Update inspec.py
Browse files
inspec.py
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@@ -1,20 +1,14 @@
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import csv
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import json
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import os
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import datasets
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from typing import List, Any
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# _SPLIT = ['train', 'test', 'valid']
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_CITATION = """\
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author: amardeep
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"""
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_DESCRIPTION = """\
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This new dataset is designed to solve kp NLP task and is crafted with a lot of care.
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"""
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_HOMEPAGE = ""
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@@ -34,37 +28,36 @@ _URLS = {
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class TestLDKP(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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VERSION = datasets.Version("
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="extraction", version=VERSION,
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datasets.BuilderConfig(name="
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datasets.BuilderConfig(name="
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]
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DEFAULT_CONFIG_NAME = "extraction"
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def _info(self):
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if self.config.name == "extraction"
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features = datasets.Features(
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{
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"id": datasets.Value("int64"),
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"document": datasets.features.Sequence(datasets.Value("string")),
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"doc_bio_tags": datasets.features.Sequence(datasets.Value("string"))
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}
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)
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elif self.config.name == "generation"
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features = datasets.Features(
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{
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"id": datasets.Value("int64"),
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"document": datasets.features.Sequence(datasets.Value("string")),
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"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
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"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string"))
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}
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)
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else:
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@@ -78,18 +71,17 @@ class TestLDKP(datasets.GeneratorBasedBuilder):
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"other_metadata": datasets.features.Sequence(
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{
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"text": datasets.features.Sequence(datasets.Value("string")),
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"bio_tags":datasets.features.Sequence(datasets.Value("string"))
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}
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)
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features,
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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@@ -98,7 +90,7 @@ class TestLDKP(datasets.GeneratorBasedBuilder):
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)
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def _split_generators(self, dl_manager):
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data_dir = dl_manager.download_and_extract(_URLS)
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return [
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datasets.SplitGenerator(
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@@ -139,20 +131,7 @@ class TestLDKP(datasets.GeneratorBasedBuilder):
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"document": data["document"],
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"doc_bio_tags": data["doc_bio_tags"]
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}
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elif self.config.name == "
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yield key, {
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"id": data['paper_id'],
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"document": data["document"]+data["other_metadata"]['text'],
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"doc_bio_tags": data["document_tags"] + data["other_metadata"]['bio_tags']
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}
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elif self.config.name == "ldkp_generation":
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yield key, {
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"id": data['paper_id'],
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"document": data["document"]+data["other_metadata"]['text'],
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"extractive_keyphrases": data["extractive_keyphrases"],
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"abstractive_keyphrases": data["abstractive_keyphrases"]
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}
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elif self.config.name == "generation":
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yield key, {
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"id": data['paper_id'],
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"document": data["document"],
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import json
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import datasets
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# _SPLIT = ['train', 'test', 'valid']
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_CITATION = """\
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"""
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_DESCRIPTION = """\
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"""
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_HOMEPAGE = ""
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class TestLDKP(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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VERSION = datasets.Version("0.1")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="extraction", version=VERSION,
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description="This part of my dataset covers extraction"),
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datasets.BuilderConfig(name="generation", version=VERSION,
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description="This part of my dataset covers generation"),
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datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"),
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]
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DEFAULT_CONFIG_NAME = "extraction"
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def _info(self):
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if self.config.name == "extraction": # 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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"id": datasets.Value("int64"),
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"document": datasets.features.Sequence(datasets.Value("string")),
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"doc_bio_tags": datasets.features.Sequence(datasets.Value("string"))
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}
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)
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elif self.config.name == "generation":
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features = datasets.Features(
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{
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"id": datasets.Value("int64"),
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"document": datasets.features.Sequence(datasets.Value("string")),
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"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
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"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string"))
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}
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)
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else:
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"other_metadata": datasets.features.Sequence(
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{
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"text": datasets.features.Sequence(datasets.Value("string")),
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"bio_tags": datasets.features.Sequence(datasets.Value("string"))
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}
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)
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features,
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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)
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def _split_generators(self, dl_manager):
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data_dir = dl_manager.download_and_extract(_URLS)
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return [
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datasets.SplitGenerator(
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"document": data["document"],
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"doc_bio_tags": data["doc_bio_tags"]
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}
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elif self.config.name == "generation":
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yield key, {
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"id": data['paper_id'],
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"document": data["document"],
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