Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- other
+languages:
+- pl
+licenses:
+- bsd-3-clause
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:**
+  https://clarin-pl.eu/dspace/handle/11321/710
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Polish
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- sentence: string, the review
+- target: sentiment of the sentence class
+
+The same tag system is used in plWordNet Emo for lexical units: [+m] (strong positive), [+s] (weak positive), [-m] (strong negative), [-s] (weak negative), [amb] (ambiguous) and [0] (neutral).
+
+Note that the test set doesn't have targets so -1 is used instead
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+CC BY-NC-SA 4.0
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"in": {"description": "The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.\n", "citation": "@inproceedings{kocon-etal-2019-multi,\ntitle = \"Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews\",\nauthor = \"Koco{'n}, Jan and\nMi{\\l}kowski, Piotr and\nZa{'s}ko-Zieli{'n}ska, Monika\",\nbooktitle = \"Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)\",\nmonth = nov,\nyear = \"2019\",\naddress = \"Hong Kong, China\",\npublisher = \"Association for Computational Linguistics\",\nurl = \"https://www.aclweb.org/anthology/K19-1092\",\ndoi = \"10.18653/v1/K19-1092\",\npages = \"980--991\",\n}\n", "homepage": "https://clarin-pl.eu/dspace/handle/11321/710", "license": "CC BY-NC-SA 4.0", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"num_classes": 4, "names": ["__label__meta_amb", "__label__meta_minus_m", "__label__meta_plus_m", "__label__meta_zero"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "polemo2", "config_name": "in", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4810215, "num_examples": 5783, "dataset_name": "polemo2"}, "test": {"name": "test", "num_bytes": 582052, "num_examples": 722, "dataset_name": "polemo2"}, "validation": {"name": "validation", "num_bytes": 593530, "num_examples": 723, "dataset_name": "polemo2"}}, "download_checksums": {"https://klejbenchmark.com/static/data/klej_polemo2.0-in.zip": {"num_bytes": 2350339, "checksum": "ec9ccfa232686081577e6c250c79c028411076e84db60d4cd192f9a567a2cb96"}}, "download_size": 2350339, "post_processing_size": null, "dataset_size": 5985797, "size_in_bytes": 8336136}, "out": {"description": "The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.\n", "citation": "@inproceedings{kocon-etal-2019-multi,\ntitle = \"Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews\",\nauthor = \"Koco{'n}, Jan and\nMi{\\l}kowski, Piotr and\nZa{'s}ko-Zieli{'n}ska, Monika\",\nbooktitle = \"Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)\",\nmonth = nov,\nyear = \"2019\",\naddress = \"Hong Kong, China\",\npublisher = \"Association for Computational Linguistics\",\nurl = \"https://www.aclweb.org/anthology/K19-1092\",\ndoi = \"10.18653/v1/K19-1092\",\npages = \"980--991\",\n}\n", "homepage": "https://clarin-pl.eu/dspace/handle/11321/710", "license": "CC BY-NC-SA 4.0", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"num_classes": 4, "names": ["__label__meta_amb", "__label__meta_minus_m", "__label__meta_plus_m", "__label__meta_zero"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "polemo2", "config_name": "out", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4810215, "num_examples": 5783, "dataset_name": "polemo2"}, "test": {"name": "test", "num_bytes": 309790, "num_examples": 494, "dataset_name": "polemo2"}, "validation": {"name": "validation", "num_bytes": 310977, "num_examples": 494, "dataset_name": "polemo2"}}, "download_checksums": {"https://klejbenchmark.com/static/data/klej_polemo2.0-out.zip": {"num_bytes": 2139891, "checksum": "202668a59ce18cf476a7d3a8c76a802fe1eeaa869caa687313c43246988046ba"}}, "download_size": 2139891, "post_processing_size": null, "dataset_size": 5430982, "size_in_bytes": 7570873}}

dummy/in/1.1.0/dummy_data.zip

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dummy/out/1.1.0/dummy_data.zip

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+size 4647

polemo2.py

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+# coding=utf-8
+# 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.
+"""PolEmo2.0 IN and OUT"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_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
+Milkowski, 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",
+}
+"""
+
+_DESCRIPTION = """\
+The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.
+"""
+
+_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/710"
+
+_LICENSE = "CC BY-NC-SA 4.0"
+
+_URLs = {
+    "in": "https://klejbenchmark.com/static/data/klej_polemo2.0-in.zip",
+    "out": "https://klejbenchmark.com/static/data/klej_polemo2.0-out.zip",
+}
+
+
+class Polemo2(datasets.GeneratorBasedBuilder):
+    """PolEmo2.0"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="in",
+            version=VERSION,
+            description="The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.",
+        ),
+        datasets.BuilderConfig(
+            name="out",
+            version=VERSION,
+            description="The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "in"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "target": datasets.ClassLabel(
+                        names=[
+                            "__label__meta_amb",
+                            "__label__meta_minus_m",
+                            "__label__meta_plus_m",
+                            "__label__meta_zero",
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.tsv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "dev.tsv"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for id_, row in enumerate(reader):
+                yield id_, {
+                    "sentence": row["sentence"],
+                    "target": -1 if split == "test" else row["target"],
+                }