Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
discourse-mode-classification
paraphrase-identification
cross-lingual-similarity
headline-classification
License:
Update files from the datasets library (from 1.16.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.16.0
- README.md +1 -0
- dataset_infos.json +0 -0
- indic_glue.py +205 -147
README.md
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---
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paperswithcode_id: null
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---
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---
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+
pretty_name: IndicGLUE
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paperswithcode_id: null
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---
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dataset_infos.json
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The diff for this file is too large to render.
See raw diff
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indic_glue.py
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import csv
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import json
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-
import os
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import textwrap
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import pandas as pd
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@@ -515,137 +514,150 @@ class IndicGlue(datasets.GeneratorBasedBuilder):
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def _split_generators(self, dl_manager):
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if self.config.name.startswith("wnli"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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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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-
"datafile":
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"split": datasets.Split.TRAIN,
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"key": "train-split",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.VALIDATION,
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"key": "val-split",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.TEST,
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"key": "test-split",
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},
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),
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]
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if self.config.name.startswith("copa"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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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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-
"datafile":
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"split": datasets.Split.TRAIN,
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"key": "train-split",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.VALIDATION,
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"key": "val-split",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.TEST,
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"key": "test-split",
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},
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),
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]
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if self.config.name.startswith("sna"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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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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-
"datafile":
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"split": datasets.Split.TRAIN,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.VALIDATION,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.TEST,
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},
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),
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]
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if self.config.name.startswith("csqa"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.TEST,
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},
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)
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]
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if self.config.name.startswith("wstp"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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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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-
"datafile":
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"split": datasets.Split.TRAIN,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.VALIDATION,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.TEST,
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},
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),
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]
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@@ -655,127 +667,139 @@ class IndicGlue(datasets.GeneratorBasedBuilder):
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or self.config.name.startswith("iitp")
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or self.config.name.startswith("actsa")
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):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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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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-
"datafile":
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"split": datasets.Split.TRAIN,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.VALIDATION,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.TEST,
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},
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),
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]
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if self.config.name.startswith("bbca"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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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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-
"datafile":
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"split": datasets.Split.TRAIN,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.TEST,
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},
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),
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]
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if self.config.name.startswith("cvit"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"datafile": None,
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-
"src":
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-
"tgt":
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"split": datasets.Split.TEST,
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},
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)
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]
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if self.config.name.startswith("md"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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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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-
"datafile":
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"split": datasets.Split.TRAIN,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
|
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-
"datafile":
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"split": datasets.Split.VALIDATION,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
|
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-
"datafile":
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"split": datasets.Split.TEST,
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},
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),
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]
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if self.config.name.startswith("wiki-ner"):
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-
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task_name = self._get_task_name_from_data_url(self.config.data_url)
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-
dl_dir =
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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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-
"datafile":
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"split": datasets.Split.TRAIN,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.VALIDATION,
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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-
"datafile":
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"split": datasets.Split.TEST,
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},
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),
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]
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def _generate_examples(self, **args):
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"""Yields examples."""
