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"""GEM: Generation Evaluation Metrics supporting datasets""" |
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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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_CITATION = """\ |
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@article{gem_benchmark, |
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author = {Sebastian Gehrmann and |
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Tosin P. Adewumi and |
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Karmanya Aggarwal and |
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Pawan Sasanka Ammanamanchi and |
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Aremu Anuoluwapo and |
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Antoine Bosselut and |
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Khyathi Raghavi Chandu and |
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Miruna{-}Adriana Clinciu and |
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Dipanjan Das and |
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Kaustubh D. Dhole and |
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Wanyu Du and |
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Esin Durmus and |
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Ondrej Dusek and |
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Chris Emezue and |
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Varun Gangal and |
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Cristina Garbacea and |
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Tatsunori Hashimoto and |
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Yufang Hou and |
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Yacine Jernite and |
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Harsh Jhamtani and |
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Yangfeng Ji and |
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Shailza Jolly and |
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Dhruv Kumar and |
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Faisal Ladhak and |
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Aman Madaan and |
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Mounica Maddela and |
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Khyati Mahajan and |
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Saad Mahamood and |
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Bodhisattwa Prasad Majumder and |
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Pedro Henrique Martins and |
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Angelina McMillan{-}Major and |
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Simon Mille and |
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Emiel van Miltenburg and |
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Moin Nadeem and |
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Shashi Narayan and |
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Vitaly Nikolaev and |
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Rubungo Andre Niyongabo and |
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Salomey Osei and |
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Ankur P. Parikh and |
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Laura Perez{-}Beltrachini and |
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Niranjan Ramesh Rao and |
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Vikas Raunak and |
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Juan Diego Rodriguez and |
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Sashank Santhanam and |
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Joao Sedoc and |
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Thibault Sellam and |
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Samira Shaikh and |
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Anastasia Shimorina and |
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Marco Antonio Sobrevilla Cabezudo and |
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Hendrik Strobelt and |
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Nishant Subramani and |
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Wei Xu and |
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Diyi Yang and |
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Akhila Yerukola and |
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Jiawei Zhou}, |
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title = {The {GEM} Benchmark: Natural Language Generation, its Evaluation and |
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Metrics}, |
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journal = {CoRR}, |
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volume = {abs/2102.01672}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2102.01672}, |
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archivePrefix = {arXiv}, |
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eprint = {2102.01672} |
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} |
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""" |
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_DESCRIPTION = """\ |
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GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation, |
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both through human annotations and automated Metrics. |
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|
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GEM aims to: |
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- measure NLG progress across 13 datasets spanning many NLG tasks and languages. |
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- provide an in-depth analysis of data and models presented via data statements and challenge sets. |
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- develop standards for evaluation of generated text using both automated and human metrics. |
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|
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It is our goal to regularly update GEM and to encourage toward more inclusive practices in dataset development |
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by extending existing data or developing datasets for additional languages. |
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""" |
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_HOMEPAGE = "https://gem-benchmark.github.io/" |
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_LICENSE = "CC-BY-SA-4.0" |
