[Bug Fix] Resolved load error due to difference in num_examples between configs

dataset_infos.json

@@ -1 +1 @@
-{"with_metadata": {"description": "The bydate version of the 20-newsgroup dataset fetched from scikit_learn and\nsplit in stratified manner into train, validation and test sets. With and\nwithout metadata is made available as individual config names. The test set\nfrom the original 20 newsgroup dataset is retained while the original train\nset is split 80:20 into train and validation sets in stratified manner based\non the newsgroup. The 20 different newsgroup are provided as the labels\ninstead of config names as specified in the official huggingface dataset.\nNewsgroups are specified as labels to provide a simplified setup for text\nclassification task. The 20 different newsgroup functioning as labels are:\n(1) alt.atheism\n(2) comp.graphics\n(3) comp.os.ms-windows.misc\n(4) comp.sys.ibm.pc.hardware\n(5) comp.sys.mac.hardware\n(6) comp.windows.x\n(7) misc.forsale\n(8) rec.autos\n(9) rec.motorcycles\n(10) rec.sport.baseball\n(11) rec.sport.hockey\n(12) sci.crypt\n(13) sci.electronics\n(14) sci.med\n(15) sci.space\n(16) soc.religion.christian\n(17) talk.politics.guns\n(18) talk.politics.mideast\n(19) talk.politics.misc\n(20) talk.religion.misc", "citation": "\n@inproceedings{Lang95,\n    author = {Ken Lang},\n    title = {Newsweeder: Learning to filter netnews}\n    year = {1995}\n    booktitle = {Proceedings of the Twelfth International Conference on Machine Learning}\n    pages = {331-339}\n    }\n ", "homepage": "http://qwone.com/~jason/20Newsgroups/", "license": "", "features": {"text": {"dtype": "large_string", "id": null, "_type": "Value"}, "labels": {"num_classes": 20, "names": ["alt.atheism", "comp.graphics", "comp.os.ms-windows.misc", "comp.sys.ibm.pc.hardware", "comp.sys.mac.hardware", "comp.windows.x", "misc.forsale", "rec.autos", "rec.motorcycles", "rec.sport.baseball", "rec.sport.hockey", "sci.crypt", "sci.electronics", "sci.med", "sci.space", "soc.religion.christian", "talk.politics.guns", "talk.politics.mideast", "talk.politics.misc", "talk.religion.misc"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "newsgroups", "config_name": "with_metadata", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 17065029, "num_examples": 9051, "dataset_name": "newsgroups"}, "validation": {"name": "validation", "num_bytes": 4279761, "num_examples": 2263, "dataset_name": "newsgroups"}, "test": {"name": "test", "num_bytes": 13328728, "num_examples": 7532, "dataset_name": "newsgroups"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 34673518, "size_in_bytes": 34673518}, "without_metadata": {"description": "The bydate version of the 20-newsgroup dataset fetched from scikit_learn and\nsplit in stratified manner into train, validation and test sets. With and\nwithout metadata is made available as individual config names. The test set\nfrom the original 20 newsgroup dataset is retained while the original train\nset is split 80:20 into train and validation sets in stratified manner based\non the newsgroup. The 20 different newsgroup are provided as the labels\ninstead of config names as specified in the official huggingface dataset.\nNewsgroups are specified as labels to provide a simplified setup for text\nclassification task. The 20 different newsgroup functioning as labels are:\n(1) alt.atheism\n(2) comp.graphics\n(3) comp.os.ms-windows.misc\n(4) comp.sys.ibm.pc.hardware\n(5) comp.sys.mac.hardware\n(6) comp.windows.x\n(7) misc.forsale\n(8) rec.autos\n(9) rec.motorcycles\n(10) rec.sport.baseball\n(11) rec.sport.hockey\n(12) sci.crypt\n(13) sci.electronics\n(14) sci.med\n(15) sci.space\n(16) soc.religion.christian\n(17) talk.politics.guns\n(18) talk.politics.mideast\n(19) talk.politics.misc\n(20) talk.religion.misc", "citation": "\n@inproceedings{Lang95,\n    author = {Ken Lang},\n    title = {Newsweeder: Learning to filter netnews}\n    year = {1995}\n    booktitle = {Proceedings of the Twelfth International Conference on Machine Learning}\n    pages = {331-339}\n    }\n ", "homepage": "http://qwone.com/~jason/20Newsgroups/", "license": "", "features": {"text": {"dtype": "large_string", "id": null, "_type": "Value"}, "labels": {"num_classes": 20, "names": ["alt.atheism", "comp.graphics", "comp.os.ms-windows.misc", "comp.sys.ibm.pc.hardware", "comp.sys.mac.hardware", "comp.windows.x", "misc.forsale", "rec.autos", "rec.motorcycles", "rec.sport.baseball", "rec.sport.hockey", "sci.crypt", "sci.electronics", "sci.med", "sci.space", "soc.religion.christian", "talk.politics.guns", "talk.politics.mideast", "talk.politics.misc", "talk.religion.misc"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "newsgroups", "config_name": "without_metadata", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10649695, "num_examples": 9051, "dataset_name": "newsgroups"}, "validation": {"name": "validation", "num_bytes": 2755338, "num_examples": 2263, "dataset_name": "newsgroups"}, "test": {"name": "test", "num_bytes": 8011416, "num_examples": 7532, "dataset_name": "newsgroups"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 21416449, "size_in_bytes": 21416449}}
