Update files from the datasets library (from 1.8.0)

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

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. Train: 11,335 Validation: 1,260 and Test: 3,149.  This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon.  The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between.\n", "citation": "@inproceedings{chakravarthi-etal-2020-corpus,\n    title = \"Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text\",\n    author = \"Chakravarthi, Bharathi Raja  and\n      Muralidaran, Vigneshwaran  and\n      Priyadharshini, Ruba  and\n      McCrae, John Philip\",\n    booktitle = \"Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources association\",\n    url = \"https://www.aclweb.org/anthology/2020.sltu-1.28\",\n    pages = \"202--210\",\n    abstract = \"Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.\",\n    language = \"English\",\n    ISBN = \"979-10-95546-35-1\",\n}\n", "homepage": "https://dravidian-codemix.github.io/2020/datasets.html", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 5, "names": ["Positive", "Negative", "Mixed_feelings", "unknown_state", "not-Tamil"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tamilmixsentiment", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 790132, "num_examples": 11335, "dataset_name": "tamilmixsentiment"}, "validation": {"name": "validation", "num_bytes": 89618, "num_examples": 1260, "dataset_name": "tamilmixsentiment"}, "test": {"name": "test", "num_bytes": 218764, "num_examples": 3149, "dataset_name": "tamilmixsentiment"}}, "download_checksums": {"https://drive.google.com/u/0/uc?id=1hDHeoFIfQzJec1NgZNXh3CTNbchiIvuG&export=download": {"num_bytes": 801261, "checksum": "4be61bf29cb68e83d682d33898bf41b2240d960f85e2ccec50e6c4fb8e24e243"}, "https://drive.google.com/u/0/uc?id=1lyCxkDwZyvKLaF5NZhS3JE2ftMmHAKBT&export=download": {"num_bytes": 90843, "checksum": "d4bc37c4d0ccbca7079ea5f5c0c6d3ef0fcca3a084be7af28ec052bc5cdfe5f2"}, "https://docs.google.com/spreadsheets/d/1_6HJWkOuD76pQ6M_RVoP4FOsUK22zPl1IMpq1atLhl4/export?format=tsv": {"num_bytes": 258688, "checksum": "8047bb44125bbc926d38aafea01e2b342bdbd317b53baee8c4dccaeae58021e1"}}, "download_size": 1150792, "post_processing_size": null, "dataset_size": 1098514, "size_in_bytes": 2249306}}
+{"default": {"description": "The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. Train: 11,335 Validation: 1,260 and Test: 3,149.  This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon.  The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between.\n", "citation": "@inproceedings{chakravarthi-etal-2020-corpus,\n    title = \"Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text\",\n    author = \"Chakravarthi, Bharathi Raja  and\n      Muralidaran, Vigneshwaran  and\n      Priyadharshini, Ruba  and\n      McCrae, John Philip\",\n    booktitle = \"Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources association\",\n    url = \"https://www.aclweb.org/anthology/2020.sltu-1.28\",\n    pages = \"202--210\",\n    abstract = \"Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.\",\n    language = \"English\",\n    ISBN = \"979-10-95546-35-1\",\n}\n", "homepage": "https://dravidian-codemix.github.io/2020/datasets.html", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 5, "names": ["Positive", "Negative", "Mixed_feelings", "unknown_state", "not-Tamil"], "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": ["Mixed_feelings", "Negative", "Positive", "not-Tamil", "unknown_state"]}], "builder_name": "tamilmixsentiment", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 790132, "num_examples": 11335, "dataset_name": "tamilmixsentiment"}, "validation": {"name": "validation", "num_bytes": 89618, "num_examples": 1260, "dataset_name": "tamilmixsentiment"}, "test": {"name": "test", "num_bytes": 218764, "num_examples": 3149, "dataset_name": "tamilmixsentiment"}}, "download_checksums": {"https://drive.google.com/u/0/uc?id=1hDHeoFIfQzJec1NgZNXh3CTNbchiIvuG&export=download": {"num_bytes": 801261, "checksum": "4be61bf29cb68e83d682d33898bf41b2240d960f85e2ccec50e6c4fb8e24e243"}, "https://drive.google.com/u/0/uc?id=1lyCxkDwZyvKLaF5NZhS3JE2ftMmHAKBT&export=download": {"num_bytes": 90843, "checksum": "d4bc37c4d0ccbca7079ea5f5c0c6d3ef0fcca3a084be7af28ec052bc5cdfe5f2"}, "https://docs.google.com/spreadsheets/d/1_6HJWkOuD76pQ6M_RVoP4FOsUK22zPl1IMpq1atLhl4/export?format=tsv": {"num_bytes": 258688, "checksum": "8047bb44125bbc926d38aafea01e2b342bdbd317b53baee8c4dccaeae58021e1"}}, "download_size": 1150792, "post_processing_size": null, "dataset_size": 1098514, "size_in_bytes": 2249306}}

tamilmixsentiment.py

@@ -18,6 +18,7 @@
 import csv
 
 import datasets
+from datasets.tasks import TextClassification
 
 
 _CITATION = """\
@@ -69,6 +70,7 @@ class Tamilmixsentiment(datasets.GeneratorBasedBuilder):
             ),
             homepage="https://dravidian-codemix.github.io/2020/datasets.html",
             citation=_CITATION,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
         )
 
     def _split_generators(self, dl_manager):