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_TASKS = { |
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"summarization": { |
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"mlsum": ["mlsum_de", "mlsum_es"], |
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"wiki_lingua": [ |
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"wiki_lingua_es_en_v0", |
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"wiki_lingua_ru_en_v0", |
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"wiki_lingua_tr_en_v0", |
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"wiki_lingua_vi_en_v0", |
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"wiki_lingua_arabic_ar", |
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"wiki_lingua_chinese_zh", |
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"wiki_lingua_czech_cs", |
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"wiki_lingua_dutch_nl", |
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"wiki_lingua_english_en", |
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"wiki_lingua_french_fr", |
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"wiki_lingua_german_de", |
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"wiki_lingua_hindi_hi", |
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"wiki_lingua_indonesian_id", |
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"wiki_lingua_italian_it", |
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"wiki_lingua_japanese_ja", |
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"wiki_lingua_korean_ko", |
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"wiki_lingua_portuguese_pt", |
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"wiki_lingua_russian_ru", |
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"wiki_lingua_spanish_es", |
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"wiki_lingua_thai_th", |
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"wiki_lingua_turkish_tr", |
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"wiki_lingua_vietnamese_vi", |
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], |
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"xsum": ["xsum"], |
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}, |
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"struct2text": { |
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"common_gen": ["common_gen"], |
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"cs_restaurants": ["cs_restaurants"], |
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"dart": ["dart"], |
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"e2e": ["e2e_nlg"], |
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"totto": ["totto"], |
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"web_nlg": ["web_nlg_en", "web_nlg_ru"], |
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}, |
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"simplification": { |
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"wiki_auto_asset_turk": ["wiki_auto_asset_turk"], |
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}, |
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"dialog": { |
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"schema_guided_dialog": ["schema_guided_dialog"], |
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}, |
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} |
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_URLs = { |
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"common_gen": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/common_gen/commongen_data.zip", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/common_gen.zip", |
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}, |
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"cs_restaurants": { |
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"train": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/train.json", |
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"validation": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/devel.json", |
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"test": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/test.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/cs_restaurants.zip", |
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}, |
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"dart": { |
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"train": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-train.json", |
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"validation": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-dev.json", |
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"test": "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-test.json", |
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}, |
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"e2e_nlg": { |
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"train": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/train-fixed.no-ol.csv", |
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"validation": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/devel-fixed.no-ol.csv", |
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"test": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/test-fixed.csv", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/e2e_nlg.zip", |
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}, |
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"mlsum_de": { |
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"train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_train.zip", |
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"validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_val.zip", |
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"test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_test.zip", |
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"bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_de.zip", |
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}, |
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"mlsum_es": { |
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"train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_train.zip", |
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"validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_val.zip", |
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"test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_test.zip", |
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"bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_es.zip", |
