# 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. import json from ast import literal_eval import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @article{duan2024boosting, title={Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations}, author={Duan, Haonan and Skreta, Marta and Cotta, Leonardo and Rajaonson, Ella Miray and Dhawan, Nikita and Aspuru-Guzik, Alán and Maddison, Chris J}, journal={bioRxiv}, pages={2024--07}, year={2024}, publisher={Cold Spring Harbor Laboratory} } """ _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ # 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 = "" # 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 = { "first_domain": "https://huggingface.co/datasets/mskrt/PAIR/raw/main/test.json", } # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class PAIRDataset(datasets.GeneratorBasedBuilder): """PAIRDataset.""" def _info(self): """_info.""" return datasets.DatasetInfo( description="My custom dataset.", features=datasets.Features( { "annotation_type": datasets.Value("string"), "sequence": datasets.Value("string"), "pid": datasets.Value("string"), "annotation": datasets.Value("string"), } ), supervised_keys=None, ) def _split_generators(self, dl_manager): """_split_generators. Parameters ---------- dl_manager : dl_manager """ # Implement logic to download and extract data files # For simplicity, assume data_files is a dict with paths to your data data_files = { "train": "train.json", "test": "test.json", } return [ # datasets.SplitGenerator( # name=datasets.Split.TRAIN, # gen_kwargs={'filepath': data_files['train']}, # ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}, ), ] def _generate_examples(self, filepath): """_generate_examples. Parameters ---------- filepath : filepath """ # Implement your data reading logic here with open(filepath, encoding="utf-8") as f: data = json.load(f) counter = 0 for idx, annotation_type in enumerate(data.keys()): # Parse your line into the appropriate fields samples = data[annotation_type] for idx_2, elem in enumerate(samples): # example = parse_line_to_example(line) if elem["content"] != [None]: content = elem["content"][0] print(content, type(content)) print(literal_eval(content), "done") yield counter, { "annotation_type": annotation_type, "sequence": elem["seq"], "pid": elem["pid"], "annotation": literal_eval(elem["content"][0]), } counter += 1 # class PAIRDataset(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="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), # ] # # DEFAULT_CONFIG_NAME = "first_domain" # 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 == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above # features = datasets.Features( # { # "sentence": datasets.Value("string"), # "option1": datasets.Value("string"), # "answer": datasets.Value("string") # # 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.TRAIN, # # # These kwargs will be passed to _generate_examples # ### gen_kwargs={ # # "filepath": os.path.join(data_dir, "train.json"), # # "split": "train", # # }, # # ), # datasets.SplitGenerator( # name=datasets.Split.TEST, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": "test.json", #os.path.join(data_dir, "test.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 data['content'] != [None]: # yield key, { # "sequence": data["seq"], # "pid": data["pid"], # #"content": "" if split == "test" else data["answer"], # "content": data['content'][0], # }