update load script

VEC.py

@@ -0,0 +1,182 @@
+# 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 NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    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"),
+                    "regative": 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[f"{self.config.name}.json"],
+                    "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'],
+                        "positive": data['obj'],
+                        "negative": data['alt'],
+                        "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'],
+                        "obj2": data['obj2'],
+                        "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'],
+                        "obj2": data['obj2'],
+                        "relation": self.config.name,
+                        "label": data['label'],
+                    }