---
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)
```