--- dataset_info: - config_name: corpus features: - name: corpus-id dtype: string - name: image dtype: image splits: - name: train num_bytes: 315479652.451 num_examples: 9593 download_size: 289013318 dataset_size: 315479652.451 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int32 splits: - name: train num_bytes: 353706 num_examples: 11307 download_size: 185552 dataset_size: 353706 - config_name: queries features: - name: query-id dtype: string - name: query dtype: string - name: answer dtype: string - name: options sequence: string - name: is_numerical dtype: int32 splits: - name: train num_bytes: 1485381 num_examples: 11307 download_size: 507845 dataset_size: 1485381 configs: - config_name: corpus data_files: - split: train path: corpus/train-* - config_name: qrels data_files: - split: train path: qrels/train-* - config_name: queries data_files: - split: train path: queries/train-* --- ## Dataset Description This is a VQA dataset based on Scientific Plots from PlotQA dataset from [PlotQA](https://arxiv.org/abs/1909.00997). ### Load the dataset ```python from datasets import load_dataset import csv def load_beir_qrels(qrels_file): qrels = {} with open(qrels_file) as f: tsvreader = csv.DictReader(f, delimiter="\t") for row in tsvreader: qid = row["query-id"] pid = row["corpus-id"] rel = int(row["score"]) if qid in qrels: qrels[qid][pid] = rel else: qrels[qid] = {pid: rel} return qrels corpus_ds = load_dataset("openbmb/VisRAG-Ret-Test-PlotQA", name="corpus", split="train") queries_ds = load_dataset("openbmb/VisRAG-Ret-Test-PlotQA", name="queries", split="train") qrels_path = "xxxx" # path to qrels file which can be found under qrels folder in the repo. qrels = load_beir_qrels(qrels_path) ```