Update config file

Signed-off-by: Diwank Singh <diwank.singh@gmail.com>

hinglish-dump.py

@@ -7,7 +7,7 @@
 """Raw merged dump of Hinglish (hi-EN) datasets."""
 
 
-import csv
+import pandas as pd
 import os
 
 import datasets
@@ -20,36 +20,8 @@ _HOMEPAGE = "https://huggingface.co/datasets/diwank/hinglish-dump"
 _LICENSE = "MIT"
 
 _URLS = {
-    "crowd_transliteration": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
-        "crowd_transliteration/crowd_transliterations.hi-en.txt",
-    ])),
-    "fire2013": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
-        "fire2013/HindiEnglish_FIRE2013_AnnotatedDev.txt",
-        "fire2013/HindiEnglish_FIRE2013_Test_GT.txt",
-    ])),
-    "hindi_romanized_dump": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
-        "hindi_romanized_dump/hi_rom.txt",
-    ])),
-    "hindi_xlit": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
-        "hindi_xlit/HiEn_ann1_test.json",
-        "hindi_xlit/HiEn_ann1_train.json",
-        "hindi_xlit/HiEn_ann1_valid.json",
-    ])),
-    "hinge": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
-        "hinge/eval_human.csv",
-        "hinge/train_human.csv",
-        "hinge/train_synthetic.csv",
-        "hinge/eval_synthetic.csv",
-    ])),
-    "hinglish_norm": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
-        "hinglish_norm/hinglishNorm_trainSet.json",
-    ])),
-    "news2018": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
-        "news2018/NEWS2018_M-EnHi_tst.xml",
-        "news2018/NEWS2018_M-EnHi_trn.xml",
-        "news2018/NEWS2018_M-EnHi_dev.xml",
-    ])),
-}
+    subset: f"{_HOMEPAGE}/resolve/main/data/{subset}/data.h5"
+    for subset in "crowd_transliteration  hindi_romanized_dump  hindi_xlit  hinge  hinglish_norm  news2018".split() }
 
 config_names = _URLS.keys()
 version = datasets.Version("1.0.0")
@@ -81,22 +53,23 @@ class HinglishDumpDataset(datasets.DatasetBuilder):
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
 
-        data_dir = self.data_dir = dl_manager.download_and_extract(_URLS)
+        urls = _URLS[self.config.name]
+        data_dir = self.data_dir = dl_manager.download_and_extract(urls)
         
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": None, "split": "train"},
+                gen_kwargs={"filepath": os.path.join(data_dir, "data.h5"), "split": "train"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": None, "split": "validation"},
+                gen_kwargs={"filepath": os.path.join(data_dir, "data.h5"), "split": "eval"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"filepath": None, "split": "test"},
+                gen_kwargs={"filepath": os.path.join(data_dir, "data.h5"), "split": "test"},
             ),
         ]
 
     def _generate_examples(self, filepath, split):
-        return None
+        return pd.read_hdf(filepath, key=split)