rel-text.py

import os
import datasets
import pandas as pd

class relTextConfig(datasets.BuilderConfig):
    def __init__(self, features, data_url, **kwargs):
        super(relTextConfig, self).__init__(**kwargs)
        self.features = features
        self.data_url = data_url


class relText(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        relTextConfig(
            name="pairs",
            features={
                "ltable_id": datasets.Value("string"),
                "rtable_id": datasets.Value("string"),
                "label": datasets.Value("string"),
            },
            data_url="https://huggingface.co/datasets/matchbench/rel-text/resolve/main/",
        ),

         relTextConfig(
            name="source",
            features={
                "content": datasets.Value("string"),
            },
            data_url="https://huggingface.co/datasets/matchbench/rel-text/resolve/main/left.txt",
        ),
        
        relTextConfig(
            name="target",
            features={
                "id": datasets.Value("string"),
                "title": datasets.Value("string"),
                "authors": datasets.Value("string"),
                "venue": datasets.Value("string"),
                "year": datasets.Value("string"),
            },
            data_url="https://huggingface.co/datasets/matchbench/rel-text/resolve/main/right.csv",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(self.config.features)
        )

    def _split_generators(self, dl_manager):
        if self.config.name == "pairs":
            return [
                datasets.SplitGenerator(
                    name=split,
                    gen_kwargs={
                        "path_file": dl_manager.download_and_extract(
                            os.path.join(self.config.data_url, f"{split}.csv")),
                        "split": split,
                    }
                )
                for split in ["train", "valid", "test"]
            ]
            
        if self.config.name == "source":
           return [datasets.SplitGenerator(name="source", gen_kwargs={
                "path_file": dl_manager.download_and_extract(self.config.data_url), "split": "source", })]
                 
        if self.config.name == "target":
            return [datasets.SplitGenerator(name="target", gen_kwargs={
                "path_file": dl_manager.download_and_extract(self.config.data_url), "split": "target", })]

    def _generate_examples(self, path_file, split):
        if split=="source":      #read in txt file.
            with open(path_file, "r") as f:
                file = f.readlines()
            for i in range(len(file)):
                yield i, {
                    "content": file[i].strip('\n')
                }
        else:
            file = pd.read_csv(path_file)
            for i, row in file.iterrows():
                if split not in ['source','target']:
                    yield i, {
                        "ltable_id": row["ltable_id"],
                        "rtable_id": row["rtable_id"],
                        "label": row["label"],
                    }
                elif split in ['target']:
                    yield i, {
                        "id": row["id"],
                        "title": row["title"],
                        "authors": row["authors"],
                        "venue": row["venue"],
                        "year": row["year"],
                    }