Commit
•
e4bfb2b
1
Parent(s):
2109e62
add code for using the ds
Browse files- dataset_code.py +99 -0
- example_usage.py +10 -0
dataset_code.py
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@@ -0,0 +1,99 @@
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import datasets
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import logging
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import csv
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import sys
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from csv import DictReader
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csv.field_size_limit(sys.maxsize)
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logger = logging.getLogger(__name__)
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class FFV4Config(datasets.BuilderConfig):
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"""BuilderConfig for SuperGLUE."""
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def __init__(self, filename: str, info: str, **kwargs):
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"""BuilderConfig for SuperGLUE.
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Args:
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features: *list[string]*, list of the features that will appear in the
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feature dict. Should not include "label".
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filename: *string*, csvfile for the dataset.
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info: *string*, for information about the data set.
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**kwargs: keyword arguments forwarded to super.
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"""
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# Version history:
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# 0.0.1: Initial version
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super().__init__(version=datasets.Version("0.0.1"), **kwargs)
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self.filename = filename
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self.info = info
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class FFV4(datasets.GeneratorBasedBuilder):
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"""The thing"""
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BUILDER_CONFIGS = [
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FFV4Config(
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name="notebook_defaults",
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filename="notebook_defaults.csv",
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info="the result of using the default values in the V4 ffarchive notebook, except without the TS/RD filter",
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),
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FFV4Config(
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name="notebook_defaults_ratio0.8_likes10",
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filename="ratio0.8_likes10.csv",
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info="default filter, but with the score filter replaced with '.ratio > 0.8, .likes > 10'",
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),
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]
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DEFAULT_CONFIG_NAME = "notebook_defaults"
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def _info(self):
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return datasets.DatasetInfo(
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description="Garbage datasets for LLM training",
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features=datasets.Features(
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{
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"id": datasets.Value("int32"),
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"header": datasets.Value("string"),
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"story": datasets.Value("string"),
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}
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),
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homepage="https://main.horse",
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)
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def _split_generators(self, x):
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return [
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datasets.SplitGenerator('everything', gen_kwargs={"filepath": self.config.filename}),
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]
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'''
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
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'''
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def _generate_examples(self, filepath):
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"""This function returns the examples in the raw (text) form."""
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logger.info("generating examples from = %s", filepath)
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with open(filepath, encoding="utf-8") as f:
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dr = DictReader(f)
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for d in dr:
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yield d['id'],d
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'''
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squad = json.load(f)
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for article in squad["data"]:
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title = article.get("title", "")
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for paragraph in article["paragraphs"]:
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context = paragraph["context"] # do not strip leading blank spaces GH-2585
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for qa in paragraph["qas"]:
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answer_starts = [answer["answer_start"] for answer in qa["answers"]]
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answers = [answer["text"] for answer in qa["answers"]]
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# Features currently used are "context", "question", and "answers".
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# Others are extracted here for the ease of future expansions.
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yield key, {
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"title": title,
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"context": context,
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"question": qa["question"],
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"id": qa["id"],
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"answers": {
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"answer_start": answer_starts,
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"text": answers,
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},
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}
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key += 1
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'''
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example_usage.py
ADDED
@@ -0,0 +1,10 @@
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import datasets
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ds = datasets.load_dataset('./dataset_code.py', 'notebook_defaults')
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ds_real = ds['everything'] # there is no such thing as a train/test split here
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one_item = ds_real[0] # grab first story, and truncuate the text of it to first 1000 characters
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one_item_truncuated = one_item | {'story': one_item['story'][:1000]}
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print(ds)
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print(one_item_truncuated)
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