kilt_tasks / kilt_tasks.py
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""KILT tasks training and evaluation data"""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{fb_kilt,
author = {Fabio Petroni and
Aleksandra Piktus and
Angela Fan and
Patrick Lewis and
Majid Yazdani and
Nicola De Cao and
James Thorne and
Yacine Jernite and
Vassilis Plachouras and
Tim Rockt\"aschel and
Sebastian Riedel},
title = {{KILT:} a {B}enchmark for {K}nowledge {I}ntensive {L}anguage {T}asks},
journal = {CoRR},
archivePrefix = {arXiv},
year = {2020},
"""
_DESCRIPTION = """\
KILT tasks training and evaluation data.
- [FEVER](https://fever.ai) | Fact Checking | fever
- [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) | Entity Linking | aidayago2
- [WNED-WIKI](https://github.com/U-Alberta/wned) | Entity Linking | wned
- [WNED-CWEB](https://github.com/U-Alberta/wned) | Entity Linking | cweb
- [T-REx](https://hadyelsahar.github.io/t-rex) | Slot Filling | trex
- [Zero-Shot RE](http://nlp.cs.washington.edu/zeroshot) | Slot Filling | structured_zeroshot
- [Natural Questions](https://ai.google.com/research/NaturalQuestions) | Open Domain QA | nq
- [HotpotQA](https://hotpotqa.github.io) | Open Domain QA | hotpotqa
- [TriviaQA](http://nlp.cs.washington.edu/triviaqa) | Open Domain QA | triviaqa
- [ELI5](https://facebookresearch.github.io/ELI5/explore.html) | Open Domain QA | eli5
- [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia) | Dialogue | wow
To finish linking TriviaQA questions to the IDs provided, follow the instructions [here](http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md).
"""
_DATA_URLS = {
"fever": {
"train": "http://dl.fbaipublicfiles.com/KILT/fever-train-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/fever-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/fever-test_without_answers-kilt.jsonl",
},
"aidayago2": {
"train": "http://dl.fbaipublicfiles.com/KILT/aidayago2-train-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/aidayago2-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/aidayago2-test_without_answers-kilt.jsonl",
},
"wned": {
"validation": "http://dl.fbaipublicfiles.com/KILT/wned-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/wned-test_without_answers-kilt.jsonl",
},
"cweb": {
"validation": "http://dl.fbaipublicfiles.com/KILT/cweb-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/cweb-test_without_answers-kilt.jsonl",
},
"trex": {
"train": "http://dl.fbaipublicfiles.com/KILT/trex-train-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/trex-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/trex-test_without_answers-kilt.jsonl",
},
"structured_zeroshot": {
"train": "http://dl.fbaipublicfiles.com/KILT/structured_zeroshot-train-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/structured_zeroshot-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/structured_zeroshot-test_without_answers-kilt.jsonl",
},
"nq": {
"train": "http://dl.fbaipublicfiles.com/KILT/nq-train-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/nq-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/nq-test_without_answers-kilt.jsonl",
},
"hotpotqa": {
"train": "http://dl.fbaipublicfiles.com/KILT/hotpotqa-train-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/hotpotqa-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/hotpotqa-test_without_answers-kilt.jsonl",
},
"triviaqa_support_only": {
"train": "http://dl.fbaipublicfiles.com/KILT/triviaqa-train_id-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/triviaqa-dev_id-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/triviaqa-test_id_without_answers-kilt.jsonl",
},
"eli5": {
"train": "http://dl.fbaipublicfiles.com/KILT/eli5-train-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/eli5-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/eli5-test_without_answers-kilt.jsonl",
},
"wow": {
"train": "http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl",
"validation": "http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl",
"test": "http://dl.fbaipublicfiles.com/KILT/wow-test_without_answers-kilt.jsonl",
},
}
