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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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- aeb
+licenses:
+- lgpl-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card Creation Guide
+
+## 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:** None
+- **Repository:** https://github.com/fbougares/TSAC
+- **Paper:** https://www.aclweb.org/anthology/W17-1307
+- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
+- **Point of Contact:** Salima Mdhaffar (firstname.lastname@univ-lemans.fr)
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "Tunisian Sentiment Analysis Corpus.\n\nAbout 17k user comments manually annotated to positive and negative polarities. This corpus is collected from Facebook users comments written on official pages of Tunisian radios and TV channels namely Mosaique FM, JawhraFM, Shemes FM, HiwarElttounsi TV and Nessma TV. The corpus is collected from a period spanning January 2015 until June 2016.\n", "citation": "@inproceedings{medhaffar-etal-2017-sentiment,\n    title = \"Sentiment Analysis of {T}unisian Dialects: Linguistic Ressources and Experiments\",\n    author = \"Medhaffar, Salima  and\n      Bougares, Fethi  and\n      Est{\\`e}ve, Yannick  and\n      Hadrich-Belguith, Lamia\",\n    booktitle = \"Proceedings of the Third {A}rabic Natural Language Processing Workshop\",\n    month = apr,\n    year = \"2017\",\n    address = \"Valencia, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W17-1307\",\n    doi = \"10.18653/v1/W17-1307\",\n    pages = \"55--61\",\n    abstract = \"Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we are interested in the SA of the Tunisian Dialect. We utilize Machine Learning techniques to determine the polarity of comments written in Tunisian Dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian Dialect corpus of 17.000 comments from Facebook. This corpus allows us a significant accuracy improvement compared to the best model trained on other Arabic dialects or MSA data. We believe that this first freely available corpus will be valuable to researchers working in the field of Tunisian Sentiment Analysis and similar areas.\",\n}\n", "homepage": "https://github.com/fbougares/TSAC", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"num_classes": 2, "names": ["1", "-1"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tsac", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1020146, "num_examples": 13669, "dataset_name": "tsac"}, "test": {"name": "test", "num_bytes": 268504, "num_examples": 3400, "dataset_name": "tsac"}}, "download_checksums": {"https://raw.githubusercontent.com/fbougares/TSAC/master/train_pos.txt": {"num_bytes": 323280, "checksum": "1ddedc6fce42e9d3e09a5892088700a1c9065dcd2f1739983babdf964fc21556"}, "https://raw.githubusercontent.com/fbougares/TSAC/master/train_neg.txt": {"num_bytes": 434702, "checksum": "4babaf50959d556ea879a471acc3f5c0b794c6e606f31da8002633ab8b199668"}, "https://raw.githubusercontent.com/fbougares/TSAC/master/test_pos.txt": {"num_bytes": 84007, "checksum": "36e64d31103fb770988337b0a3a71340ac2187c568d52e9eec139df4571b7eb1"}, "https://raw.githubusercontent.com/fbougares/TSAC/master/test_neg.txt": {"num_bytes": 121026, "checksum": "41e8e829d1b74196ba82c03171e793ac641dd3c936de6a662d9fa399d438a3c6"}}, "download_size": 963015, "post_processing_size": null, "dataset_size": 1288650, "size_in_bytes": 2251665}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5a26266637c6be81b4ee6f4eb70de4c9b88cd8fd51240f68a87f7db327f5dc63
+size 1389

tsac.py

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+# coding=utf-8
+# Copyright 2020 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
+import datasets
+
+
+_DESCRIPTION = """\
+Tunisian Sentiment Analysis Corpus.
+
+About 17k user comments manually annotated to positive and negative polarities. This corpus is collected from Facebook users comments written on official pages of Tunisian radios and TV channels namely Mosaique FM, JawhraFM, Shemes FM, HiwarElttounsi TV and Nessma TV. The corpus is collected from a period spanning January 2015 until June 2016.
+"""
+_HOMEPAGE_URL = "https://github.com/fbougares/TSAC"
+_CITATION = """\
+@inproceedings{medhaffar-etal-2017-sentiment,
+    title = "Sentiment Analysis of {T}unisian Dialects: Linguistic Ressources and Experiments",
+    author = "Medhaffar, Salima  and
+      Bougares, Fethi  and
+      Est{`e}ve, Yannick  and
+      Hadrich-Belguith, Lamia",
+    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
+    month = apr,
+    year = "2017",
+    address = "Valencia, Spain",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/W17-1307",
+    doi = "10.18653/v1/W17-1307",
+    pages = "55--61",
+    abstract = "Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we are interested in the SA of the Tunisian Dialect. We utilize Machine Learning techniques to determine the polarity of comments written in Tunisian Dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian Dialect corpus of 17.000 comments from Facebook. This corpus allows us a significant accuracy improvement compared to the best model trained on other Arabic dialects or MSA data. We believe that this first freely available corpus will be valuable to researchers working in the field of Tunisian Sentiment Analysis and similar areas.",
+}
+"""
+
+_TRAIN_POS_URL = "https://raw.githubusercontent.com/fbougares/TSAC/master/train_pos.txt"
+_TRAIN_NEG_URL = "https://raw.githubusercontent.com/fbougares/TSAC/master/train_neg.txt"
+_TEST_POS_URL = "https://raw.githubusercontent.com/fbougares/TSAC/master/test_pos.txt"
+_TEST_NEG_URL = "https://raw.githubusercontent.com/fbougares/TSAC/master/test_neg.txt"
+
+
+class TSAC(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "target": datasets.ClassLabel(names=["1", "-1"]),
+                },
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_pos_path = dl_manager.download_and_extract(_TRAIN_POS_URL)
+        train_neg_path = dl_manager.download_and_extract(_TRAIN_NEG_URL)
+        test_pos_path = dl_manager.download_and_extract(_TEST_POS_URL)
+        test_neg_path = dl_manager.download_and_extract(_TEST_NEG_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"pospath": train_pos_path, "negpath": train_neg_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"pospath": test_pos_path, "negpath": test_neg_path},
+            ),
+        ]
+
+    def _generate_examples(self, pospath, negpath):
+        sentence_counter = 0
+
+        with open(pospath, encoding="utf-8") as f:
+            for row in f:
+                row = row.strip()
+                result = (
+                    sentence_counter,
+                    {
+                        "id": str(sentence_counter),
+                        "sentence": row,
+                        "target": "1",
+                    },
+                )
+                yield result
+                sentence_counter += 1
+
+        with open(negpath, encoding="utf-8") as f:
+            for row in f:
+                row = row.strip()
+                result = (
+                    sentence_counter,
+                    {
+                        "id": str(sentence_counter),
+                        "sentence": row,
+                        "target": "-1",
+                    },
+                )
+                yield result
+                sentence_counter += 1