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}, |
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"schema_guided_dialog": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_sgd_context.zip", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/schema_guided_dialog.zip", |
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}, |
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"totto": { |
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"data": "https://storage.googleapis.com/totto-public/totto_data.zip", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/totto.zip", |
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}, |
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"web_nlg_en": { |
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"train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_train.json", |
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"validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_val.json", |
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"test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_test.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_en.zip", |
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}, |
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"web_nlg_ru": { |
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"train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_train.json", |
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"validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_val.json", |
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"test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_test.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_ru.zip", |
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}, |
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"wiki_auto_asset_turk": { |
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"train": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.tsv", |
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"validation": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/valid.tsv", |
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"test_turk": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_turk_detokenized.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/wiki_auto_asset_turk_train_valid.zip", |
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}, |
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"wiki_lingua_es_en_v0": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", |
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}, |
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"wiki_lingua_ru_en_v0": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", |
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}, |
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"wiki_lingua_tr_en_v0": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", |
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}, |
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"wiki_lingua_vi_en_v0": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip", |
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}, |
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"wiki_lingua_arabic_ar": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/arabic.zip", |
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}, |
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"wiki_lingua_chinese_zh": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/chinese.zip", |
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}, |
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"wiki_lingua_czech_cs": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/czech.zip", |
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}, |
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"wiki_lingua_dutch_nl": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/dutch.zip", |
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}, |
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"wiki_lingua_english_en": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/english.zip", |
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}, |
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"wiki_lingua_french_fr": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/french.zip", |
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}, |
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"wiki_lingua_german_de": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/german.zip", |
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}, |
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"wiki_lingua_hindi_hi": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/hindi.zip", |
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}, |
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"wiki_lingua_indonesian_id": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/indonesian.zip", |
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}, |
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"wiki_lingua_italian_it": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/italian.zip", |
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}, |
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"wiki_lingua_japanese_ja": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/japanese.zip", |
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}, |
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"wiki_lingua_korean_ko": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/korean.zip", |
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}, |
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"wiki_lingua_portuguese_pt": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/portuguese.zip", |
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}, |
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"wiki_lingua_russian_ru": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/russian.zip", |
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}, |
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"wiki_lingua_spanish_es": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/spanish.zip", |
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}, |
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"wiki_lingua_thai_th": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/thai.zip", |
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}, |
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"wiki_lingua_turkish_tr": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/turkish.zip", |
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}, |