class KiltTasks(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="triviaqa_support_only",
version=datasets.Version("1.0.0"),
description="Supporting paragraphs information for the TriviaQA task",
)
] + [
datasets.BuilderConfig(
name=k, version=datasets.Version("1.0.0"), description=f"Task data and supporting paragraphs for {k}"
)
for k in _DATA_URLS
if k != "triviaqa_support_only"
]
DEFAULT_CONFIG_NAME = "nq"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"input": datasets.Value("string"),
"meta": {
"left_context": datasets.Value("string"),
"mention": datasets.Value("string"),
"right_context": datasets.Value("string"),
"partial_evidence": [
{
"start_paragraph_id": datasets.Value("int32"),
"end_paragraph_id": datasets.Value("int32"),
"title": datasets.Value("string"),
"section": datasets.Value("string"),
"wikipedia_id": datasets.Value("string"),
"meta": {"evidence_span": [datasets.Value("string")]},
}
],
"obj_surface": [datasets.Value("string")],
"sub_surface": [datasets.Value("string")],
"subj_aliases": [datasets.Value("string")],
"template_questions": [datasets.Value("string")],
},
"output": [
{
"answer": datasets.Value("string"),
"meta": {"score": datasets.Value("int32")},
"provenance": [
{
"bleu_score": datasets.Value("float32"),
"start_character": datasets.Value("int32"),
"start_paragraph_id": datasets.Value("int32"),
"end_character": datasets.Value("int32"),
"end_paragraph_id": datasets.Value("int32"),
"meta": {
"fever_page_id": datasets.Value("string"),
"fever_sentence_id": datasets.Value("int32"),
"annotation_id": datasets.Value("string"), # int runs into overflow issues
"yes_no_answer": datasets.Value("string"),
"evidence_span": [datasets.Value("string")],
},
"section": datasets.Value("string"),
"title": datasets.Value("string"),
"wikipedia_id": datasets.Value("string"),
}
],
}
],
}
),
supervised_keys=None,
homepage="https://github.com/facebookresearch/KILT",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
file_paths = dl_manager.download_and_extract(_DATA_URLS[self.config.name])
return [
datasets.SplitGenerator(name=split, gen_kwargs={"filepath": downloaded_path})
for split, downloaded_path in file_paths.items()
]
def _generate_examples(self, filepath):
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
for idx, line in enumerate(f):
article = json.loads(line.strip())
article["input"] = article.get("input", "")
# meta
article["meta"] = article.get("meta", {})
for k in ["left_context", "mention", "right_context"]:
article["meta"][k] = article["meta"].get(k, "")
for k in ["obj_surface", "sub_surface", "subj_aliases", "template_questions"]:
article["meta"][k] = article["meta"].get(k, [])
# partial evidence
article["meta"]["partial_evidence"] = [
{
"start_paragraph_id": partial.get("start_paragraph_id", -1),
"end_paragraph_id": partial.get("end_paragraph_id", -1),
"title": partial.get("title", ""),
"section": partial.get("section", ""),
"wikipedia_id": partial.get("wikipedia_id", ""),
"meta": {"evidence_span": partial.get("meta", {}).get("evidence_span", [])},
}
for partial in article["meta"].get("partial_evidence", [])
]
# output
article["output"] = [
{
"answer": output.get("answer", ""),
"meta": output.get("meta", {"score": -1}),
"provenance": [
{
"bleu_score": provenance.get("bleu_score", -1.0),
"start_character": provenance.get("start_character", -1),
"start_paragraph_id": provenance.get("start_paragraph_id", -1),
"end_character": provenance.get("end_character", -1),
"end_paragraph_id": provenance.get("end_paragraph_id", -1),
"meta": {
"fever_page_id": provenance.get("meta", {}).get("fever_page_id", ""),
"fever_sentence_id": provenance.get("meta", {}).get("fever_sentence_id", -1),
"annotation_id": str(
provenance.get("meta", {}).get("annotation_id", -1)
), # int runs into overflow issues
"yes_no_answer": provenance.get("meta", {}).get("yes_no_answer", ""),
"evidence_span": provenance.get("meta", {}).get("evidence_span", []),
},
"section": provenance.get("section", ""),
"title": provenance.get("title", ""),
"wikipedia_id": provenance.get("wikipedia_id", ""),
}
for provenance in output.get("provenance", [])
],
}
for output in article.get("output", [])
]
yield idx, article