eduagarcia
commited on
Commit
•
e7e3c60
1
Parent(s):
b0d53b2
update script to remove fewshot split
Browse files
data/fewshot-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:5a9616244730b16c2206f28abda97ceb484d79381a33c474380d3f4369a25402
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size 19101
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data/test-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:d7ffb343ba1006a2d87ff746e7974eaa80848a3cb82b234defe1e24875db3d1f
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size 1019953
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enem_challenge_disabled.py
CHANGED
@@ -243,34 +243,24 @@ class ENEMChallenge(datasets.GeneratorBasedBuilder):
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def _split_generators(self, dl_manager):
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downloaded_file = dl_manager.download(FILE_URL)
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-
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dataset = {
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'2022': datasets.load_dataset(DATASET_PATH, name="2022", split="train"),
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'2023': datasets.load_dataset(DATASET_PATH, name="2023", split="train")
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}
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#filter fewshot
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to_filter = [int(d['id'].split('_')[1]) for d in fewshot]
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dataset['2022'] = dataset['2022'].filter(lambda x: int(x['id'].split('_')[1]) not in to_filter)
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-
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return [
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datasets.SplitGenerator(
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name=datasets.Split.
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gen_kwargs={
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"data_list": [downloaded_file, dataset],
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"types": ["zip", "dataset"]
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),
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datasets.SplitGenerator(
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name='fewshot',
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gen_kwargs={
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"data_list": [fewshot],
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"types": ["list"]
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}
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)
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]
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def _generate_examples(self, data_list, types):
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for data, type in zip(data_list, types):
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if type == "zip":
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with zipfile.ZipFile(data, 'r') as zip_ref:
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@@ -301,26 +291,8 @@ class ENEMChallenge(datasets.GeneratorBasedBuilder):
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"question": document["context"] + '\n' + document["question"],
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"choices": choices,
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"answerKey": document["label"].upper(),
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"explanation": None
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}
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if type == "list":
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for document in data:
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choices = {
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"text": document["options"],
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"label": ["A", "B", "C", "D", "E"]
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}
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doc_id = document['id']
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yield doc_id, {
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"id": doc_id,
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"question_number": int(doc_id.split('_')[1]),
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"exam_id": document['exam'],
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"exam_year": document['exam'].split('_')[0],
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"nullified": None,
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"question": document["context"] + '\n' + document["question"],
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"choices": choices,
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"answerKey": document["label"].upper(),
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"explanation": document["explanation"],
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}
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if type == "dataset":
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for exam_id in data:
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dataset = data[exam_id]
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@@ -342,6 +314,6 @@ class ENEMChallenge(datasets.GeneratorBasedBuilder):
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"question": document["question"],
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"choices": choices,
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"answerKey": document["label"].upper(),
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"explanation": None
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}
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def _split_generators(self, dl_manager):
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downloaded_file = dl_manager.download(FILE_URL)
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explanation_questions = {d['id']:d['explanation'] for d in _get_train_examples()}
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dataset = {
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'2022': datasets.load_dataset(DATASET_PATH, name="2022", split="train"),
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'2023': datasets.load_dataset(DATASET_PATH, name="2023", split="train")
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}
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"data_list": [downloaded_file, dataset],
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"types": ["zip", "dataset"],
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"explanation_questions": explanation_questions
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}
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)
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]
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def _generate_examples(self, data_list, types, explanation_questions):
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for data, type in zip(data_list, types):
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if type == "zip":
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with zipfile.ZipFile(data, 'r') as zip_ref:
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"question": document["context"] + '\n' + document["question"],
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"choices": choices,
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"answerKey": document["label"].upper(),
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"explanation": explanation_questions.get(doc_id, None)
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}
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if type == "dataset":
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for exam_id in data:
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dataset = data[exam_id]
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"question": document["question"],
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"choices": choices,
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"answerKey": document["label"].upper(),
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"explanation": explanation_questions.get(doc_id, None)
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}
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