Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Portuguese
Size:
1K - 10K
License:
Fix Essay from grader's data
#3
by
abarbosa
- opened
- aes_enem_dataset.py +12 -12
- propor2024.tar.gz +0 -0
aes_enem_dataset.py
CHANGED
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@@ -340,14 +340,14 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
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if self.config.name == "sourceAWithGraders":
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grader_a, grader_b = self._parse_graders_data(dirname)
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| 342 |
grader_a_data = pd.merge(
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| 343 |
-
train_df[["id", "id_prompt"]],
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| 344 |
-
grader_a,
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| 345 |
on=["id", "id_prompt"],
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how="inner",
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)
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grader_b_data = pd.merge(
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-
train_df[["id", "id_prompt"]],
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| 350 |
-
grader_b,
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| 351 |
on=["id", "id_prompt"],
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| 352 |
how="inner",
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| 353 |
)
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@@ -355,14 +355,14 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
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train_df = pd.concat([train_df, grader_b_data])
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| 356 |
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grader_a_data = pd.merge(
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| 358 |
-
val_df[["id", "id_prompt"]],
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| 359 |
-
grader_a,
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| 360 |
on=["id", "id_prompt"],
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| 361 |
how="inner",
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| 362 |
)
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| 363 |
grader_b_data = pd.merge(
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| 364 |
-
val_df[["id", "id_prompt"]],
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| 365 |
-
grader_b,
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| 366 |
on=["id", "id_prompt"],
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| 367 |
how="inner",
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| 368 |
)
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@@ -370,14 +370,14 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
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val_df = pd.concat([val_df, grader_b_data])
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| 371 |
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grader_a_data = pd.merge(
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| 373 |
-
test_df[["id", "id_prompt"]],
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| 374 |
-
grader_a,
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| 375 |
on=["id", "id_prompt"],
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| 376 |
how="inner",
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| 377 |
)
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| 378 |
grader_b_data = pd.merge(
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| 379 |
-
test_df[["id", "id_prompt"]],
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| 380 |
-
grader_b,
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| 381 |
on=["id", "id_prompt"],
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| 382 |
how="inner",
|
| 383 |
)
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|
|
|
| 340 |
if self.config.name == "sourceAWithGraders":
|
| 341 |
grader_a, grader_b = self._parse_graders_data(dirname)
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| 342 |
grader_a_data = pd.merge(
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| 343 |
+
train_df[["id", "id_prompt","essay"]],
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| 344 |
+
grader_a.drop(columns=['essay']),
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| 345 |
on=["id", "id_prompt"],
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| 346 |
how="inner",
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| 347 |
)
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| 348 |
grader_b_data = pd.merge(
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| 349 |
+
train_df[["id", "id_prompt","essay"]],
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| 350 |
+
grader_b.drop(columns=['essay']),
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| 351 |
on=["id", "id_prompt"],
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| 352 |
how="inner",
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| 353 |
)
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| 355 |
train_df = pd.concat([train_df, grader_b_data])
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| 356 |
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| 357 |
grader_a_data = pd.merge(
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| 358 |
+
val_df[["id", "id_prompt","essay"]],
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| 359 |
+
grader_a.drop(columns=['essay']),
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| 360 |
on=["id", "id_prompt"],
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| 361 |
how="inner",
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| 362 |
)
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| 363 |
grader_b_data = pd.merge(
|
| 364 |
+
val_df[["id", "id_prompt","essay"]],
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| 365 |
+
grader_b.drop(columns=['essay']),
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| 366 |
on=["id", "id_prompt"],
|
| 367 |
how="inner",
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| 368 |
)
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| 370 |
val_df = pd.concat([val_df, grader_b_data])
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| 371 |
|
| 372 |
grader_a_data = pd.merge(
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| 373 |
+
test_df[["id", "id_prompt","essay"]],
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| 374 |
+
grader_a.drop(columns=['essay']),
|
| 375 |
on=["id", "id_prompt"],
|
| 376 |
how="inner",
|
| 377 |
)
|
| 378 |
grader_b_data = pd.merge(
|
| 379 |
+
test_df[["id", "id_prompt","essay"]],
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| 380 |
+
grader_b.drop(columns=['essay']),
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| 381 |
on=["id", "id_prompt"],
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| 382 |
how="inner",
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| 383 |
)
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propor2024.tar.gz
CHANGED
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Binary files a/propor2024.tar.gz and b/propor2024.tar.gz differ
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