Update parquet files

.gitattributes

@@ -1,27 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bin.* filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,217 +0,0 @@
----
-annotations_creators:
-- crowdsourced
-language:
-- en
-language_creators:
-- found
-- crowdsourced
-license:
-- mit
-multilinguality:
-- monolingual
-pretty_name: CommonGen
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text2text-generation
-task_ids: []
-paperswithcode_id: commongen
-tags:
-- concepts-to-text
-dataset_info:
-  features:
-  - name: concept_set_idx
-    dtype: int32
-  - name: concepts
-    sequence: string
-  - name: target
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 6724250
-    num_examples: 67389
-  - name: validation
-    num_bytes: 408752
-    num_examples: 4018
-  - name: test
-    num_bytes: 77530
-    num_examples: 1497
-  download_size: 1845699
-  dataset_size: 7210532
----
-
-# Dataset Card for "common_gen"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://inklab.usc.edu/CommonGen/index.html](https://inklab.usc.edu/CommonGen/index.html)
-- **Repository:** https://github.com/INK-USC/CommonGen
-- **Paper:** [CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning](https://arxiv.org/abs/1911.03705)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 1.76 MB
-- **Size of the generated dataset:** 6.88 MB
-- **Total amount of disk used:** 8.64 MB
-
-### Dataset Summary
-
-CommonGen is a constrained text generation task, associated with a benchmark dataset,
-to explicitly test machines for the ability of generative commonsense reasoning. Given
-a set of common concepts; the task is to generate a coherent sentence describing an
-everyday scenario using these concepts.
-
-CommonGen is challenging because it inherently requires 1) relational reasoning using
-background commonsense knowledge, and 2) compositional generalization ability to work
-on unseen concept combinations. Our dataset, constructed through a combination of
-crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and
-50k sentences in total.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### default
-
-- **Size of downloaded dataset files:** 1.76 MB
-- **Size of the generated dataset:** 6.88 MB
-- **Total amount of disk used:** 8.64 MB
-
-An example of 'train' looks as follows.
-```
-{
-    "concept_set_idx": 0,
-    "concepts": ["ski", "mountain", "skier"],
-    "target": "Three skiers are skiing on a snowy mountain."
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `concept_set_idx`: a `int32` feature.
-- `concepts`: a `list` of `string` features.
-- `target`: a `string` feature.
-
-### Data Splits
-
-| name  |train|validation|test|
-|-------|----:|---------:|---:|
-|default|67389|      4018|1497|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-The dataset is licensed under [MIT License](https://github.com/INK-USC/CommonGen/blob/master/LICENSE).
-
-### Citation Information
-
-```bib
-@inproceedings{lin-etal-2020-commongen,
-    title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
-    author = "Lin, Bill Yuchen  and
-      Zhou, Wangchunshu  and
-      Shen, Ming  and
-      Zhou, Pei  and
-      Bhagavatula, Chandra  and
-      Choi, Yejin  and
-      Ren, Xiang",
-    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165",
-    doi = "10.18653/v1/2020.findings-emnlp.165",
-    pages = "1823--1840"
-}
-```
-
-
-### Contributions
-
-Thanks to [@JetRunner](https://github.com/JetRunner), [@yuchenlin](https://github.com/yuchenlin), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

