Delete loading script

kilt_tasks.py

@@ -1,259 +0,0 @@
-# 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