Felix
commited on
Commit
•
a01bec3
1
Parent(s):
e6f8644
rename dalaj -> dalaj-ged
Browse files- superlim-2.py +12 -23
superlim-2.py
CHANGED
@@ -112,7 +112,7 @@ _URL = "https://huggingface.co/datasets/sbx/superlim-2/resolve/main/data/"
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_TASKS = {
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"absabank-imm": "absabank-imm",
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"argumentation_sent":"argumentation-sentences",
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-
"dalaj": "dalag-ged-superlim",
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"swesim_relatedness": "supersim-superlim/supersim-superlim-relatedness",
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"swesim_similarity": "supersim-superlim/supersim-superlim-similarity",
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"sweana": "sweanalogy",
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@@ -161,7 +161,7 @@ class SuperLim(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="absabank-imm", version=VERSION, description=_DaLAJ_DESCRIPTION),
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-
datasets.BuilderConfig(name="dalaj", version=VERSION, description=_DaLAJ_DESCRIPTION),
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datasets.BuilderConfig(name="swesim_relatedness", version=VERSION, description=_SweSim_DESCRIPTION),
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datasets.BuilderConfig(name="swesim_similarity", version=VERSION, description=_SweSim_DESCRIPTION),
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datasets.BuilderConfig(name="sweana", version=VERSION, description=_SweAna_DESCRIPTION),
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@@ -175,18 +175,12 @@ class SuperLim(datasets.GeneratorBasedBuilder):
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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-
if self.config.name == "dalaj": # This is the name of the configuration selected in BUILDER_CONFIGS above
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features = datasets.Features(
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{
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"
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"
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"
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"corrected_indices": datasets.Value("string"),
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"error_corr_pair": datasets.Value("string"),
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"error_label": datasets.Value("string"),
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"l1": datasets.Value("string"),
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"approximate_level": datasets.Value("string"),
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# These are the features of your dataset like images, labels ...
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}
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)
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elif self.config.name == 'absabank-imm':
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@@ -338,7 +332,7 @@ class SuperLim(datasets.GeneratorBasedBuilder):
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},
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)
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splits.append(split_test)
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-
if self.config.name in ("absabank-imm", "dalaj", "swefaq", "swewic", "swedn"):
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data_dir_dev = dl_manager.download_and_extract(os.path.join(_URL,_TASKS[self.config.name],f"{_TASKS[self.config.name]}_dev.{file_format}"))
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split_dev = datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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@@ -349,7 +343,7 @@ class SuperLim(datasets.GeneratorBasedBuilder):
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},
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)
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splits.append(split_dev)
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if self.config.name in ("absabank-imm", "dalaj", "swefaq", "swewic", "argumentation_sent", "swedn"):
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data_dir_train = dl_manager.download_and_extract(os.path.join(_URL,_TASKS[self.config.name],f"{_TASKS[self.config.name]}_train.{file_format}"))
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split_train = datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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@@ -367,17 +361,12 @@ class SuperLim(datasets.GeneratorBasedBuilder):
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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df = pd.read_json(filepath, lines=True)
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for key, row in df.iterrows():
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if self.config.name == "dalaj":
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# Yields examples as (key, example) tuples
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yield key, {
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"
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"
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"
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"corrected_indices": row["corrected indices"],
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"error_corr_pair": row["error-corr pair"],
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"error_label": row["error label"],
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"l1": row["l1"],
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"approximate_level": row["approximate level"],
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}
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elif self.config.name == "absabank-imm":
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yield key, {
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_TASKS = {
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"absabank-imm": "absabank-imm",
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"argumentation_sent":"argumentation-sentences",
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+
"dalaj-ged": "dalag-ged-superlim",
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"swesim_relatedness": "supersim-superlim/supersim-superlim-relatedness",
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"swesim_similarity": "supersim-superlim/supersim-superlim-similarity",
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"sweana": "sweanalogy",
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="absabank-imm", version=VERSION, description=_DaLAJ_DESCRIPTION),
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datasets.BuilderConfig(name="dalaj-ged", version=VERSION, description=_DaLAJ_DESCRIPTION),
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datasets.BuilderConfig(name="swesim_relatedness", version=VERSION, description=_SweSim_DESCRIPTION),
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datasets.BuilderConfig(name="swesim_similarity", version=VERSION, description=_SweSim_DESCRIPTION),
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datasets.BuilderConfig(name="sweana", version=VERSION, description=_SweAna_DESCRIPTION),
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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if self.config.name == "dalaj-ged": # This is the name of the configuration selected in BUILDER_CONFIGS above
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features = datasets.Features(
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{
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"sentence": datasets.Value("string"),
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"label": datasets.Value("string"),
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"meta": datasets.Value("string")
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}
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)
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elif self.config.name == 'absabank-imm':
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},
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)
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splits.append(split_test)
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if self.config.name in ("absabank-imm", "dalaj-ged", "swefaq", "swewic", "swedn"):
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data_dir_dev = dl_manager.download_and_extract(os.path.join(_URL,_TASKS[self.config.name],f"{_TASKS[self.config.name]}_dev.{file_format}"))
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split_dev = datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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},
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)
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splits.append(split_dev)
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+
if self.config.name in ("absabank-imm", "dalaj-ged", "swefaq", "swewic", "argumentation_sent", "swedn"):
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data_dir_train = dl_manager.download_and_extract(os.path.join(_URL,_TASKS[self.config.name],f"{_TASKS[self.config.name]}_train.{file_format}"))
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split_train = datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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df = pd.read_json(filepath, lines=True)
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for key, row in df.iterrows():
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if self.config.name == "dalaj-ged":
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# Yields examples as (key, example) tuples
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yield key, {
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"sentence": row["sentence"],
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"label": row["label"],
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"meta": row["meta"],
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}
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elif self.config.name == "absabank-imm":
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yield key, {
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