Delete IRFL.py with huggingface_hub

IRFL.py

@@ -1,141 +0,0 @@
-# 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.
-""" IRFL Loading Script """
-
-import json
-import os
-import pandas as pd
-import datasets
-from huggingface_hub import hf_hub_url
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """
-"""
-
-_DESCRIPTION = """\
-IRFL is a dataset of multimodal idioms, metaphors, and similes and a benchmark to evaluate vision and language models' understanding of figurative language.
-"""
-
-_HOMEPAGE = "https://irfl-dataset.github.io/"
-
-_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
-
-_URL = "https://huggingface.co/datasets/lampent/IRFL"
-
-class Vasr(datasets.GeneratorBasedBuilder):
-    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
-    idiom_keys = ['query', 'distractors', 'answer', 'figurative_type', 'images_metadata', 'type', 'definition', 'phrase']
-    metaphor_keys = ['distractors', 'answer', 'figurative_type', 'images_metadata', 'type', 'phrase']
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="idioms", version=VERSION, description="IRFL dataset"),
-        datasets.BuilderConfig(name="metaphors", version=VERSION, description="IRFL dataset"),
-    ]
-
-
-    def _info(self):
-        if self.config.name == 'idioms':
-            features = datasets.Features(
-                {
-                    "query": datasets.Value("string"),
-                    "distractors": [datasets.Image()],
-                    "answer": datasets.Image(),
-                    "figurative_type": datasets.Value("string"),
-                    "images_metadata": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "definition": datasets.Value("string"),
-                    "phrase": datasets.Value("string")
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "distractors": [datasets.Image()],
-                    "answer": datasets.Image(),
-                    "figurative_type": datasets.Value("string"),
-                    "images_metadata": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "phrase": datasets.Value("string")
-                }
-            )
-        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):
-        # 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
-        data_dir = dl_manager.download_and_extract({
-            "images_dir": hf_hub_url(repo_id="lampent/IRFL", repo_type='dataset', filename="IRFL_images.zip")
-        })
-
-        if self.config.name == "idioms":
-            test_examples = hf_hub_url(repo_id="lampent/IRFL", repo_type='dataset', filename="idiom_detection_task.csv")
-        else:
-            test_examples = hf_hub_url(repo_id="lampent/IRFL", repo_type='dataset', filename="metaphor_detection_task.csv")
-        # dev_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="dev_gold.csv")
-        # train_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="train_gold.csv")
-
-        # train_gen = datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={**data_dir, **{'examples_csv': train_examples}})
-        # dev_gen = datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={**data_dir, **{'examples_csv': dev_examples}})
-        test_gen = datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={**data_dir, **{'examples_csv': test_examples}})
-
-        return [test_gen]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, examples_csv, images_dir):
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-
-        df = pd.read_csv(examples_csv)
-
-        for r_idx, r in df.iterrows():
-            r_dict = r.to_dict()
-            r_dict['distractors'] = json.loads(r_dict['distractors'])
-            r_dict['distractors'] = [os.path.join(images_dir, "IRFL_images", x) for x in r_dict['distractors']]
-            r_dict['answer'] = os.path.join(images_dir, "IRFL_images", r_dict['answer'])
-
-            # r_dict["A_str"] = r_dict['A_img']
-            # r_dict["A'_str"] = r_dict['B_img']
-            # r_dict["B_str"] = r_dict['C_img']
-            # r_dict["B'_str"] = r_dict['D_img']
-            # for img in ['A_img', 'B_img', 'C_img', 'D_img']:
-            #     if r_dict[img] == self.HIDDEN_LABEL:
-            #         r_dict[img] = os.path.join(images_dir, "vasr_images", self.QMARK_IMG)
-            #     else:
-            #         r_dict[img] = os.path.join(images_dir, "vasr_images", r_dict[img])
-            # r_dict["A"] = r_dict['A_img']
-            # r_dict["A'"] = r_dict['B_img']
-            # r_dict["B"] = r_dict['C_img']
-            # r_dict["B'"] = r_dict['D_img']
-            # relevant_r_dict = {k:v for k,v in r_dict.items() if k in self.DATASET_KEYS or k == 'candidates_images'}
-            yield r_idx, r_dict