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"wiki_lingua_vietnamese_vi": { |
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"data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/vietnamese.zip", |
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}, |
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"xsum": { |
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"data": "http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz", |
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"splits": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_xsum_confidence_0.8.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/xsum.zip", |
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}, |
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} |
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_URLs["wiki_auto_asset_turk"][ |
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"test_asset_orig" |
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] = "https://github.com/facebookresearch/asset/raw/main/dataset/asset.test.orig" |
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for i in range(10): |
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_URLs["wiki_auto_asset_turk"][ |
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f"test_asset_{i}" |
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] = f"https://github.com/facebookresearch/asset/raw/main/dataset/asset.test.simp.{i}" |
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|
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_SGD_ACTS = [ |
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"AFFIRM", |
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"AFFIRM_INTENT", |
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"CONFIRM", |
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"GOODBYE", |
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"INFORM", |
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"INFORM_COUNT", |
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"INFORM_INTENT", |
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"NEGATE", |
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"NEGATE_INTENT", |
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"NOTIFY_FAILURE", |
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"NOTIFY_SUCCESS", |
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"OFFER", |
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"OFFER_INTENT", |
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"REQUEST", |
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"REQUEST_ALTS", |
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"REQ_MORE", |
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"SELECT", |
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"THANK_YOU", |
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] |
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_XSUM_REMOVE_LINES = set( |
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[ |
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"Share this with\n", |
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"Email\n", |
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"Facebook\n", |
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"Messenger\n", |
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"Twitter\n", |
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"Pinterest\n", |
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"WhatsApp\n", |
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"Linkedin\n", |
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"LinkedIn\n", |
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"Copy this link\n", |
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"These are external links and will open in a new window\n", |
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] |
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) |
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class Gem(datasets.GeneratorBasedBuilder): |
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"""GEM: datasets supporting the Generation Evaluation Metrics 2021 shared task.""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name=conf, |
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version=datasets.Version("1.1.0"), |
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description=f"GEM benchmark: {task} task, {conf} subset", |
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) |
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for task, dset_confs in _TASKS.items() |
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for conf_list in dset_confs.values() |
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for conf in conf_list |
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] |
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DEFAULT_CONFIG_NAME = "common_gen" |
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|
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def _info(self): |
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if self.config.name == "common_gen": |
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features = datasets.Features( |
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{ |
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"gem_id": datasets.Value("string"), |
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"gem_parent_id": datasets.Value("string"), |
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"concept_set_id": datasets.Value("int32"), |
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"concepts": [datasets.Value("string")], |
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"target": datasets.Value("string"), |
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"references": [datasets.Value("string")], |
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} |
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) |
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elif self.config.name == "cs_restaurants": |
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features = datasets.Features( |
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{ |
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"gem_id": datasets.Value("string"), |
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"gem_parent_id": datasets.Value("string"), |
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"dialog_act": datasets.Value("string"), |
|
"dialog_act_delexicalized": datasets.Value("string"), |
|
"target_delexicalized": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
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} |
