VEC / VEC.py
LiLei
update
b282208
# 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'],
}