Datasets:
Languages:
Portuguese
License:
| import csv | |
| import json | |
| import os | |
| import datasets | |
| _DESCRIPTION = """\ | |
| FIQA translated dataset to portuguese | |
| """ | |
| _URLS = { | |
| "corpus": "https://huggingface.co/datasets/leonardo-avila/fiqa_pt/blob/main/corpus_pt.tsv", | |
| "topics": "https://huggingface.co/datasets/leonardo-avila/fiqa_pt/blob/main/topics_pt.tsv", | |
| "qrel": "https://huggingface.co/qrel.tsv", | |
| } | |
| # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case | |
| class BeirPT(datasets.GeneratorBasedBuilder): | |
| """BEIR BenchmarkDataset.""" | |
| VERSION = datasets.Version("1.1.0") | |
| # 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="corpus", version=VERSION, description="Load corpus"), | |
| datasets.BuilderConfig(name="topics", version=VERSION, description="Load topics"), | |
| datasets.BuilderConfig(name="qrel", version=VERSION, description="Load qrel"), | |
| ] | |
| DEFAULT_CONFIG_NAME = "corpus" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| if self.config.name in ["corpus", "topics"]: | |
| features = datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| } | |
| ) | |
| else: # This is an example to show how to have different features for "first_domain" and "second_domain" | |
| features = datasets.Features( | |
| { | |
| "query_id": datasets.Value("string"), | |
| "doc_id": datasets.Value("string"), | |
| "rel": datasets.Value("string"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features | |
| ) | |
| 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=self.config.name, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"filepath": data_dir}, | |
| ), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| with open(filepath, encoding="utf-8") as f: | |
| if self.config.name in ["corpus", "topics"]: | |
| for line in f: | |
| fields = line.strip().split("\t") | |
| idx = fields[0] | |
| text = fields[1] | |
| yield idx, text | |
| else: | |
| for line in f: | |
| if "query-id" not in line: | |
| fields = line.strip().split("\t") | |
| query_id = fields[0] | |
| doc_id = fields[1] | |
| rel = int(fields[2]) | |
| yield query_id, doc_id, rel |