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filepath = args["datafile"]
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if self.config.name.startswith("wnli"):
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if args["key"] == "test-split":
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-
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else:
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if self.config.name.startswith("copa"):
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if args["key"] == "test-split":
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-
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else:
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if self.config.name.startswith("sna"):
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-
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-
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if self.config.name.startswith("csqa"):
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-
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-
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-
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-
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df["out_of_context_options"].loc[df["out_of_context_options"].isnull()]
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if self.config.name.startswith("wstp"):
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-
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if (
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self.config.name.startswith("inltkh")
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or self.config.name.startswith("bbca")
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or self.config.name.startswith("iitp")
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):
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-
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-
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-
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if self.config.name.startswith("actsa"):
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-
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-
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-
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-
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if self.config.name.startswith("cvit"):
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source = args["src"]
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target = args["tgt"]
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if self.config.name.startswith("md"):
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-
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if self.config.name.startswith("wiki-ner"):
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-
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-
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tokens = []
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labels = []
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infos = []
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def _get_task_name_from_data_url(self, data_url):
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return data_url.split("/")[-1].split(".")[0]
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import csv
|
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import json
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import textwrap
|
7 |
|
8 |
import pandas as pd
|
|
|
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def _split_generators(self, dl_manager):
|
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if self.config.name.startswith("wnli"):
|
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+
archive = dl_manager.download(self.config.data_url)
|
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task_name = self._get_task_name_from_data_url(self.config.data_url)
|
519 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
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return [
|
521 |
datasets.SplitGenerator(
|
522 |
name=datasets.Split.TRAIN,
|
523 |
gen_kwargs={
|
524 |
+
"datafile": dl_dir + "/" + "train.csv",
|
525 |
"split": datasets.Split.TRAIN,
|
526 |
"key": "train-split",
|
527 |
+
"files": dl_manager.iter_archive(archive),
|
528 |
},
|
529 |
),
|
530 |
datasets.SplitGenerator(
|
531 |
name=datasets.Split.VALIDATION,
|
532 |
gen_kwargs={
|
533 |
+
"datafile": dl_dir + "/" + "dev.csv",
|
534 |
"split": datasets.Split.VALIDATION,
|
535 |
"key": "val-split",
|
536 |
+
"files": dl_manager.iter_archive(archive),