+{"with_metadata": {"description": "The bydate version of the 20-newsgroup dataset fetched from scikit_learn and\nsplit in stratified manner into train, validation and test sets. With and\nwithout metadata is made available as individual config names. The test set\nfrom the original 20 newsgroup dataset is retained while the original train\nset is split 80:20 into train and validation sets in stratified manner based\non the newsgroup. The 20 different newsgroup are provided as the labels\ninstead of config names as specified in the official huggingface dataset.\nNewsgroups are specified as labels to provide a simplified setup for text\nclassification task. The 20 different newsgroup functioning as labels are:\n(1) alt.atheism\n(2) comp.graphics\n(3) comp.os.ms-windows.misc\n(4) comp.sys.ibm.pc.hardware\n(5) comp.sys.mac.hardware\n(6) comp.windows.x\n(7) misc.forsale\n(8) rec.autos\n(9) rec.motorcycles\n(10) rec.sport.baseball\n(11) rec.sport.hockey\n(12) sci.crypt\n(13) sci.electronics\n(14) sci.med\n(15) sci.space\n(16) soc.religion.christian\n(17) talk.politics.guns\n(18) talk.politics.mideast\n(19) talk.politics.misc\n(20) talk.religion.misc", "citation": "\n@inproceedings{Lang95,\n    author = {Ken Lang},\n    title = {Newsweeder: Learning to filter netnews}\n    year = {1995}\n    booktitle = {Proceedings of the Twelfth International Conference on Machine Learning}\n    pages = {331-339}\n    }\n ", "homepage": "http://qwone.com/~jason/20Newsgroups/", "license": "", "features": {"text": {"dtype": "large_string", "id": null, "_type": "Value"}, "labels": {"num_classes": 20, "names": ["alt.atheism", "comp.graphics", "comp.os.ms-windows.misc", "comp.sys.ibm.pc.hardware", "comp.sys.mac.hardware", "comp.windows.x", "misc.forsale", "rec.autos", "rec.motorcycles", "rec.sport.baseball", "rec.sport.hockey", "sci.crypt", "sci.electronics", "sci.med", "sci.space", "soc.religion.christian", "talk.politics.guns", "talk.politics.mideast", "talk.politics.misc", "talk.religion.misc"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "newsgroups", "config_name": "with_metadata", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 17065029, "num_examples": 9051, "dataset_name": "newsgroups"}, "validation": {"name": "validation", "num_bytes": 4279761, "num_examples": 2263, "dataset_name": "newsgroups"}, "test": {"name": "test", "num_bytes": 13328728, "num_examples": 7532, "dataset_name": "newsgroups"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 34673518, "size_in_bytes": 34673518}, "without_metadata": {"description": "The bydate version of the 20-newsgroup dataset fetched from scikit_learn and\nsplit in stratified manner into train, validation and test sets. With and\nwithout metadata is made available as individual config names. The test set\nfrom the original 20 newsgroup dataset is retained while the original train\nset is split 80:20 into train and validation sets in stratified manner based\non the newsgroup. The 20 different newsgroup are provided as the labels\ninstead of config names as specified in the official huggingface dataset.\nNewsgroups are specified as labels to provide a simplified setup for text\nclassification task. The 20 different newsgroup functioning as labels are:\n(1) alt.atheism\n(2) comp.graphics\n(3) comp.os.ms-windows.misc\n(4) comp.sys.ibm.pc.hardware\n(5) comp.sys.mac.hardware\n(6) comp.windows.x\n(7) misc.forsale\n(8) rec.autos\n(9) rec.motorcycles\n(10) rec.sport.baseball\n(11) rec.sport.hockey\n(12) sci.crypt\n(13) sci.electronics\n(14) sci.med\n(15) sci.space\n(16) soc.religion.christian\n(17) talk.politics.guns\n(18) talk.politics.mideast\n(19) talk.politics.misc\n(20) talk.religion.misc", "citation": "\n@inproceedings{Lang95,\n    author = {Ken Lang},\n    title = {Newsweeder: Learning to filter netnews}\n    year = {1995}\n    booktitle = {Proceedings of the Twelfth International Conference on Machine Learning}\n    pages = {331-339}\n    }\n ", "homepage": "http://qwone.com/~jason/20Newsgroups/", "license": "", "features": {"text": {"dtype": "large_string", "id": null, "_type": "Value"}, "labels": {"num_classes": 20, "names": ["alt.atheism", "comp.graphics", "comp.os.ms-windows.misc", "comp.sys.ibm.pc.hardware", "comp.sys.mac.hardware", "comp.windows.x", "misc.forsale", "rec.autos", "rec.motorcycles", "rec.sport.baseball", "rec.sport.hockey", "sci.crypt", "sci.electronics", "sci.med", "sci.space", "soc.religion.christian", "talk.politics.guns", "talk.politics.mideast", "talk.politics.misc", "talk.religion.misc"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "newsgroups", "config_name": "without_metadata", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10852391, "num_examples": 8811, "dataset_name": "newsgroups"}, "validation": {"name": "validation", "num_bytes": 2547760, "num_examples": 2203, "dataset_name": "newsgroups"}, "test": {"name": "test", "num_bytes": 8007923, "num_examples": 7317, "dataset_name": "newsgroups"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 21408074, "size_in_bytes": 21408074}}

