| language: | |
| - en | |
| dataset_info: | |
| features: | |
| - name: passages | |
| struct: | |
| - name: is_selected | |
| sequence: int32 | |
| - name: passage_text | |
| sequence: string | |
| - name: url | |
| sequence: string | |
| - name: query | |
| dtype: string | |
| - name: query_id | |
| dtype: int32 | |
| - name: query_type | |
| dtype: string | |
| - name: golden_passages | |
| sequence: string | |
| - name: answer | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 326842258 | |
| num_examples: 70616 | |
| download_size: 168328467 | |
| dataset_size: 326842258 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| This dataset was created by filtering and adding columns needed to evaluate retrievers to "v1.1" version of [MSMARCO dataset] (https://github.com/microsoft/MSMARCO-Question-Answering). I am additionally providing the code used to filter the dataset in order to make everything clear. | |
| ``` | |
| msmarco = load_dataset("ms_marco", "v1.1", split="train").to_pandas() | |
| msmarco["golden_passages"] = [row["passages"]["passage_text"][row["passages"]["is_selected"]==1] for _, row in msmarco.iterrows()] | |
| msmarco_correct_answers = msmarco[msmarco["answers"].apply(lambda x: len(x) == 1)] | |
| msmarco_correct_answers = msmarco_correct_answers[msmarco_correct_answers["wellFormedAnswers"].apply(lambda x: len(x) == 0)] | |
| msmarco_correct_answers.dropna(inplace=True) | |
| msmarco_correct_answers["answer"] = msmarco_correct_answers["answers"].apply(lambda x: x[0]) | |
| msmarco_correct_answers.drop(["wellFormedAnswers", "answers"], axis=1, inplace=True) | |
| msmarco_correct_answers.reset_index(inplace=True, drop=True) | |
| ``` | |