Update files from the datasets library (from 1.8.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

ajgt_twitter_ar.py

@@ -23,6 +23,7 @@ import openpyxl  # noqa: requires this pandas optional dependency for reading xl
 import pandas as pd
 
 import datasets
+from datasets.tasks import TextClassification
 
 
 _DESCRIPTION = """\
@@ -83,6 +84,7 @@ class AjgtTwitterAr(datasets.GeneratorBasedBuilder):
             supervised_keys=None,
             homepage="https://github.com/komari6/Arabic-twitter-corpus-AJGT",
             citation=_CITATION,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
         )
 
     def _split_generators(self, dl_manager):

dataset_infos.json

@@ -1 +1 @@
-{"plain_text": {"description": "Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.\n", "citation": "@inproceedings{alomari2017arabic,\n  title={Arabic tweets sentimental analysis using machine learning},\n  author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},\n  booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},\n  pages={602--610},\n  year={2017},\n  organization={Springer}\n}\n", "homepage": "https://github.com/komari6/Arabic-twitter-corpus-AJGT", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["Negative", "Positive"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ajgt_twitter_ar", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 175424, "num_examples": 1800, "dataset_name": "ajgt_twitter_ar"}}, "download_checksums": {"https://raw.githubusercontent.com/komari6/Arabic-twitter-corpus-AJGT/master/AJGT.xlsx": {"num_bytes": 107395, "checksum": "966c52213872b6b8a3ced5fb7c60aee2abf47ca673c7d2c2eeb064a60bc9ed51"}}, "download_size": 107395, "post_processing_size": null, "dataset_size": 175424, "size_in_bytes": 282819}}
+{"plain_text": {"description": "Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.\n", "citation": "@inproceedings{alomari2017arabic,\n  title={Arabic tweets sentimental analysis using machine learning},\n  author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},\n  booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},\n  pages={602--610},\n  year={2017},\n  organization={Springer}\n}\n", "homepage": "https://github.com/komari6/Arabic-twitter-corpus-AJGT", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["Negative", "Positive"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "text-classification", "text_column": "text", "label_column": "label", "labels": ["Negative", "Positive"]}], "builder_name": "ajgt_twitter_ar", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 175424, "num_examples": 1800, "dataset_name": "ajgt_twitter_ar"}}, "download_checksums": {"https://raw.githubusercontent.com/komari6/Arabic-twitter-corpus-AJGT/master/AJGT.xlsx": {"num_bytes": 107395, "checksum": "966c52213872b6b8a3ced5fb7c60aee2abf47ca673c7d2c2eeb064a60bc9ed51"}}, "download_size": 107395, "post_processing_size": null, "dataset_size": 175424, "size_in_bytes": 282819}}