|
537 |
},
|
538 |
),
|
539 |
datasets.SplitGenerator(
|
540 |
name=datasets.Split.TEST,
|
541 |
gen_kwargs={
|
542 |
+
"datafile": dl_dir + "/" + "test.csv",
|
543 |
"split": datasets.Split.TEST,
|
544 |
"key": "test-split",
|
545 |
+
"files": dl_manager.iter_archive(archive),
|
546 |
},
|
547 |
),
|
548 |
]
|
549 |
|
550 |
if self.config.name.startswith("copa"):
|
551 |
+
archive = dl_manager.download(self.config.data_url)
|
552 |
task_name = self._get_task_name_from_data_url(self.config.data_url)
|
553 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
554 |
|
555 |
return [
|
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datasets.SplitGenerator(
|
557 |
name=datasets.Split.TRAIN,
|
558 |
gen_kwargs={
|
559 |
+
"datafile": dl_dir + "/" + "train.jsonl",
|
560 |
"split": datasets.Split.TRAIN,
|
561 |
"key": "train-split",
|
562 |
+
"files": dl_manager.iter_archive(archive),
|
563 |
},
|
564 |
),
|
565 |
datasets.SplitGenerator(
|
566 |
name=datasets.Split.VALIDATION,
|
567 |
gen_kwargs={
|
568 |
+
"datafile": dl_dir + "/" + "val.jsonl",
|
569 |
"split": datasets.Split.VALIDATION,
|
570 |
"key": "val-split",
|
571 |
+
"files": dl_manager.iter_archive(archive),
|
572 |
},
|
573 |
),
|
574 |
datasets.SplitGenerator(
|
575 |
name=datasets.Split.TEST,
|
576 |
gen_kwargs={
|
577 |
+
"datafile": dl_dir + "/" + "test.jsonl",
|
578 |
"split": datasets.Split.TEST,
|
579 |
"key": "test-split",
|
580 |
+
"files": dl_manager.iter_archive(archive),
|
581 |
},
|
582 |
),
|
583 |
]
|
584 |
|
585 |
if self.config.name.startswith("sna"):
|
586 |
+
archive = dl_manager.download(self.config.data_url)
|
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task_name = self._get_task_name_from_data_url(self.config.data_url)
|
588 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
589 |
|
590 |
return [
|
591 |
datasets.SplitGenerator(
|
592 |
name=datasets.Split.TRAIN,
|
593 |
gen_kwargs={
|
594 |
+
"datafile": dl_dir + "/" + "bn-train.csv",
|
595 |
"split": datasets.Split.TRAIN,
|
596 |
+
"files": dl_manager.iter_archive(archive),
|
597 |
},
|
598 |
),
|
599 |
datasets.SplitGenerator(
|
600 |
name=datasets.Split.VALIDATION,
|
601 |
gen_kwargs={
|
602 |
+
"datafile": dl_dir + "/" + "bn-valid.csv",
|
603 |
"split": datasets.Split.VALIDATION,
|
604 |
+
"files": dl_manager.iter_archive(archive),
|
605 |
},
|
606 |
),
|
607 |
datasets.SplitGenerator(
|
608 |
name=datasets.Split.TEST,
|
609 |
gen_kwargs={
|
610 |
+
"datafile": dl_dir + "/" + "bn-test.csv",
|
611 |
"split": datasets.Split.TEST,
|
612 |
+
"files": dl_manager.iter_archive(archive),
|
613 |
},
|
614 |
),
|
615 |
]
|
616 |
|
617 |
if self.config.name.startswith("csqa"):
|
618 |
+
archive = dl_manager.download(self.config.data_url)
|
619 |
task_name = self._get_task_name_from_data_url(self.config.data_url)
|
620 |
+
dl_dir = task_name
|
621 |
|
622 |
return [
|
623 |
datasets.SplitGenerator(
|
624 |
name=datasets.Split.TEST,
|
625 |
gen_kwargs={
|
626 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}.json",
|
627 |
"split": datasets.Split.TEST,
|
628 |
+
"files": dl_manager.iter_archive(archive),
|
629 |
},
|
630 |
)
|
631 |
]
|
632 |
|
633 |
if self.config.name.startswith("wstp"):
|
634 |
+
archive = dl_manager.download(self.config.data_url)
|
635 |
task_name = self._get_task_name_from_data_url(self.config.data_url)
|
636 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
637 |
|
638 |
return [
|
639 |
datasets.SplitGenerator(
|
640 |
name=datasets.Split.TRAIN,
|
641 |
gen_kwargs={
|
642 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-train.json",
|
643 |
"split": datasets.Split.TRAIN,
|
644 |
+
"files": dl_manager.iter_archive(archive),
|
645 |
},
|
646 |
),
|
647 |
datasets.SplitGenerator(
|
648 |
name=datasets.Split.VALIDATION,
|
649 |
gen_kwargs={
|
650 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-valid.json",
|
651 |
"split": datasets.Split.VALIDATION,
|
652 |
+
"files": dl_manager.iter_archive(archive),
|
653 |
},
|
654 |
),
|
655 |
datasets.SplitGenerator(
|
656 |
name=datasets.Split.TEST,
|
657 |
gen_kwargs={
|
658 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-test.json",
|
659 |
"split": datasets.Split.TEST,
|
660 |
+
"files": dl_manager.iter_archive(archive),
|
661 |
},
|
662 |
),
|
663 |
]
|
|
|
667 |
or self.config.name.startswith("iitp")
|
668 |
or self.config.name.startswith("actsa")
|
669 |
):
|
670 |
+
archive = dl_manager.download(self.config.data_url)
|
671 |
task_name = self._get_task_name_from_data_url(self.config.data_url)
|
672 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
673 |
|
674 |
return [
|
675 |