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) |
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elif self.config.name == "dart": |
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features = datasets.Features( |
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{ |
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"gem_id": datasets.Value("string"), |
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"gem_parent_id": datasets.Value("string"), |
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"dart_id": datasets.Value("int32"), |
|
"tripleset": [[datasets.Value("string")]], |
|
"subtree_was_extended": datasets.Value("bool"), |
|
"target_sources": [datasets.Value("string")], |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
} |
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) |
|
elif self.config.name == "e2e_nlg": |
|
features = datasets.Features( |
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{ |
|
"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"meaning_representation": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
} |
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) |
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elif self.config.name.startswith("mlsum"): |
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features = datasets.Features( |
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{ |
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"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"text": datasets.Value("string"), |
|
"topic": datasets.Value("string"), |
|
"url": datasets.Value("string"), |
|
"title": datasets.Value("string"), |
|
"date": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
} |
|
) |
|
elif self.config.name == "schema_guided_dialog": |
|
features = datasets.Features( |
|
{ |
|
"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"dialog_acts": [ |
|
{ |
|
"act": datasets.ClassLabel(names=_SGD_ACTS), |
|
"slot": datasets.Value("string"), |
|
"values": [datasets.Value("string")], |
|
} |
|
], |
|
"context": [datasets.Value("string")], |
|
"dialog_id": datasets.Value("string"), |
|
"service": datasets.Value("string"), |
|
"turn_id": datasets.Value("int32"), |
|
"prompt": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
} |
|
) |
|
elif self.config.name == "totto": |
|
features = datasets.Features( |
|
{ |
|
"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"totto_id": datasets.Value("int32"), |
|
"table_page_title": datasets.Value("string"), |
|
"table_webpage_url": datasets.Value("string"), |
|
"table_section_title": datasets.Value("string"), |
|
"table_section_text": datasets.Value("string"), |
|
"table": [ |
|
[ |
|
{ |
|
"column_span": datasets.Value("int32"), |
|
"is_header": datasets.Value("bool"), |
|
"row_span": datasets.Value("int32"), |
|
"value": datasets.Value("string"), |
|
} |
|
] |
|
], |
|
"highlighted_cells": [[datasets.Value("int32")]], |
|
"example_id": datasets.Value("string"), |
|
"sentence_annotations": [ |
|
{ |
|
"original_sentence": datasets.Value("string"), |
|
"sentence_after_deletion": datasets.Value("string"), |
|
"sentence_after_ambiguity": datasets.Value("string"), |
|
"final_sentence": datasets.Value("string"), |
|
} |
|
], |
|
"overlap_subset": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
}, |
|
) |
|
elif self.config.name.startswith("web_nlg"): |
|
features = datasets.Features( |
|
{ |
|
"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"input": [datasets.Value("string")], |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
"category": datasets.Value("string"), |
|
"webnlg_id": datasets.Value("string"), |
|
} |
|
) |
|
elif self.config.name == "wiki_auto_asset_turk": |
|
features = datasets.Features( |
|
{ |
|
"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"source": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
} |
|
) |
|
elif self.config.name.startswith("wiki_lingua"): |
|
if "v0" in self.config.name: |
|
features = datasets.Features( |
|
{ |
|
"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"source": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
} |
|
) |
|
else: |
|
ln = self.config.name.split("_")[-1] |
|
features = datasets.Features( |
|
{ |
|
"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"source_aligned": datasets.Translation(languages=[ln, "en"]), |
|
"target_aligned": datasets.Translation(languages=[ln, "en"]), |
|
"source": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
} |
|
) |
|
elif self.config.name == "xsum": |
|
features = datasets.Features( |
|
{ |
|
"gem_id": datasets.Value("string"), |
|
"gem_parent_id": datasets.Value("string"), |
|
"xsum_id": datasets.Value("string"), |
|
"document": datasets.Value("string"), |
|
"target": datasets.Value("string"), |
|
"references": [datasets.Value("string")], |
|
} |
|
) |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
supervised_keys=None, |
|
homepage=_HOMEPAGE, |
|
license=_LICENSE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
dl_dir = dl_manager.download_and_extract(_URLs[self.config.name]) |
|
if self.config.name == "common_gen": |
|
challenge_sets = [ |
|
("challenge_train_sample", "train_common_gen_RandomSample500.json"), |
|
("challenge_validation_sample", "validation_common_gen_RandomSample500.json"), |
|
("challenge_test_scramble", "test_common_gen_ScrambleInputStructure500.json"), |
|
] |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["data"], "commongen.train.jsonl"), |
|
"split": "train", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["data"], "commongen.dev.jsonl"), |
|
"split": "validation", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["data"], "commongen.test_noref.jsonl"), |
|
"split": "test", |
|
}, |
|
), |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
elif self.config.name == "cs_restaurants": |
|
challenge_sets = [ |
|
("challenge_train_sample", "train_cs_restaurants_RandomSample500.json"), |
|
("challenge_validation_sample", "validation_cs_restaurants_RandomSample500.json"), |
|
("challenge_test_scramble", "test_cs_restaurants_ScrambleInputStructure500.json"), |
|
] |
|
return [ |
|
datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) |
|
for spl in ["train", "validation", "test"] |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
elif self.config.name == "dart": |
|
return [ |
|
datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) |
|
for spl in ["train", "validation", "test"] |
|
] |
|
elif self.config.name == "e2e_nlg": |
|
challenge_sets = [ |
|
("challenge_train_sample", "train_e2e_nlg_RandomSample500.json"), |
|
("challenge_validation_sample", "validation_e2e_nlg_RandomSample500.json"), |
|