newsgroups.py

@@ -5,29 +5,30 @@ from sklearn.model_selection import train_test_split
 import pandas as pd
 
 _NEWSGROUPS = [
-    'alt.atheism',
-    'comp.graphics',
-    'comp.os.ms-windows.misc',
-    'comp.sys.ibm.pc.hardware',
-    'comp.sys.mac.hardware',
-    'comp.windows.x',
-    'misc.forsale',
-    'rec.autos',
-    'rec.motorcycles',
-    'rec.sport.baseball',
-    'rec.sport.hockey',
-    'sci.crypt',
-    'sci.electronics',
-    'sci.med',
-    'sci.space',
-    'soc.religion.christian',
-    'talk.politics.guns',
-    'talk.politics.mideast',
-    'talk.politics.misc',
-    'talk.religion.misc',
- ]
-
-_DESCRIPTION = textwrap.dedent("""\
+    "alt.atheism",
+    "comp.graphics",
+    "comp.os.ms-windows.misc",
+    "comp.sys.ibm.pc.hardware",
+    "comp.sys.mac.hardware",
+    "comp.windows.x",
+    "misc.forsale",
+    "rec.autos",
+    "rec.motorcycles",
+    "rec.sport.baseball",
+    "rec.sport.hockey",
+    "sci.crypt",
+    "sci.electronics",
+    "sci.med",
+    "sci.space",
+    "soc.religion.christian",
+    "talk.politics.guns",
+    "talk.politics.mideast",
+    "talk.politics.misc",
+    "talk.religion.misc",
+]
+
+_DESCRIPTION = textwrap.dedent(
+    """\
     The bydate version of the 20-newsgroup dataset fetched from scikit_learn and
     split in stratified manner into train, validation and test sets. With and
     without metadata is made available as individual config names. The test set
@@ -39,7 +40,7 @@ _DESCRIPTION = textwrap.dedent("""\
     classification task. The 20 different newsgroup functioning as labels are:
 """
 )
-_DESCRIPTION += "\n".join(f"({i+1}) {j}" for i,j in enumerate(_NEWSGROUPS))
+_DESCRIPTION += "\n".join(f"({i+1}) {j}" for i, j in enumerate(_NEWSGROUPS))
 
 _HOMEPAGE = "http://qwone.com/~jason/20Newsgroups/"
 
@@ -55,18 +56,19 @@ _CITATION = """
 
 _VERSION = datasets.utils.Version("2.0.0")
 
-class NewsgroupsConfig(datasets.BuilderConfig):
 