datasets.SplitGenerator(
|
676 |
name=datasets.Split.TRAIN,
|
677 |
gen_kwargs={
|
678 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-train.csv",
|
679 |
"split": datasets.Split.TRAIN,
|
680 |
+
"files": dl_manager.iter_archive(archive),
|
681 |
},
|
682 |
),
|
683 |
datasets.SplitGenerator(
|
684 |
name=datasets.Split.VALIDATION,
|
685 |
gen_kwargs={
|
686 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-valid.csv",
|
687 |
"split": datasets.Split.VALIDATION,
|
688 |
+
"files": dl_manager.iter_archive(archive),
|
689 |
},
|
690 |
),
|
691 |
datasets.SplitGenerator(
|
692 |
name=datasets.Split.TEST,
|
693 |
gen_kwargs={
|
694 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-test.csv",
|
695 |
"split": datasets.Split.TEST,
|
696 |
+
"files": dl_manager.iter_archive(archive),
|
697 |
},
|
698 |
),
|
699 |
]
|
700 |
|
701 |
if self.config.name.startswith("bbca"):
|
702 |
+
archive = dl_manager.download(self.config.data_url)
|
703 |
task_name = self._get_task_name_from_data_url(self.config.data_url)
|
704 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
705 |
|
706 |
return [
|
707 |
datasets.SplitGenerator(
|
708 |
name=datasets.Split.TRAIN,
|
709 |
gen_kwargs={
|
710 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-train.csv",
|
711 |
"split": datasets.Split.TRAIN,
|
712 |
+
"files": dl_manager.iter_archive(archive),
|
713 |
},
|
714 |
),
|
715 |
datasets.SplitGenerator(
|
716 |
name=datasets.Split.TEST,
|
717 |
gen_kwargs={
|
718 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-test.csv",
|
719 |
"split": datasets.Split.TEST,
|
720 |
+
"files": dl_manager.iter_archive(archive),
|
721 |
},
|
722 |
),
|
723 |
]
|
724 |
|
725 |
if self.config.name.startswith("cvit"):
|
726 |
+
archive = dl_manager.download(self.config.data_url)
|
727 |
task_name = self._get_task_name_from_data_url(self.config.data_url)
|
728 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
729 |
|
730 |
return [
|
731 |
datasets.SplitGenerator(
|
732 |
name=datasets.Split.TEST,
|
733 |
gen_kwargs={
|
734 |
"datafile": None,
|
735 |
+
"src": dl_dir + "/" + f"mkb.{self.config.name.split('.')[1].split('-')[0]}",
|
736 |
+
"tgt": dl_dir + "/" + f"mkb.{self.config.name.split('.')[1].split('-')[1]}",
|
737 |
"split": datasets.Split.TEST,
|
738 |
+
"files": dl_manager.iter_archive(archive),
|
739 |
},
|
740 |
)
|
741 |
]
|
742 |
|
743 |
if self.config.name.startswith("md"):
|
744 |
+
archive = dl_manager.download(self.config.data_url)
|
745 |
task_name = self._get_task_name_from_data_url(self.config.data_url)
|
746 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
747 |
|
748 |
return [
|
749 |
datasets.SplitGenerator(
|
750 |
name=datasets.Split.TRAIN,
|
751 |
gen_kwargs={
|
752 |
+
"datafile": dl_dir + "/" + "train.json",
|
753 |
"split": datasets.Split.TRAIN,
|
754 |
+
"files": dl_manager.iter_archive(archive),
|
755 |
},
|
756 |
),
|
757 |
datasets.SplitGenerator(
|
758 |
name=datasets.Split.VALIDATION,
|
759 |
gen_kwargs={
|
760 |
+
"datafile": dl_dir + "/" + "val.json",
|
761 |
"split": datasets.Split.VALIDATION,
|
762 |
+
"files": dl_manager.iter_archive(archive),
|
763 |
},
|
764 |
),
|
765 |
datasets.SplitGenerator(
|
766 |
name=datasets.Split.TEST,
|
767 |
gen_kwargs={
|
768 |
+
"datafile": dl_dir + "/" + "test.json",
|
769 |
"split": datasets.Split.TEST,
|
770 |
+
"files": dl_manager.iter_archive(archive),
|
771 |
},
|
772 |
),
|
773 |
]
|
774 |
|
775 |
if self.config.name.startswith("wiki-ner"):
|
776 |
+
archive = dl_manager.download(self.config.data_url)
|
777 |
task_name = self._get_task_name_from_data_url(self.config.data_url)
|
778 |
+
dl_dir = task_name + "/" + self.config.name.split(".")[1]
|
779 |
|
780 |
return [
|
781 |
datasets.SplitGenerator(
|
782 |
name=datasets.Split.TRAIN,
|
783 |
gen_kwargs={
|
784 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-train.txt",
|
785 |
"split": datasets.Split.TRAIN,
|
786 |
+
"files": dl_manager.iter_archive(archive),
|
787 |
},
|
788 |
),
|
789 |
datasets.SplitGenerator(
|
790 |
name=datasets.Split.VALIDATION,
|
791 |
gen_kwargs={
|
792 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-valid.txt",
|
793 |
"split": datasets.Split.VALIDATION,
|
794 |
+
"files": dl_manager.iter_archive(archive),
|
795 |
},
|
796 |
),
|
797 |
datasets.SplitGenerator(
|
798 |
name=datasets.Split.TEST,
|
799 |
gen_kwargs={
|
800 |
+
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-test.txt",
|
801 |
"split": datasets.Split.TEST,
|
802 |
+
"files": dl_manager.iter_archive(archive),
|
803 |
},
|
804 |
),
|
805 |
]
|
|
|
807 |
def _generate_examples(self, **args):
|
808 |
"""Yields examples."""