("challenge_test_scramble", "test_e2e_nlg_ScrambleInputStructure500.json"), |
|
] |
|
return [ |
|
datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) |
|
for spl in ["train", "validation", "test"] |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
elif self.config.name.startswith("mlsum"): |
|
lang = self.config.name.split("_")[1] |
|
challenge_sets = [ |
|
("challenge_train_sample", f"train_mlsum_{lang}_RandomSample500.json"), |
|
("challenge_validation_sample", f"validation_mlsum_{lang}_RandomSample500.json"), |
|
("challenge_test_covid", f"{lang}_test_covid19_cleaned.jsonl"), |
|
] |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["train"], lang + "_train.jsonl"), |
|
"split": "train", |
|
"lang": lang, |
|
"filepaths": dl_dir["bad_ids"], |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["validation"], lang + "_val.jsonl"), |
|
"split": "validation", |
|
"lang": lang, |
|
"filepaths": dl_dir["bad_ids"], |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["test"], lang + "_test.jsonl"), |
|
"split": "test", |
|
"lang": lang, |
|
"filepaths": dl_dir["bad_ids"], |
|
}, |
|
), |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
elif self.config.name == "schema_guided_dialog": |
|
challenge_sets = [ |
|
("challenge_train_sample", "train_schema_guided_dialog_RandomSample500_reformatted.json"), |
|
("challenge_validation_sample", "validation_schema_guided_dialog_RandomSample500_reformatted.json"), |
|
("challenge_test_backtranslation", "test_schema_guided_dialog_BackTranslation500_reformatted.json"), |
|
( |
|
"challenge_test_bfp02", |
|
"test_schema_guided_dialog_ButterFingersPerturbation_p=0.02_500_reformatted.json", |
|
), |
|
( |
|
"challenge_test_bfp05", |
|
"test_schema_guided_dialog_ButterFingersPerturbation_p=0.05_500_reformatted.json", |
|
), |
|
("challenge_test_nopunc", "test_schema_guided_dialog_WithoutPunctuation500_reformatted.json"), |
|
("challenge_test_scramble", "test_schema_guided_dialog_ScrambleInputStructure500_reformatted.json"), |
|
] |
|
return [ |
|
datasets.SplitGenerator( |
|
name=spl, gen_kwargs={"filepath": os.path.join(dl_dir["data"], "gem_sgd.json"), "split": spl} |
|
) |
|
for spl in ["train", "validation", "test"] |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
elif self.config.name == "totto": |
|
challenge_sets = [ |
|
("challenge_train_sample", "train_totto_RandomSample500.json"), |
|
("challenge_validation_sample", "validation_totto_RandomSample500.json"), |
|
("challenge_test_scramble", "test_totto_ScrambleInputStructure500.json"), |
|
] |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["data"], "totto_data/totto_train_data.jsonl"), |
|
"split": "train", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["data"], "totto_data/totto_dev_data.jsonl"), |
|
"split": "validation", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["data"], "totto_data/unlabeled_totto_test_data.jsonl"), |
|
"split": "test", |
|
}, |
|
), |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
elif self.config.name.startswith("web_nlg"): |
|
ln = self.config.name.split("_")[-1] |
|
challenge_sets = [ |
|
("challenge_train_sample", f"train_web_nlg_{ln}_RandomSample500.json"), |
|
("challenge_validation_sample", f"validation_web_nlg_{ln}_RandomSample500.json"), |
|
("challenge_test_scramble", f"test_web_nlg_{ln}_ScrambleInputStructure500.json"), |
|
] |
|
if ln == "en": |
|
challenge_sets += [("challenge_test_numbers", f"test_web_nlg_{ln}_replace_numbers_500.json")] |
|
return [ |
|
datasets.SplitGenerator(name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}) |
|
for spl in ["train", "validation", "test"] |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
elif self.config.name == "wiki_auto_asset_turk": |
|
challenge_sets = [ |
|
("challenge_train_sample", "train_wiki_auto_asset_turk_RandomSample500.json"), |
|
("challenge_validation_sample", "validation_wiki_auto_asset_turk_RandomSample500.json"), |
|
("challenge_test_asset_backtranslation", "test_asset_wiki_auto_asset_turk_BackTranslation.json"), |
|
( |
|
"challenge_test_asset_bfp02", |
|
"test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json", |
|
), |
|
( |
|
"challenge_test_asset_bfp05", |
|
"test_asset_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json", |
|
), |
|
("challenge_test_asset_nopunc", "test_asset_wiki_auto_asset_turk_WithoutPunctuation.json"), |
|
("challenge_test_turk_backtranslation", "detok_test_turk_wiki_auto_asset_turk_BackTranslation.json"), |
|
( |
|
"challenge_test_turk_bfp02", |
|
"detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.02.json", |
|
), |
|
( |
|
"challenge_test_turk_bfp05", |
|
"detok_test_turk_wiki_auto_asset_turk_ButterFingersPerturbation_p=0.05.json", |
|
), |
|
("challenge_test_turk_nopunc", "detok_test_turk_wiki_auto_asset_turk_WithoutPunctuation.json"), |
|
] |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": dl_dir["train"], |
|
"split": "train", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": dl_dir["validation"], |
|
"split": "validation", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name="test_asset", |
|
gen_kwargs={ |
|
"filepath": "", |
|
"split": "test_asset", |
|
"filepaths": [dl_dir["test_asset_orig"]] + [dl_dir[f"test_asset_{i}"] for i in range(10)], |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name="test_turk", |
|
gen_kwargs={ |
|
"filepath": dl_dir["test_turk"], |
|
"split": "test_turk", |
|
}, |
|
), |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], "wiki_auto_asset_turk", filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
elif self.config.name.startswith("wiki_lingua"): |
|
if "v0" in self.config.name: |
|
lang = self.config.name.split("_")[-3] |
|
base_dir = os.path.join(dl_dir["data"], "GEM_data_crosslingual", f"{lang}_en") |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": base_dir, |
|
"split": "train", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": base_dir, |
|
"split": "val", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": base_dir, |
|
"split": "test", |
|
}, |
|
), |
|
] |
|
else: |
|
lang_name = self.config.name.split("_")[-2] |
|
lang = self.config.name.split("_")[-1] |
|
base_dir = os.path.join(dl_dir["data"], lang_name) |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": base_dir, |
|
"split": "train", |
|
"lang": lang, |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": base_dir, |
|
"split": "val", |
|
"lang": lang, |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": base_dir, |
|
"split": "test", |
|
"lang": lang, |
|
}, |
|
), |
|
] |
|
elif self.config.name == "xsum": |
|
challenge_sets = [ |
|
("challenge_train_sample", "train_xsum_RandomSample500.json"), |
|
("challenge_validation_sample", "validation_xsum_RandomSample500.json"), |
