Host data file (#2)

- Host data file (f40238161de8ccc2c09ca976513fb844cde8b275)
- Update loading script (45d0e2031d77f3fa23b06f1d2132e2afbee19500)
- Update metadata (736678ada0232e12b9b452773cb6b349e4319b07)
- Delete legacy dataset_infos.json (40e0bd7fbfaad522dd37b51d7ab5a27eacaa3cbe)

README.md

@@ -72,9 +72,8 @@ dataset_info:
 
 ## Dataset Description
 
-- **Homepage:** https://www.aclweb.org/anthology/2020.lrec-1.335
 - **Repository:** https://github.com/ajesujoba/YorubaTwi-Embedding/
-- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335
+- **Paper:** https://aclanthology.org/2020.lrec-1.335/
 - **Leaderboard:**
 - **Point of Contact:** [Jesujoba Alabi](mailto:alabijesujoba@gmail.com)
 

data/yo_C3_large_clean_plus_noisy.txt.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ace61add272cdf8ffdb1f16f95372fe47045303b03e9a4c32ffe499d0d7a6a5b
+size 20315121

dataset_infos.json

@@ -1 +0,0 @@
-{"plain_text": {"description": "Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the \nYorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/\n", "citation": "@inproceedings{alabi-etal-2020-massive,\n    title = \"Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\\`u}b{'a} and {T}wi\",\n    author = \"Alabi, Jesujoba  and\n      Amponsah-Kaakyire, Kwabena  and\n      Adelani, David  and\n      Espa{\\~n}a-Bonet, Cristina\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.335\",\n    pages = \"2754--2762\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://www.aclweb.org/anthology/2020.lrec-1.335/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yoruba_twi_text_c3", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 77094396, "num_examples": 562238, "dataset_name": "yoruba_twi_text_c3"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1Nug7-Sri50mkJL4GrWw6C2ZIbfeU-6Am": {"num_bytes": 75407454, "checksum": "171f71c1d2249fa300219a335361c9b04f96b0abf514f351128514d4ccc03b50"}}, "download_size": 75407454, "post_processing_size": null, "dataset_size": 77094396, "size_in_bytes": 152501850}, "yoruba_text_c3": {"description": "Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the \nYorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/\n", "citation": "@inproceedings{alabi-etal-2020-massive,\n    title = \"Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\\`u}b{'a} and {T}wi\",\n    author = \"Alabi, Jesujoba  and\n      Amponsah-Kaakyire, Kwabena  and\n      Adelani, David  and\n      Espa{\\~n}a-Bonet, Cristina\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.335\",\n    pages = \"2754--2762\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://www.aclweb.org/anthology/2020.lrec-1.335/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yoruba_twi_text_c3", "config_name": "yoruba_text_c3", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 77094396, "num_examples": 562238, "dataset_name": "yoruba_twi_text_c3"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1Nug7-Sri50mkJL4GrWw6C2ZIbfeU-6Am": {"num_bytes": 75407454, "checksum": "171f71c1d2249fa300219a335361c9b04f96b0abf514f351128514d4ccc03b50"}}, "download_size": 75407454, "post_processing_size": null, "dataset_size": 77094396, "size_in_bytes": 152501850}}

yoruba_text_c3.py

@@ -25,6 +25,8 @@ Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText
 YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/
 """
 
+_HOMEPAGE = "https://github.com/ajesujoba/YorubaTwi-Embedding/"
+
 _CITATION = """\
 @inproceedings{alabi-etal-2020-massive,
     title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yoruba and {T}wi",
@@ -44,30 +46,12 @@ _CITATION = """\
 }
 """
 
-URL = "https://drive.google.com/uc?export=download&id=1Nug7-Sri50mkJL4GrWw6C2ZIbfeU-6Am"
-
-
-class YorubaTextC3Config(datasets.BuilderConfig):
-    """BuilderConfig for YorubaTextC3."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for BookCorpus.
-        Args:
-        **kwargs: keyword arguments forwarded to super.
-        """
-        super(YorubaTextC3Config, self).__init__(**kwargs)
+URL = "data/yo_C3_large_clean_plus_noisy.txt.gz"
 
 
 class YorubaTextC3(datasets.GeneratorBasedBuilder):
     """Yoruba Text C3 dataset."""
-
-    BUILDER_CONFIGS = [
-        YorubaTextC3Config(
-            name="yoruba_text_c3",
-            version=datasets.Version("1.0.0"),
-            description="Yoruba Texts C3 ",
-        )
-    ]
+    VERSION = datasets.Version("1.0.0")
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -78,15 +62,15 @@ class YorubaTextC3(datasets.GeneratorBasedBuilder):
                 }
             ),
             supervised_keys=None,
-            homepage="https://www.aclweb.org/anthology/2020.lrec-1.335/",
+            homepage=_HOMEPAGE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        arch_path = dl_manager.download_and_extract(URL)
+        filepath = dl_manager.download_and_extract(URL)
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": arch_path}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": filepath}),
         ]
 
     def _generate_examples(self, filepath):