update script

lccc.py

@@ -47,10 +47,6 @@ grammatically incorrect sentences, and incoherent conversations are filtered.
 
 _HOMEPAGE = "https://github.com/thu-coai/CDial-GPT"
 _LICENSE = "MIT"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
     "large": "https://huggingface.co/datasets/silver/lccc/resolve/main/lccc_large.jsonl.gz",
     "base": {
@@ -61,32 +57,17 @@ _URLS = {
 }
 
 
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class NewDataset(datasets.GeneratorBasedBuilder):
+class LCCC(datasets.GeneratorBasedBuilder):
     """Large-scale Cleaned Chinese Conversation corpus."""
 
     VERSION = datasets.Version("1.0.0")
 
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name="large", version=VERSION, description="The large version of LCCC"),
         datasets.BuilderConfig(name="base", version=VERSION, description="The base version of LCCC"),
     ]
 
-    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
         features = datasets.Features(
             {
                 "dialog": datasets.Value("string"),
@@ -109,56 +90,33 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # 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
         urls = _URLS[self.config.name]
         downloaded_data = dl_manager.download_and_extract(urls)
         if self.config.name == "large":
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN, 
-                    gen_kwargs={
-                        "filepath": os.path.join(downloaded_data),
-                        "split": "train",
-                    }
+                    gen_kwargs={ "filepath": os.path.join(downloaded_data), "split": "train", }
                 )
             ]
         if self.config.name == "base":
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(downloaded_data["train"]),
-                        "split": "train",
-                    },
+                    gen_kwargs={ "filepath": os.path.join(downloaded_data["train"]), "split": "train", },
                 ),
                 datasets.SplitGenerator(
                     name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(downloaded_data["train"]),
-                        "split": "test"
-                    },
+                    gen_kwargs={ "filepath": os.path.join(downloaded_data["test"]), "split": "test" },
                 ),
                 datasets.SplitGenerator(
                     name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(downloaded_data["valid"]),
-                        "split": "dev",
-                    },
+                    gen_kwargs={ "filepath": os.path.join(downloaded_data["valid"]), "split": "dev", },
                 ),
             ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         with open(filepath, encoding="utf-8") as f:
             for key, row in enumerate(f):
                 row = row.strip()