Datasets:

rag-datasets
/

rag-mini-bioasq

Tasks:

Question Answering

Sentence Similarity

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Tags:

information-retrieval

question-answering

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rag-mini-bioasq / generate.py

tillwenke

before creating dataset

463a104 8 months ago

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3.39 kB

	import json

	import pandas as pd
	from Bio import Entrez
	from retry import retry
	from tqdm import tqdm
	import dask.dataframe as dd

	# provided your NIH credentials
	# read from .json file
	with open("credentials.json") as f:
	credentials = json.load(f)
	Entrez.email = credentials["email"]
	Entrez.api_key = credentials["api_key"]


	# change output file names here if necessary
	RAW_EVALUATION_DATASET = "./raw_data/training11b.json"
	PATH_TO_PASSAGE_DATASET = "./data/passages.parquet"
	PATH_TO_EVALUATION_DATASET = "./data/test.parquet"

	# only use questions that have at most MAX_PASSAGES passages to control the size of the dataset
	# set to None to use all questions
	MAX_PASSAGES = None


	@retry()
	def get_abstract(passage_id):
	with Entrez.efetch(
	db="pubmed", id=passage_id, rettype="abstract", retmode="text"
	) as response:
	# get only the abstract - no metadata
	r = response.read()
	r = r.split("\n\n")
	abstract = max(r, key=len)
	return abstract


	if __name__ == "__main__":
	# load the training data containing the questions, answers and the ids of relevant passages
	# but lacks the actual passages
	with open(RAW_EVALUATION_DATASET) as f:
	eval_data = json.load(f)["questions"]

	eval_df = pd.DataFrame(eval_data, columns=["body", "documents", "ideal_answer"])
	eval_df = eval_df.rename(
	columns={
	"body": "question",
	"documents": "relevant_passage_ids",
	"ideal_answer": "answer",
	}
	)
	eval_df.answer = eval_df.answer.apply(lambda x: x[0])
	# get abstract id from url
	eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply(
	lambda x: [int(url.split("/")[-1]) for url in x]
	)
	if MAX_PASSAGES:
	eval_df["passage_count"] = eval_df.relevant_passage_ids.apply(lambda x: len(x))
	eval_df = eval_df.drop(columns=["passage_count"])

	# remove duplicate passage ids
	eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply(lambda x: set(x))
	eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply(lambda x: list(x))

	# get all passage ids that are relevant
	passage_ids = set().union(*eval_df.relevant_passage_ids)
	passage_ids = list(passage_ids)
	passages = pd.DataFrame(index=passage_ids)

	for i, passage_id in enumerate(tqdm(passages.index)):
	passages.loc[passage_id, "passage"] = get_abstract(passage_id)

	# intermediate save
	if i % 1000 == 0:
	dd.from_pandas(passages, npartitions=1).to_parquet(PATH_TO_PASSAGE_DATASET)


	# filter out the passages whos pmids (pubmed ids) where not available
	unavailable_passages = passages[passages["passage"] == "1. "]
	passages = passages[passages["passage"] != "1. "]
	passages.index.name = "id"
	dd.from_pandas(passages, npartitions=1).to_parquet(PATH_TO_PASSAGE_DATASET)

	# remove passages from evaluation dataset whose abstract could not be retrieved from pubmed website
	unavailable_ids = unavailable_passages.index.tolist()
	eval_df["relevant_passage_ids"] = eval_df["relevant_passage_ids"].apply(
	lambda x: [i for i in x if i not in unavailable_ids]
	)
	eval_df.index.name = "id"
	eval_df = eval_df[["question", "answer", "relevant_passage_ids"]]
	dd.from_pandas(eval_df, npartitions=1).to_parquet(PATH_TO_EVALUATION_DATASET)