|
809 |
filepath = args["datafile"]
|
810 |
+
files = args["files"]
|
811 |
|
812 |
if self.config.name.startswith("wnli"):
|
813 |
if args["key"] == "test-split":
|
814 |
+
for path, f in files:
|
815 |
+
if path == filepath:
|
816 |
+
data = csv.DictReader((line.decode("utf-8") for line in f))
|
817 |
+
for id_, row in enumerate(data):
|
818 |
+
yield id_, {"hypothesis": row["sentence1"], "premise": row["sentence2"], "label": "None"}
|
819 |
+
break
|
820 |
else:
|
821 |
+
for path, f in files:
|
822 |
+
if path == filepath:
|
823 |
+
data = csv.DictReader((line.decode("utf-8") for line in f))
|
824 |
+
for id_, row in enumerate(data):
|
825 |
+
label = "entailment" if row["label"] else "not_entailment"
|
826 |
+
yield id_, {
|
827 |
+
"hypothesis": row["sentence1"],
|
828 |
+
"premise": row["sentence2"],
|
829 |
+
"label": label,
|
830 |
+
}
|
831 |
+
break
|
832 |
|
833 |
if self.config.name.startswith("copa"):
|
834 |
if args["key"] == "test-split":
|
835 |
+
for path, f in files:
|
836 |
+
if path == filepath:
|
837 |
+
lines = f.readlines()
|
838 |
+
data = map(lambda l: json.loads(l), lines)
|
839 |
+
data = list(data)
|
840 |
+
for id_, row in enumerate(data):
|
841 |
+
yield id_, {
|
842 |
+
"premise": row["premise"],
|
843 |
+
"choice1": row["choice1"],
|
844 |
+
"choice2": row["choice2"],
|
845 |
+
"question": row["question"],
|
846 |
+
"label": 0,
|
847 |
+
}
|
848 |
+
break
|
849 |
else:
|
850 |
+
for path, f in files:
|
851 |
+
if path == filepath:
|
852 |
+
lines = f.readlines()
|
853 |
+
data = map(lambda l: json.loads(l), lines)
|
854 |
+
data = list(data)
|
855 |
+
for id_, row in enumerate(data):
|
856 |
+
yield id_, {
|
857 |
+
"premise": row["premise"],
|
858 |
+
"choice1": row["choice1"],
|
859 |
+
"choice2": row["choice2"],
|
860 |
+
"question": row["question"],
|
861 |
+
"label": row["label"],
|
862 |
+
}
|
863 |
+
break
|
864 |
|
865 |
if self.config.name.startswith("sna"):
|
866 |
+
for path, f in files:
|
867 |
+
if path == filepath:
|
868 |
+
df = pd.read_csv(f, names=["label", "text"])
|
869 |
+
for id_, row in df.iterrows():
|
870 |
+
yield id_, {"text": row["text"], "label": row["label"]}
|
871 |
+
break
|
872 |
|
873 |
if self.config.name.startswith("csqa"):
|
874 |
+
for path, f in files:
|
875 |
+
if path == filepath:
|
876 |
+
data = json.load(f)
|
877 |
+
df = pd.DataFrame(data["cloze_data"])
|
878 |
+
df["out_of_context_options"].loc[df["out_of_context_options"].isnull()] = (
|
879 |
+
df["out_of_context_options"].loc[df["out_of_context_options"].isnull()].apply(lambda x: [])
|
880 |
+
)
|
881 |
+
for id_, row in df.iterrows():
|
882 |
+
yield id_, {
|
883 |
+
"question": row["question"],
|
884 |
+
"answer": row["answer"],
|
885 |
+
"category": row["category"],
|
886 |
+
"title": row["title"],
|
887 |
+
"out_of_context_options": row["out_of_context_options"],
|
888 |
+
"options": row["options"],
|
889 |
+
}
|
890 |
+
break
|
891 |
|
892 |
