init with scrip for ds generation

.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+training11b.json filter=lfs diff=lfs merge=lfs -text

.gitignore

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+/env

bioasq_ir_pubmed_corpus_subset.py

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+import json
+
+import pandas as pd
+from Bio import Entrez
+from retry import retry
+from tqdm import tqdm
+
+# provided your NIH credentials
+Entrez.email = "***"
+Entrez.api_key = "***"
+
+
+# change output file names here if necessary
+RAW_EVALUATION_DATASET = "training11b.json"
+PATH_TO_PASSAGE_DATASET = "./passages.parquet"
+PATH_TO_EVALUATION_DATASET = "./eval.parquet"
+
+# only use questions that have at most MAX_PASSAGES passages to control the size of the dataset
+# set to None to use all passages
+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_passages",
+            "ideal_answer": "answer",
+        }
+    )
+    eval_df.answer = eval_df.answer.apply(lambda x: x[0])
+    # get abstract id from url
+    eval_df.relevant_passages = eval_df.relevant_passages.apply(
+        lambda x: [url.split("/")[-1] for url in x]
+    )
+    if MAX_PASSAGES:
+        eval_df["passage_count"] = eval_df.relevant_passages.apply(lambda x: len(x))
+        eval_df = eval_df.drop(columns=["passage_count"])
+
+    # remove duplicate passage ids
+    eval_df.relevant_passages = eval_df.relevant_passages.apply(lambda x: set(x))
+    eval_df.relevant_passages = eval_df.relevant_passages.apply(lambda x: list(x))
+
+    # get all passage ids that are relevant
+    passage_ids = set().union(*eval_df.relevant_passages)
+    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)
+
+        # intermidiate save
+        if i % 4000 == 0:
+            passages.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.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_passages"] = eval_df["relevant_passages"].apply(
+        lambda x: [i for i in x if i not in unavailable_ids]
+    )
+    eval_df.to_parquet(PATH_TO_EVALUATION_DATASET)

requirements.txt

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+biopython
+pandas
+retry
+tqdm

training11b.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6df656862ca860efc355c7805d07ddca700d64ecc3785c519a49afccaaeeac98
+size 37639648