# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# 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. | |
# TODO: Address all TODOs and remove all explanatory comments | |
"""TODO: Add a description here.""" | |
import csv | |
import json | |
import os | |
import datasets | |
# TODO: Add BibTeX citation | |
# Find for instance the citation on arxiv or on the dataset repo/website | |
_CITATION = """\ | |
@misc{ | |
li2023what, | |
title={What Does Vision Supervision Bring to Language Models? A Case Study of {CLIP}}, | |
author={Lei Li and Jingjing Xu and Qingxiu Dong and Ce Zheng and Qi Liu and Lingpeng Kong and Xu Sun}, | |
year={2023}, | |
url={https://openreview.net/forum?id=SdBfRJE9SX-} | |
} | |
""" | |
# TODO: Add description of the dataset here | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
Visual and Embodied Concept (VEC) benchmark is designed for evaluating the LLM understanding ability of basic visual (color, shape, size, height and material) and embodied (mass, temperature, hardness) concepts. | |
""" | |
# TODO: Add a link to an official homepage for the dataset here | |
_HOMEPAGE = "" | |
# TODO: Add the licence for the dataset here if you can find it | |
_LICENSE = "Apache 2.0" | |
# 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) | |
_URLS = { | |
"color": {"test": "./data/color.json"}, | |
"shape": {"test":"./data/shape.json"}, | |
"size": {"test":"./data/size.json"}, | |
"height": {"test": "./data/height.json"}, | |
"material": {"test":"./data/material.json"}, | |
"hardness": {"test":"./data/hardness.json"}, | |
"temperature": {"test":"./data/temperature.json"}, | |
"mass": {"test":"./data/mass.json"}, | |
} | |
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case | |
class VECDataset(datasets.GeneratorBasedBuilder): | |
"""VEC dataset for evaluating visual and embodied concepts""" | |
VERSION = datasets.Version("1.1.0") | |
# This is an example of a dataset with multiple configurations. | |
# If you don't want/need to define several sub-sets in your dataset, | |
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
# 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="mass", version=VERSION, description="mass dataset"), | |
datasets.BuilderConfig(name="temperature", version=VERSION, description="temperature dataset"), | |
datasets.BuilderConfig(name="hardness", version=VERSION, description="hardness dataset"), | |
datasets.BuilderConfig(name="shape", version=VERSION, description="shape dataset"), | |
datasets.BuilderConfig(name="size", version=VERSION, description="size dataset"), | |
datasets.BuilderConfig(name="material", version=VERSION, description="material dataset"), | |
datasets.BuilderConfig(name="color", version=VERSION, description="color dataset"), | |
datasets.BuilderConfig(name="height", version=VERSION, description="height dataset"), | |
] | |
DEFAULT_CONFIG_NAME = "hardness" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
def _info(self): | |
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
if self.config.name in ["color", "shape", "material"]: # This is the name of the configuration selected in BUILDER_CONFIGS above | |
features = datasets.Features( | |
{ | |
"obj": datasets.Value("string"), | |
"positive": datasets.Value("string"), | |
"negative": datasets.Value("string"), | |
"relation": datasets.Value("string"), | |
# These are the features of your dataset like images, labels ... | |
} | |
) | |
else: # for pair comparison | |
features = datasets.Features( | |
{ | |
"obj1": datasets.Value("string"), | |
"obj2": datasets.Value("string"), | |
"relation": datasets.Value("string"), | |
"label": datasets.ClassLabel(num_classes=2, names=["<", ">"]) | |
# 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 = dl_manager.download_and_extract(urls) | |
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. | |
with open(filepath, encoding="utf-8") as f: | |
for key, row in enumerate(f): | |
data = json.loads(row) | |
if self.config.name in ['color', 'shape', 'material']: | |
# Yields examples as (key, example) tuples | |
# {"sub": "jacket", "obj": "black", "alt": "purple"} | |
yield f"{self.config.name}-{key}", { | |
"obj": data['sub'].strip(), | |
"positive": data['obj'].strip(), | |
"negative": data['alt'].strip(), | |
"relation": self.config.name # change to prompt template later | |
} | |
elif self.config.name in ["shape", "height"]: # shape | |
#{"text": "An ant and a bird.", "question": "Is an ant taller than a bird?", "obj_a": "ant", "obj_b": "bird", "label": 0, "obj1": "ant", "obj2": "bird"} | |
yield f"{self.config.name}-{key}", { | |
"obj1": data['obj1'].strip(), | |
"obj2": data['obj2'].strip(), | |
"relation": self.config.name, # change to prompt template later | |
"label": data['label'] | |
} | |
else: # hardness, mass, temperature | |
yield f"{self.config.name}-{key}", { | |
"obj1": data['obj1'].strip(), | |
"obj2": data['obj2'].strip(), | |
"relation": self.config.name, | |
"label": data['label'], | |
} |