+class NewsgroupsConfig(datasets.BuilderConfig):
     def __init__(self, **kwargs):
         super(NewsgroupsConfig, self).__init__(version=_VERSION, **kwargs)
 
-        
+
 class Newsgroups(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         NewsgroupsConfig(
             name="with_metadata",
-            description=textwrap.dedent("""\
+            description=textwrap.dedent(
+                """\
                 The original complete bydate 20-Newsgroups dataset with the headers,
                 footers, and quotes metadata as intact and just the continuous
                 whitespaces (including new-line) replaced by single whitespace
@@ -75,7 +77,8 @@ class Newsgroups(datasets.GeneratorBasedBuilder):
         ),
         NewsgroupsConfig(
             name="without_metadata",
-            description=textwrap.dedent("""\
+            description=textwrap.dedent(
+                """\
                 The bydate 20-Newsgroups dataset without the headers, footers,
                 and quotes metadata as well as the continuous whitespaces
                 (including new-line) replaced by single whitespace characters."""
@@ -100,30 +103,55 @@ class Newsgroups(datasets.GeneratorBasedBuilder):
             citation=_CITATION,
         )
 
-    
     def _split_generators(self, dl_manager):
-        
+
         if self.config.name == "with_metadata":
             train_data = fetch_20newsgroups(subset="train", random_state=42)
             test_data = fetch_20newsgroups(subset="test", random_state=42)
 
         else:
-            train_data = fetch_20newsgroups(subset="train", random_state=42, remove=("headers", "footers", "quotes"))
-            test_data = fetch_20newsgroups(subset="test", random_state=42, remove=("headers", "footers", "quotes"))
-
-        train_labels = [train_data["target_names"][i] for i in train_data["target"]]
-        test_labels = [test_data["target_names"][i] for i in test_data["target"]]
-        
-        train_df = pd.DataFrame({"text": train_data["data"], "labels": train_labels})
-        test_df = pd.DataFrame({"text": test_data["data"], "labels": test_labels})
-        
+            train_data = fetch_20newsgroups(
+                subset="train", random_state=42, remove=("headers", "footers", "quotes")
+            )
+            test_data = fetch_20newsgroups(
+                subset="test", random_state=42, remove=("headers", "footers", "quotes")
+            )
+
+            empty_data_idcs = set(
+                [i for i, j in enumerate(train_data.data) if j.strip() == ""]
+            )
+            train_data.data = [
+                j for i, j in enumerate(train_data.data) if i not in empty_data_idcs
+            ]
+            train_data.target = [
+                j for i, j in enumerate(train_data.target) if i not in empty_data_idcs
+            ]
+
+            empty_data_idcs = set(
+                [i for i, j in enumerate(test_data.data) if j.strip() == ""]
+            )
+            test_data.data = [
+                j for i, j in enumerate(test_data.data) if i not in empty_data_idcs
+            ]
+            test_data.target = [
+                j for i, j in enumerate(test_data.target) if i not in empty_data_idcs
+            ]
+
+        train_labels = [train_data.target_names[i] for i in train_data.target]
+        test_labels = [test_data.target_names[i] for i in test_data.target]
+
+        train_df = pd.DataFrame({"text": train_data.data, "labels": train_labels})
+        test_df = pd.DataFrame({"text": test_data.data, "labels": test_labels})
+
         train_df["text"] = train_df["text"].str.replace("\s+", " ", regex=True)
         test_df["text"] = test_df["text"].str.replace("\s+", " ", regex=True)
-        
-        train_df = train_df[train_df["text"].str.strip()!=""]
-        test_df = test_df[test_df["text"].str.strip()!=""]
 
-        train_df, val_df = train_test_split(train_df, test_size=0.2, random_state=42, stratify=train_df["labels"])
+        # train_df = train_df[train_df["text"].str.strip()!=""]
+        # test_df = test_df[test_df["text"].str.strip()!=""]
+
+        train_df, val_df = train_test_split(
+            train_df, test_size=0.2, random_state=42, stratify=train_df["labels"]
+        )
         train_df = train_df.reset_index(drop=True)
         val_df = val_df.reset_index(drop=True)
 
@@ -139,7 +167,6 @@ class Newsgroups(datasets.GeneratorBasedBuilder):
             ),
         ]
 
-
     def _generate_examples(self, df):
         for idx, row in df.iterrows():
-            yield idx, row.to_dict()
+            yield idx, row.to_dict()