Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Portuguese
Size:
1K - 10K
License:
Adding general and specific comments to Source A
Browse files- aes_enem_dataset.py +6 -2
aes_enem_dataset.py
CHANGED
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@@ -142,6 +142,8 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
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"essay_text": datasets.Value("string"),
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"grades": datasets.Sequence(datasets.Value("int16")),
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"essay_year": datasets.Value("int16"),
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}
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)
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@@ -286,7 +288,6 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
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grader_a = pd.read_csv(f"{dirname}/GraderA.csv")
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grader_b = pd.read_csv(f"{dirname}/GraderB.csv")
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-
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for grader in [grader_a, grader_b]:
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grader.grades = grader.grades.apply(lambda x: x.strip("[]").split(", "))
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grader.grades = grader.grades.apply(map_list)
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@@ -393,6 +394,7 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
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assert (
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len(set(val_df["id_prompt"]).intersection(set(test_df["id_prompt"]))) == 0
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), "Overlap between val and test id_prompt"
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train_df.to_csv(f"{dirname}/train.csv", index=False)
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val_df.to_csv(f"{dirname}/validation.csv", index=False)
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test_df.to_csv(f"{dirname}/test.csv", index=False)
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@@ -415,6 +417,8 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
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"essay_text": row["essay"],
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"grades": grades,
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"essay_year": row["essay_year"],
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}
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@@ -585,7 +589,7 @@ class HTMLParser:
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for span in soup.find_all("span"):
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span.decompose()
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result = table.find_all("p")
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-
result = "
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[paragraph.get_text().strip() for paragraph in result]
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)
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return result
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"essay_text": datasets.Value("string"),
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"grades": datasets.Sequence(datasets.Value("int16")),
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"essay_year": datasets.Value("int16"),
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+
"general_comment": datasets.Value("string"),
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+
"specific_comment": datasets.Value("string"),
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}
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)
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grader_a = pd.read_csv(f"{dirname}/GraderA.csv")
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grader_b = pd.read_csv(f"{dirname}/GraderB.csv")
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for grader in [grader_a, grader_b]:
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grader.grades = grader.grades.apply(lambda x: x.strip("[]").split(", "))
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grader.grades = grader.grades.apply(map_list)
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assert (
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len(set(val_df["id_prompt"]).intersection(set(test_df["id_prompt"]))) == 0
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), "Overlap between val and test id_prompt"
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+
#train_df['essay_year'] = train_df['essay_year'].astype(int)
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train_df.to_csv(f"{dirname}/train.csv", index=False)
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val_df.to_csv(f"{dirname}/validation.csv", index=False)
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test_df.to_csv(f"{dirname}/test.csv", index=False)
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"essay_text": row["essay"],
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"grades": grades,
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"essay_year": row["essay_year"],
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+
"general_comment": row["general"],
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+
"specific_comment": row["specific"],
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}
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for span in soup.find_all("span"):
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span.decompose()
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result = table.find_all("p")
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+
result = " ".join(
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[paragraph.get_text().strip() for paragraph in result]
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)
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return result
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