first draft of loading script for ml only

README.md

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+## IndicNLP News Article Classification Dataset
+
+We used the IndicNLP text corpora to create classification datasets comprising news articles and their categories for 9 languages. The dataset is balanced across classes.  The following table contains the statistics of our dataset:
+
+| Language  | Classes                                     | Articles per Class |
+| --------- | ------------------------------------------- | ------------------ |
+| Bengali   | entertainment, sports                       | 7K                 |
+| Gujarati  | business, entertainment, sports             | 680                |
+| Kannada   | entertainment, lifestyle, sports            | 10K                |
+| Malayalam | business, entertainment, sports, technology | 1.5K               |
+| Marathi   | entertainment, lifestyle, sports            | 1.5K               |
+| Oriya     | business, crime, entertainment, sports      | 7.5K               |
+| Punjabi   | business, entertainment, sports, politics   | 780                |
+| Tamil     | entertainment, politics, sport              | 3.9K               |
+| Telugu    | entertainment, business, sports             | 8K                 |
+
+## Citing
+
+If you are using any of the resources, please cite the following article: 
+
+```
+@article{kunchukuttan2020indicnlpcorpus,
+    title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},
+    author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
+    year={2020},
+    journal={arXiv preprint arXiv:2005.00085},
+}
+```

dataset_infos.json

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+{
+    "default": {
+        "description": "The AI4Bharat-IndicNLP dataset is an ongoing effort to create a collection of large-scale, general-domain corpora  \n  for Indian languages. Currently, it contains 2.7 billion words for 10 Indian languages from two language families. We share  \n  pre-trained word embeddings trained on these corpora. We create news article category classification datasets for 9  \n  languages to evaluate the embeddings. We evaluate the IndicNLP embeddings on multiple evaluation tasks.\n",
+        "citation": "@article{kunchukuttan2020indicnlpcorpus,\n  title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},\n  author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n  journal={arXiv preprint arXiv:2005.00085},\n  year={2020}\n}\n",
+        "homepage": "https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset",
+        "license": "",
+        "features": {
+            "text": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "label": {
+                "num_classes": 4,
+                "names": [
+                    "business",
+                    "entertainment",
+                    "sports",
+                    "technology"
+                ],
+                "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": [
+                    "business",
+                    "entertainment",
+                    "sports",
+                    "technology"
+                ]
+            }
+        ],
+        "builder_name": "malayalam_news",
+        "config_name": "default",
+        "version": {
+            "version_str": "0.0.0",
+            "description": null,
+            "major": 0,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 29817351,
+                "num_examples": 120000,
+                "dataset_name": "malayalam_news"
+            },
+            "validation": {
+                "name": "validation",
+                "num_bytes": 29817351,
+                "num_examples": 120000,
+                "dataset_name": "malayalam_news"
+            },
+            "test": {
+                "name": "test",
+                "num_bytes": 1879478,
+                "num_examples": 7600,
+                "dataset_name": "malayalam_news"
+            }
+        },
+        "download_checksums": {
+            "https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/classification/indicnlp-news-articles.tgz": {
+                "num_bytes": null,
+                "checksum": ""
+            }
+        },
+        "download_size": null,
+        "post_processing_size": null,
+        "dataset_size": null,
+        "size_in_bytes": null
+    }
+}

malayalam_news.py

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+import csv
+import os
+
+import datasets
+
+from datasets.tasks import TextClassification
+
+_DESCRIPTION = """\
+The AI4Bharat-IndicNLP dataset is an ongoing effort to create a collection of large-scale, 
+general-domain corpora for Indian languages. Currently, it contains 2.7 billion words for 10 Indian languages from two language families. 
+We share pre-trained word embeddings trained on these corpora.
+We create news article category classification datasets for 9 languages to evaluate the embeddings.
+We evaluate the IndicNLP embeddings on multiple evaluation tasks.
+"""
+
+_CITATION = """\
+@article{kunchukuttan2020indicnlpcorpus,
+    title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},
+    author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
+    year={2020},
+    journal={arXiv preprint arXiv:2005.00085},
+}
+"""
+
+_DOWNLOAD_URL = "https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/classification/indicnlp-news-articles.tgz"
+
+class MalayalamNews(datasets.GeneratorBasedBuilder):
+    """Malayalam News topic classification dataset."""
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["business", "entertainment", "sports", "technology"]),
+                }
+            ),
+            homepage="https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset",
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "indicnlp-news-articles", "ml", "ml-train.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "indicnlp-news-articles", "ml", "ml-valid.csv"),
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "indicnlp-news-articles", "ml", "ml-test.csv"),
+                    "split": "test",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate Malayalam News examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            for id_, row in enumerate(csv_reader):
+                label, title, description = row
+                # Original labels are [1, 2, 3, 4] ->
+                #                   ['World', 'Sports', 'Business', 'Sci/Tech']
+                # Re-map to [0, 1, 2, 3].
+                label = int(label) - 1
+                text = " ".join((title, description))
+                yield id_, {"text": text, "label": label}