Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.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

common_gen.py

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+from __future__ import absolute_import, division, print_function
+
+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 = """\
+@article{lin2019comgen,
+     author = {Bill Yuchen Lin and Ming Shen and Wangchunshu Zhou and Pei Zhou and Chandra Bhagavatula and Yejin Choi and Xiang Ren},
+     title = {CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning},
+     journal = {CoRR},
+     volume = {abs/1911.03705},
+     year = {2019}
+}
+"""
+
+_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

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+{"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": "@article{lin2019comgen,\n     author = {Bill Yuchen Lin and Ming Shen and Wangchunshu Zhou and Pei Zhou and Chandra Bhagavatula and Yejin Choi and Xiang Ren},\n     title = {CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning},\n     journal = {CoRR},\n     volume = {abs/1911.03705},\n     year = {2019}\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}}

dummy/2020.5.30/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:884c5253c1efbb9befc969a34ca74d0480de4233d709871d934ed3c6d296c5ea
+size 2495