if self.config.name.startswith("wstp"):
|
893 |
+
for path, f in files:
|
894 |
+
if path == filepath:
|
895 |
+
df = pd.read_json(f)
|
896 |
+
for id_, row in df.iterrows():
|
897 |
+
yield id_, {
|
898 |
+
"sectionText": row["sectionText"],
|
899 |
+
"correctTitle": row["correctTitle"],
|
900 |
+
"titleA": row["titleA"],
|
901 |
+
"titleB": row["titleB"],
|
902 |
+
"titleC": row["titleC"],
|
903 |
+
"titleD": row["titleD"],
|
904 |
+
"url": row["url"],
|
905 |
+
}
|
906 |
+
break
|
907 |
|
908 |
if (
|
909 |
self.config.name.startswith("inltkh")
|
910 |
or self.config.name.startswith("bbca")
|
911 |
or self.config.name.startswith("iitp")
|
912 |
):
|
913 |
+
for path, f in files:
|
914 |
+
if path == filepath:
|
915 |
+
df = pd.read_csv(f, names=["label", "text"])
|
916 |
+
for id_, row in df.iterrows():
|
917 |
+
yield id_, {"text": row["text"], "label": row["label"]}
|
918 |
+
break
|
919 |
|
920 |
if self.config.name.startswith("actsa"):
|
921 |
+
for path, f in files:
|
922 |
+
if path == filepath:
|
923 |
+
df = pd.read_csv(f, names=["label", "text"])
|
924 |
+
for id_, row in df.iterrows():
|
925 |
+
label = "positive" if row["label"] else "negative"
|
926 |
+
yield id_, {"text": row["text"], "label": label}
|
927 |
+
break
|
928 |
|
929 |
if self.config.name.startswith("cvit"):
|
930 |
source = args["src"]
|
931 |
target = args["tgt"]
|
932 |
+
src, tgt = None, None
|
933 |
+
for path, f in files:
|
934 |
+
if path == source:
|
935 |
+
src = f.read().decode("utf-8").splitlines()
|
936 |
+
elif path == target:
|
937 |
+
tgt = f.read().decode("utf-8").splitlines()
|
938 |
+
if src is not None and tgt is not None:
|
939 |
+
for id_, row in enumerate(zip(src, tgt)):
|
940 |
+
yield id_, {"sentence1": row[0], "sentence2": row[1]}
|
941 |
+
break
|
942 |
|
943 |
if self.config.name.startswith("md"):
|
944 |
+
for path, f in files:
|
945 |
+
if path == filepath:
|
946 |
+
df = pd.read_json(f)
|
947 |
+
for id_, row in df.iterrows():
|
948 |
+
yield id_, {
|
949 |
+
"story_number": row["Story_no"],
|
950 |
+
"sentence": row["Sentence"],
|
951 |
+
"discourse_mode": row["Discourse Mode"],
|
952 |
+
"id": row["id"],
|
953 |
+
}
|
954 |
+
break
|
955 |
|
956 |
if self.config.name.startswith("wiki-ner"):
|
957 |
+
for path, f in files:
|
958 |
+
if path == filepath:
|
959 |
+
data = f.read().decode("utf-8").splitlines()
|
960 |
tokens = []
|
961 |
labels = []
|
962 |
infos = []
|
963 |
+
for id_, row in enumerate(data):
|
964 |
+
row = row.split()
|
965 |
+
|
966 |
+
if len(row) == 0:
|
967 |
+
yield id_, {"tokens": tokens, "ner_tags": labels, "additional_info": infos}
|
968 |
+
tokens = []
|
969 |
+
labels = []
|
970 |
+
infos = []
|
971 |
+
continue
|
972 |
+
|
973 |
+
tokens.append(row[0])
|
974 |
+
labels.append(row[-1])
|
975 |
+
infos.append(row[1:-1])
|
976 |
+
break
|
977 |
|
978 |
def _get_task_name_from_data_url(self, data_url):
|
979 |
return data_url.split("/")[-1].split(".")[0]
|