common_gen.py

@@ -1,109 +0,0 @@
-import json
-import os
-import random
-
-import datasets
-
-
-random.seed(42)  # This is important, to ensure the same order for concept sets as the official script.
-
-_CITATION = """\
-@inproceedings{lin-etal-2020-commongen,
-    title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
-    author = "Lin, Bill Yuchen  and
-      Zhou, Wangchunshu  and
-      Shen, Ming  and
-      Zhou, Pei  and
-      Bhagavatula, Chandra  and
-      Choi, Yejin  and
-      Ren, Xiang",
-    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165",
-    doi = "10.18653/v1/2020.findings-emnlp.165",
-    pages = "1823--1840"
-}
-"""
-
-_DESCRIPTION = """\
-CommonGen is a constrained text generation task, associated with a benchmark dataset,
-to explicitly test machines for the ability of generative commonsense reasoning. Given
-a set of common concepts; the task is to generate a coherent sentence describing an
-everyday scenario using these concepts.
-
-CommonGen is challenging because it inherently requires 1) relational reasoning using
-background commonsense knowledge, and 2) compositional generalization ability to work
-on unseen concept combinations. Our dataset, constructed through a combination of
-crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and
-50k sentences in total.
-"""
-_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/common_gen/commongen_data.zip"
-
-
-class CommonGen(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("2020.5.30")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "concept_set_idx": datasets.Value("int32"),
-                "concepts": datasets.Sequence(datasets.Value("string")),
-                "target": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=datasets.info.SupervisedKeysData(input="concepts", output="target"),
-            homepage="https://inklab.usc.edu/CommonGen/index.html",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        dl_dir = dl_manager.download_and_extract(_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": os.path.join(dl_dir, "commongen.train.jsonl"), "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": os.path.join(dl_dir, "commongen.dev.jsonl"), "split": "dev"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": os.path.join(dl_dir, "commongen.test_noref.jsonl"), "split": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            id_ = 0
-            for idx, row in enumerate(f):
-                row = row.replace(", }", "}")  # Fix possible JSON format error
-                data = json.loads(row)
-
-                rand_order = [word for word in data["concept_set"].split("#")]
-                random.shuffle(rand_order)
-
-                if split == "test":
-                    yield idx, {
-                        "concept_set_idx": idx,
-                        "concepts": rand_order,
-                        "target": "",
-                    }
-                else:
-                    for scene in data["scene"]:
-                        yield id_, {
-                            "concept_set_idx": idx,
-                            "concepts": rand_order,
-                            "target": scene,
-                        }
-                        id_ += 1

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "CommonGen is a constrained text generation task, associated with a benchmark dataset, \nto explicitly test machines for the ability of generative commonsense reasoning. Given \na set of common concepts; the task is to generate a coherent sentence describing an \neveryday scenario using these concepts.\n\nCommonGen is challenging because it inherently requires 1) relational reasoning using \nbackground commonsense knowledge, and 2) compositional generalization ability to work \non unseen concept combinations. Our dataset, constructed through a combination of \ncrowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and \n50k sentences in total.\n", "citation": "@inproceedings{lin-etal-2020-commongen,\n     author = {Bill Yuchen Lin and Wangchunshu Zhou and Ming Shen and Pei Zhou and Chandra Bhagavatula and Yejin Choi and Xiang Ren},\n     title = {{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning},\n     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},\n  year = {2020}\n}\n", "homepage": "https://inklab.usc.edu/CommonGen/index.html", "license": "", "features": {"concept_set_idx": {"dtype": "int32", "id": null, "_type": "Value"}, "concepts": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "target": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "concepts", "output": "target"}, "builder_name": "common_gen", "config_name": "default", "version": {"version_str": "2020.5.30", "description": null, "datasets_version_to_prepare": null, "major": 2020, "minor": 5, "patch": 30}, "splits": {"train": {"name": "train", "num_bytes": 6724250, "num_examples": 67389, "dataset_name": "common_gen"}, "validation": {"name": "validation", "num_bytes": 408752, "num_examples": 4018, "dataset_name": "common_gen"}, "test": {"name": "test", "num_bytes": 77530, "num_examples": 1497, "dataset_name": "common_gen"}}, "download_checksums": {"https://storage.googleapis.com/huggingface-nlp/datasets/common_gen/commongen_data.zip": {"num_bytes": 1845699, "checksum": "a3f19ca607da4e874fc5f2dd1f53c13a6788a497f883d74cc3f9a1fcda44c594"}}, "download_size": 1845699, "post_processing_size": null, "dataset_size": 7210532, "size_in_bytes": 9056231}}

default/common_gen-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2c7642eb9ea7d6ea78fb4d834d5e95ecca049aabf69e3f66e3d74da848f292e
+size 31156

default/common_gen-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b249952e1280a48057ef558684dbb4229b85967509715478ff20cf5bac904e8
+size 3232447

default/common_gen-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0560c096ddc90966c4a98587cbb2644bcfe21a648aba1f3f5f9b1e57ba5da5b1
+size 171259