add code for using the ds

dataset_code.py

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+import datasets
+import logging
+import csv
+import sys
+from csv import DictReader
+csv.field_size_limit(sys.maxsize)
+
+logger = logging.getLogger(__name__)
+
+class FFV4Config(datasets.BuilderConfig):
+    """BuilderConfig for SuperGLUE."""
+
+    def __init__(self, filename: str, info: str, **kwargs):
+        """BuilderConfig for SuperGLUE.
+
+        Args:
+        features: *list[string]*, list of the features that will appear in the
+            feature dict. Should not include "label".
+        filename: *string*, csvfile for the dataset.
+        info: *string*, for information about the data set.
+        **kwargs: keyword arguments forwarded to super.
+        """
+        # Version history:
+        # 0.0.1: Initial version
+        super().__init__(version=datasets.Version("0.0.1"), **kwargs)
+        self.filename = filename
+        self.info = info
+
+class FFV4(datasets.GeneratorBasedBuilder):
+    """The thing"""
+
+    BUILDER_CONFIGS = [
+        FFV4Config(
+            name="notebook_defaults",
+            filename="notebook_defaults.csv",
+            info="the result of using the default values in the V4 ffarchive notebook, except without the TS/RD filter",
+        ),
+        FFV4Config(
+            name="notebook_defaults_ratio0.8_likes10",
+            filename="ratio0.8_likes10.csv",
+            info="default filter, but with the score filter replaced with '.ratio > 0.8, .likes > 10'",
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = "notebook_defaults"
+
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="Garbage datasets for LLM training",
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "header": datasets.Value("string"),
+                    "story": datasets.Value("string"),
+                }
+            ),
+            homepage="https://main.horse",
+        )
+
+
+    def _split_generators(self, x):
+        return [
+            datasets.SplitGenerator('everything', gen_kwargs={"filepath": self.config.filename}),
+        ]
+        '''
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+        '''
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            dr = DictReader(f)
+            for d in dr:
+                yield d['id'],d
+            '''
+            squad = json.load(f)
+            for article in squad["data"]:
+                title = article.get("title", "")
+                for paragraph in article["paragraphs"]:
+                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
+                    for qa in paragraph["qas"]:
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                        answers = [answer["text"] for answer in qa["answers"]]
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield key, {
+                            "title": title,
+                            "context": context,
+                            "question": qa["question"],
+                            "id": qa["id"],
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }
+                        key += 1
+            '''

example_usage.py

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+import datasets
+
+ds = datasets.load_dataset('./dataset_code.py', 'notebook_defaults')
+
+ds_real = ds['everything'] # there is no such thing as a train/test split here
+one_item = ds_real[0] # grab first story, and truncuate the text of it to first 1000 characters
+one_item_truncuated = one_item | {'story': one_item['story'][:1000]}
+
+print(ds)
+print(one_item_truncuated)