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C2Gen.py

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-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""The SuperGLUE benchmark."""
-
-import json
-import os
-import datasets
-import pandas as pd
-
-_CITATION = """TODO
-"""
-
-# You can copy an official description
-_DESCRIPTION = """The task of C2Gen is to both generate commonsensical text which include the given words, and also have the generated text adhere to the given context.
-"""
-
-_HOMEPAGE = ""
-
-_LICENSE = "cc-by-sa-4.0"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URL = "https://huggingface.co/datasets/Severine/C2Gen/resolve/main/data/"
-_TASKS = {
-    "c2gen": "C2Gen",
-}
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class C2Gen(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="c2gen", version=VERSION, description=_DESCRIPTION),
-    ]
-
-    DEFAULT_CONFIG_NAME = "c2gen"
-    
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        # This is the name of the configuration selected in BUILDER_CONFIGS above
-        features = datasets.Features(
-            {
-                "context": datasets.Value("string"),
-                "keywords": datasets.Sequence(feature=datasets.Value(dtype="string",id=None), length=-1,id=None),
-                # These are the features of your dataset like images, labels ...
-            }
-        )
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-  
-    def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        #urls = _URLS[self.config.name]
-        data_dir_test = dl_manager.download_and_extract(os.path.join(_URL, "test.json"))
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir_test,
-                    "split": "test"
-                },
-            ),
-        ]
-   
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        data = json.load(open(filepath,"r"))
-        for key, row in enumerate(data):
-
-            # Yields examples as (key, example) tuples
-            yield key, {
-                "context": row["Context"],
-                "keywords": row["Words"],
-            }
-

README.md

@@ -1,70 +0,0 @@
----
-language:
-- en
-license:
-- cc-by-sa-4.0
-size_categories:
-- <100K
-task_categories:
-- text-generation
----
-# Dataset Card for Contextualized CommonGen(C2Gen)
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-instances)
-  - [Data Splits](#data-instances)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Initial Data Collection and Normalization](#initial-cata-collection-and-normalization)
-- [Licensing Information](#licensing-information)
- 
-
-## Dataset Description
-
-- **Repository:** [Non-Residual Prompting](https://github.com/FreddeFrallan/Non-Residual-Prompting)
-- **Paper:** [Fine-Grained Controllable Text Generation Using Non-Residual Prompting](https://aclanthology.org/2022.acl-long.471)
-- **Point of Contact:** [Fredrik Carlsson](mailto:Fredrik.Carlsson@ri.se)
-
-
-### Dataset Summary
-
-CommonGen [Lin et al., 2020](https://arxiv.org/abs/1911.03705) is a dataset for the constrained text generation task of word inclusion. But the task does not allow to include context. Therefore, to complement CommonGen, we provide an extended test set C2Gen [Carlsson et al., 2022](https://aclanthology.org/2022.acl-long.471) where an additional context is provided for each set of target words. The task is therefore reformulated to both generate commonsensical text which include the given words, and also have the generated text adhere to the given context.
-
-### Languages
-
-English
-
-## Dataset Structure
-
-### Data Instances
-
-{"Context": "The show came on the television with people singing. The family all gathered to watch. They all became silent when the show came on.", "Words": ["follow", "series", "voice"]}
-
-### Data Fields
-
-- context: the generated text by the model should adhere to this text
-- words: the words that should be included in the generated continuation
-
-### Data Splits
-
-Test
-
-## Dataset Creation
-
-### Curation Rationale
-
-C2Gen was created because the authors of the paper believed that the task formulation of CommonGen is too narrow, and that it needlessly incentivizes researchers
-to focus on methods that do not support context. Which is orthogonal to their belief that many application areas necessitates the consideration of surrounding context. Therefore, to complement CommonGen, they provide an extended test set where an additional context is provided for each set of target words.
-
-### Initial Data Collection and Normalization
-
-The dataset was constructed with the help the crowd sourcing platform MechanicalTurk. Each remaining concept set manually received a textual context. To assure the quality of the data generation, only native English speakers with a recorded high acceptance were allowed to participate. Finally, all contexts were manually verified, and fixed in terms of typos and poor quality. Furthermore we want to raise awareness that C2GEN can contain personal data or offensive content. If you would encounter such a sample, please reach out to us.
-
-## Licensing Information
-
-license: cc-by-sa-4.0

c2gen/c2_gen-test.parquet

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dataset_infos.json

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