|
("challenge_test_backtranslation", "test_xsum_BackTranslation500.json"), |
|
("challenge_test_bfp_02", "test_xsum_ButterFingersPerturbation_p=0.02_500.json"), |
|
("challenge_test_bfp_05", "test_xsum_ButterFingersPerturbation_p=0.05_500.json"), |
|
("challenge_test_nopunc", "test_xsum_WithoutPunctuation500.json"), |
|
("challenge_test_covid", "en_test_covid19.jsonl"), |
|
] |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": dl_dir["splits"], |
|
"split": "train", |
|
"filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"), |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": dl_dir["splits"], |
|
"split": "validation", |
|
"filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"), |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": dl_dir["splits"], |
|
"split": "test", |
|
"filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"), |
|
}, |
|
), |
|
] + [ |
|
datasets.SplitGenerator( |
|
name=challenge_split, |
|
gen_kwargs={ |
|
"filepath": os.path.join(dl_dir["challenge_set"], "xsum", filename), |
|
"split": challenge_split, |
|
}, |
|
) |
|
for challenge_split, filename in challenge_sets |
|
] |
|
|
|
def _generate_examples(self, filepath, split, filepaths=None, lang=None): |
|
"""Yields examples.""" |
|
if self.config.name == "common_gen": |
|
if split.startswith("challenge"): |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
if len(exple) == 0: |
|
continue |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
yield id_, exple |
|
else: |
|
with open(filepath, encoding="utf-8") as f: |
|
id_ = -1 |
|
i = -1 |
|
for row in f: |
|
row = row.replace(", }", "}") |
|
data = json.loads(row) |
|
concepts = [word for word in data["concept_set"].split("#")] |
|
if split == "train": |
|
i += 1 |
|
for scene in data["scene"]: |
|
id_ += 1 |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"concept_set_id": i, |
|
"concepts": concepts, |
|
"target": scene, |
|
"references": [], |
|
} |
|
else: |
|
id_ += 1 |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"concept_set_id": id_, |
|
"concepts": concepts, |
|
"target": "" if split == "test" else data["scene"][0], |
|
"references": [] if split == "test" else data["scene"], |
|
} |
|
elif self.config.name == "cs_restaurants": |
|
if split.startswith("challenge"): |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
if len(exple) == 0: |
|
continue |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
yield id_, exple |
|
else: |
|
with open(filepath, encoding="utf8") as f: |
|
data = json.load(f) |
|
for id_, instance in enumerate(data): |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"dialog_act": instance["da"], |
|
"dialog_act_delexicalized": instance["delex_da"], |
|
"target": instance["text"], |
|
"target_delexicalized": instance["delex_text"], |
|
"references": [] if split == "train" else [instance["text"]], |
|
} |
|
elif self.config.name == "dart": |
|
with open(filepath, encoding="utf-8") as f: |
|
data = json.loads(f.read()) |
|
id_ = -1 |
|
i = -1 |
|
for example in data: |
|
if split == "train": |
|
i += 1 |
|
for annotation in example["annotations"]: |
|
id_ += 1 |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"dart_id": i, |
|
"tripleset": example["tripleset"], |
|
"subtree_was_extended": example.get("subtree_was_extended", None), |
|
"target_sources": [annotation["source"] for annotation in example["annotations"]], |
|
"target": annotation["text"], |
|
"references": [], |
|
} |
|
else: |
|
id_ += 1 |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"dart_id": id_, |
|
"tripleset": example["tripleset"], |
|
"subtree_was_extended": example.get("subtree_was_extended", None), |
|
"target_sources": [annotation["source"] for annotation in example["annotations"]], |
|
"target": example["annotations"][0]["text"] if len(example["annotations"]) > 0 else "", |
|
"references": [annotation["text"] for annotation in example["annotations"]], |
|
} |
|
elif self.config.name == "e2e_nlg": |
|
if split.startswith("challenge"): |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
if len(exple) == 0: |
|
continue |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
yield id_, exple |
|
else: |
|
with open(filepath, encoding="utf-8") as f: |
|
reader = csv.DictReader(f) |
|
for id_, example in enumerate(reader): |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"meaning_representation": example["mr"], |
|
"target": example["ref"], |
|
"references": [] if split == "train" else [example["ref"]], |
|
} |
|
elif self.config.name.startswith("mlsum"): |
|
if split in ["train", "validation", "test", "challenge_test_covid"]: |
|
if split == "challenge_test_covid": |
|
bad_ids = {} |
|
else: |
|
bad_ids_dct = json.load(open(filepaths, encoding="utf-8")) |
|
bad_ids = dict((bad_url, True) for _, bad_url in bad_ids_dct[f"{lang}-{split}"]) |
|
with open(filepath, encoding="utf-8") as f: |
|
id_ = -1 |
|
for line in f: |
|
data = json.loads(line) |
|
if data["url"] in bad_ids: |
|
continue |
|
else: |
|
id_ += 1 |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"text": data["text"], |
|
"target": data["summary"], |
|
"references": [] if split == "train" else [data["summary"]], |
|
"topic": data["topic"], |
|
"url": data["url"], |
|
"title": data["title"], |
|
"date": data["date"], |
|
} |
|
else: |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
if len(exple) == 0: |
|
continue |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
yield id_, exple |
|
elif self.config.name == "schema_guided_dialog": |
|
if "challenge" in split: |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
if len(exple) == 0: |
|
continue |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
yield id_, exple |
|
else: |
|
examples = json.load(open(filepath, encoding="utf-8"))[split] |
|
for id_, example in enumerate(examples): |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"dialog_acts": [ |
|
{ |
|
"act": act_id, |
|
"slot": slot, |
|
"values": values, |
|
} |
|
for act_id, slot, values in example["da"] |
|
], |
|
"context": example["context"], |
|
"dialog_id": example["dialog_id"], |
|
"service": example["service"], |
|
"turn_id": example["turn_ix"], |
|
"prompt": example["prompt"], |
|
"target": example["target"], |
|
"references": [] if split == "train" else [example["target"]], |
|
} |
|
elif self.config.name == "totto": |
|
if "challenge" in split: |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
if len(exple) == 0: |
|
continue |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
yield id_, exple |
|
else: |
|
with open(filepath, "r", encoding="utf-8") as json_file: |
|
json_list = list(json_file) |
|
id_ = -1 |
|
i = -1 |
|
