewed

	@@ -0,0 +1,173 @@

+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+# Lint as: python3
+"""PolEmo2 dataset."""
+from dataclasses import dataclass
+from typing import List, Dict, Generator, Union, Optional, Tuple
+import datasets
+_DESCRIPTION = """PolEmo2 dataset."""
+_CITATION = """
+@inproceedings{kocon-etal-2019-multi,
+    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
+    author = "Koco{\'n}, Jan  and
+      Mi{\l}kowski, Piotr  and
+      Za{\'s}ko-Zieli{\'n}ska, Monika",
+    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/K19-1092",
+    doi = "10.18653/v1/K19-1092",
+    pages = "980--991",}
+"""
+_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/710"
+_DOMAINS = [
+    "all",
+    "hotels",
+    "medicine",
+    "products",
+    "reviews",
+]
+_OUT_DOMAINS = ["Nhotels", "Nmedicine", "Nproducts", "Nreviews"]
+_CONFIGS_TEXT = ["text", "sentence"]
+_LABELS = ["zero", "minus", "plus", "amb"]
+URL_PATH = (
+    "https://huggingface.co/datasets/clarin-pl/polemo2-official/resolve/main/data"
+)
+_URLS = {
+    cfg: {
+        **{
+            domain: {
+                split_type: f"{URL_PATH}/{domain}.{cfg}.{split_type}.txt"
+                for split_type in ["train", "dev", "test"]
+            }
+            for domain in _DOMAINS
+        },
+        **{
+            domain: {
+                split_type: f"{URL_PATH}/{domain}.{cfg}.{split_type}.txt"
+                for split_type in ["train", "dev"]
+            }
+            for domain in _OUT_DOMAINS
+        },
+    }
+    for cfg in _CONFIGS_TEXT
+}
+@dataclass
+class PolEmo2Config(datasets.BuilderConfig):
+    text_cfg: Optional[str] = None
+    domain: Optional[str] = None
+    train_domains: Optional[List[str]] = None
+    dev_domains: Optional[List[str]] = None
+    test_domains: Optional[List[str]] = None
+class PolEmo2(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = PolEmo2Config
+    BUILDER_CONFIGS = [
+        *[
+            PolEmo2Config(
+                name=f"{domain}_{text_type}",
+                domain=domain,
+                text_cfg=text_type,
+                train_domains=[domain],
+                dev_domains=[domain],
+                test_domains=[domain],
+            )
+            for domain in _DOMAINS
+            for text_type in _CONFIGS_TEXT
+        ]
+    ]
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "target": datasets.features.ClassLabel(
+                        names=_LABELS, num_classes=len(_LABELS)
+                    ),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+    def _get_files_by_domains(self, domains: List[str], split: str) -> List[str]:
+        return [_URLS[self.config.text_cfg][domain][split] for domain in domains]
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        files = {
+            "train": dl_manager.download_and_extract(
+                self._get_files_by_domains(
+                    domains=self.config.train_domains, split="train"
+                )
+            ),
+            "dev": dl_manager.download_and_extract(
+                self._get_files_by_domains(domains=self.config.dev_domains, split="dev")
+            ),
+            "test": dl_manager.download_and_extract(
+                self._get_files_by_domains(
+                    domains=self.config.test_domains, split="test"
+                )
+            ),
+        }
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": files["dev"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": files["test"]},
+            ),
+        ]
+    def _generate_examples(
+        self, filepath: Union[str, List[str]]
+    ) -> Generator[Tuple[int, Dict[str, str]], None, None]:
+        gid = 0
+        for path in filepath:
+            with open(path, "r") as f:
+                for line in f:
+                    splitted_line = line.split(" ")
+                    yield gid, {
+                        "text": " ".join(splitted_line[:-1]),
+                        "target": (
+                            splitted_line[-1]
+                            .strip()
+                            .replace("minus_m", "minus")
+                            .replace("plus_m", "plus")
+                            .split("_")[-1]
+                        ),
+                    }
+                    gid += 1