added load script

afriberta.py

@@ -0,0 +1,112 @@
+# 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.
+
+# Lint as: python3
+
+import datasets
+
+_DESCRIPTION = """\
+Corpus used for training AfriBERTa models
+"""
+_CITATION = """\
+@inproceedings{ogueji-etal-2021-small,
+    title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
+    author = "Ogueji, Kelechi  and
+      Zhu, Yuxin  and
+      Lin, Jimmy",
+    booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
+    month = nov,
+    year = "2021",
+    address = "Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.mrl-1.11",
+    pages = "116--126",
+}
+"""
+_HOMEPAGE_URL = "https://github.com/keleog/afriberta"
+_VERSION = "1.0.0"
+_LANGUAGES = [
+    "afaanoromoo", 
+    "amharic", 
+    "gahuza", 
+    "hausa", 
+    "igbo", 
+    "pidgin", 
+    "somali", 
+    "swahili", 
+    "tigrinya", 
+    "yoruba"]
+
+_DATASET_URLS = {
+    language: {
+        "train": f"https://huggingface.co/datasets/castorini/afriberta/resolve/main/{language}/train.zip",
+        "test": f"https://huggingface.co/datasets/castorini/afriberta/resolve/main/{language}/eval.zip",
+    } for language in _LANGUAGES
+}
+
+class MrTyDiCorpus(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            version=datasets.Version(_VERSION),
+            name=language, 
+            description=f"AfriBERTa corpus for {language}."
+        ) for language in _LANGUAGES
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                },
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        language = self.config.name
+        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[language])
+
+        splits = [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "file_path": downloaded_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name="test",
+                gen_kwargs={
+                    "file_path": downloaded_files["test"],
+                },
+            ),
+        ]
+        return splits
+
+    def _generate_examples(self, file_path):
+        with open(file_path, encoding="utf-8") as f:
+            for sentence_counter, line in enumerate(f):
+                result = (
+                    sentence_counter,
+                    {
+                        "id": str(sentence_counter),
+                        "text": line,
+                    },
+                )
+                yield result