for json_str in json_list: |
|
result = json.loads(json_str) |
|
if split == "train": |
|
i += 1 |
|
for sentence in result["sentence_annotations"]: |
|
id_ += 1 |
|
response = { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"totto_id": i, |
|
"table_page_title": result["table_page_title"], |
|
"table_webpage_url": result["table_webpage_url"], |
|
"table_section_title": result["table_section_title"], |
|
"table_section_text": result["table_section_text"], |
|
"table": result["table"], |
|
"highlighted_cells": result["highlighted_cells"], |
|
"example_id": str(result["example_id"]), |
|
"overlap_subset": "none", |
|
"sentence_annotations": [sentence], |
|
"references": [], |
|
"target": sentence["final_sentence"], |
|
} |
|
yield id_, response |
|
else: |
|
id_ += 1 |
|
response = { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"totto_id": id_, |
|
"table_page_title": result["table_page_title"], |
|
"table_webpage_url": result["table_webpage_url"], |
|
"table_section_title": result["table_section_title"], |
|
"table_section_text": result["table_section_text"], |
|
"table": result["table"], |
|
"highlighted_cells": result["highlighted_cells"], |
|
"example_id": str(result["example_id"]), |
|
"overlap_subset": str(result["overlap_subset"]), |
|
"sentence_annotations": [] if split == "test" else result["sentence_annotations"], |
|
} |
|
response["references"] = [ |
|
sentence["final_sentence"] for sentence in response["sentence_annotations"] |
|
] |
|
response["target"] = response["references"][0] if len(response["references"]) > 0 else "" |
|
yield id_, response |
|
elif self.config.name.startswith("web_nlg"): |
|
if "challenge" in split: |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
if len(exple) == 0: |
|
continue |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
yield id_, exple |
|
else: |
|
with open(filepath, encoding="utf-8") as f: |
|
examples = json.load(f) |
|
id_ = -1 |
|
for example in examples["values"]: |
|
if split == "train": |
|
for target in example["target"]: |
|
id_ += 1 |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"input": example["input"], |
|
"target": target, |
|
"references": [] if split == "train" else example["target"], |
|
"category": example["category"], |
|
"webnlg_id": example["webnlg-id"], |
|
} |
|
else: |
|
id_ += 1 |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"input": example["input"], |
|
"target": example["target"][0] if len(example["target"]) > 0 else "", |
|
"references": example["target"], |
|
"category": example["category"], |
|
"webnlg_id": example["webnlg-id"], |
|
} |
|
elif self.config.name == "wiki_auto_asset_turk": |
|
if split in ["train", "validation"]: |
|
keys = [ |
|
"source", |
|
"target", |
|
] |
|
with open(filepath, encoding="utf-8") as f: |
|
for id_, line in enumerate(f): |
|
values = line.strip().split("\t") |
|
assert len(values) == 2, f"Not enough fields in ---- {line} --- {values}" |
|
example = dict([(k, val) for k, val in zip(keys, values)]) |
|
example["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
example["gem_parent_id"] = example["gem_id"] |
|
example["references"] = [] if split == "train" else [example["target"]] |
|
yield id_, example |
|
elif split == "test_turk": |
|
examples = json.load(open(filepath, encoding="utf-8")) |
|
for id_, example in enumerate(examples): |
|
example["gem_parent_id"] = example["gem_id"] |
|
for k in ["source_id", "target_id"]: |
|
if k in example: |
|
del example[k] |
|
yield id_, example |
|
elif split == "test_asset": |
|
files = [open(f_name, encoding="utf-8") for f_name in filepaths] |
|
for id_, lines in enumerate(zip(*files)): |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"target": lines[1].strip(), |
|
"source": lines[0].strip(), |
|
"references": [line.strip() for line in lines[1:]], |
|
} |
|
else: |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
for k in ["source_id", "target_id"]: |
|
if k in exple: |
|
del exple[k] |
|
yield id_, exple |
|
elif self.config.name.startswith("wiki_lingua"): |
|
if "v0" in self.config.name: |
|
with open(os.path.join(filepath, f"{split}.src"), encoding="utf-8") as f_in: |
|
with open(os.path.join(filepath, f"{split}.tgt"), encoding="utf-8") as f_out: |
|
for id_, (src, tgt) in enumerate(zip(f_in, f_out)): |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"source": src.strip(), |
|
"target": tgt.strip(), |
|
"references": [] if split == "train" else [tgt.strip()], |
|
} |
|
else: |
|
with open(os.path.join(filepath, f"{split}.src.{lang}"), encoding="utf-8") as f_in_ln: |
|
with open(os.path.join(filepath, f"{split}.src.en"), encoding="utf-8") as f_in_en: |
|
with open(os.path.join(filepath, f"{split}.tgt.{lang}"), encoding="utf-8") as f_out_ln: |
|
with open(os.path.join(filepath, f"{split}.tgt.en"), encoding="utf-8") as f_out_en: |
|
for id_, (src_ln, src_en, tgt_ln, tgt_en) in enumerate( |
|
zip(f_in_ln, f_in_en, f_out_ln, f_out_en) |
|
): |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"source_aligned": {lang: src_ln.strip(), "en": src_en.strip()}, |
|
"target_aligned": {lang: tgt_ln.strip(), "en": tgt_en.strip()}, |
|
"source": src_ln.strip(), |
|
"target": tgt_en.strip(), |
|
"references": [] if split == "train" else [tgt_en.strip()], |
|
} |
|
elif self.config.name == "xsum": |
|
if "challenge" in split: |
|
if "covid" in split: |
|
with open(filepath, encoding="utf-8") as f: |
|
id_ = -1 |
|
for line in f: |
|
data = json.loads(line) |
|
id_ += 1 |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"xsum_id": data["url"], |
|
"document": data["text"], |
|
"target": data["summary"], |
|
"references": [] if split == "train" else [data["summary"]], |
|
} |
|
else: |
|
exples = json.load(open(filepath, encoding="utf-8")) |
|
if isinstance(exples, dict): |
|
assert len(exples) == 1, "multiple entries found" |
|
exples = list(exples.values())[0] |
|
for id_, exple in enumerate(exples): |
|
exple["gem_parent_id"] = exple["gem_id"] |
|
exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
|
yield id_, exple |
|
else: |
|
with open(filepath, "r", encoding="utf-8") as f: |
|
split_ids = json.load(f) |
|
for id_, i in enumerate(split_ids[split]): |
|
with open(os.path.join(filepaths, i + ".summary"), "r", encoding="utf-8") as f: |
|
text = "".join( |
|
[line for line in f.readlines() if line not in _XSUM_REMOVE_LINES and line.strip()] |
|
) |
|
segs = text.split("[SN]") |
|
yield id_, { |
|
"gem_id": f"{self.config.name}-{split}-{id_}", |
|
"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
|
"xsum_id": i, |
|
"document": segs[8].strip(), |
|
"target": segs[6].strip(), |
|
"references": [] if split == "train" else [segs[6].strip()], |
|
} |
|
|
