from collections import Counter import json import re import datasets import pandas as pd from huggingface_hub import create_repo, upload_file, hf_hub_download from huggingface_hub.utils._errors import HfHubHTTPError ######################## # Cleanup queries data # ######################## # load dataset dl_path = hf_hub_download( repo_id="antoinelb7/alloprof", filename="data/alloprof.csv", repo_type="dataset", revision="0faa90fee1ad1a6e3e461d7be49abf71488e6687" ) alloprof_queries = pd.read_csv(dl_path) # remove non-queries alloprof_queries = alloprof_queries[alloprof_queries["is_query"]] # remove nans in text alloprof_queries = alloprof_queries[~alloprof_queries["text"].isna()] # most data flagged as language "en" are actually french. We je remove english ones # by matching specifig words alloprof_queries = alloprof_queries[ ~( (alloprof_queries["text"].str.lower().str.startswith("hi")) | (alloprof_queries["text"].str.lower().str.startswith("hello")) | (alloprof_queries["text"].str.lower().str.startswith("how")) | (alloprof_queries["text"].str.lower().str.startswith("i ")) ) ] # only keep queries with french relevant documents alloprof_queries = alloprof_queries[ (~alloprof_queries["relevant"].isna()) & (alloprof_queries["relevant"].str.endswith("-fr")) ] # remove queries with url in text because question relies on picture alloprof_queries = alloprof_queries[~alloprof_queries["text"].str.contains("https://www.alloprof.qc.ca")] # split multiple relevant docs and remove -fr suffix on id def parse_relevant_ids(row): row = row.split(";") row = [r[:-3] for r in row if r.endswith("-fr")] return row alloprof_queries["relevant"] = alloprof_queries["relevant"].apply(parse_relevant_ids) # Parse the answer def parse_answer(row): try: row = json.loads(row) text = [] for i in row: if type(i["insert"]) is not dict: text.append(i["insert"]) text = "".join(text) except: text = row return text.replace(" ", " ").replace("\u200b", "").replace("\xa0", "") alloprof_queries["answer"] = alloprof_queries["answer"].apply(parse_answer) # only keep useful columns alloprof_queries = alloprof_queries[["id", "text", "answer", "relevant", "subject"]] # remove duplicate queries (same text) alloprof_queries = alloprof_queries.drop_duplicates(subset=["text"], keep="first") ########################## # Cleanup documents data # ########################## # load dataset dl_path = hf_hub_download( repo_id="antoinelb7/alloprof", filename="data/pages/page-content-fr.json", repo_type="dataset", revision="0faa90fee1ad1a6e3e461d7be49abf71488e6687" ) alloprof_docs = pd.read_json(dl_path) # Remove Nans in data alloprof_docs = alloprof_docs[~alloprof_docs["data"].isna()] # parse dataset def parse_row(row): return [row["file"]["uuid"], row["file"]["title"], row["file"]["topic"]] def get_text(row): text = [] for s in row["file"]["sections"]: for m in s["modules"]: if m["type"] == "blocSpecial": if m["subtype"] in ["definition", "exemple"]: for sm in m["submodules"]: if sm["type"] == "text": text.append(sm["text"]) elif m["type"] == "text": text.append(m["text"]) text = " ".join(text) text = re.sub("<[^<]+?>", "", text) text = text.replace(" ", " ").replace("\u200b", "") text = re.sub("\s{2,}", " ", text) return text parsed_df = alloprof_docs["data"].apply(parse_row) alloprof_docs[["uuid", "title", "topic"]] = parsed_df.tolist() alloprof_docs["text"] = alloprof_docs["data"].apply(get_text) # remove unnecessary columns alloprof_docs = alloprof_docs[["uuid", "title", "topic", "text"]] ################ # Post Process # ################ # check that all relevant docs mentioned in queries are in docs dataset relevants = alloprof_queries["relevant"].tolist() relevants = {i for j in relevants for i in j} # flatten list and get uniques assert relevants.issubset( alloprof_docs["uuid"].tolist() ), "Some relevant document of queries are not present in the corpus" # convert to Dataset alloprof_queries = datasets.Dataset.from_pandas(alloprof_queries) alloprof_docs = datasets.Dataset.from_pandas(alloprof_docs) # identify duplicate documents # (duplicates are actually error documents, # such as "fiche en construction", " ", ... duplicate_docs = Counter(alloprof_docs["text"]) duplicate_docs = {k:v for k,v in duplicate_docs.items() if v > 1} # for each text that is in duplicate... for dup_text in duplicate_docs: # ...get the ids of docs that have that text duplicate_ids = [d["uuid"] for d in alloprof_docs if d["text"] == dup_text] # ...delete all the documents that have these ids from the corpus dataset alloprof_docs = alloprof_docs.filter(lambda x: x["uuid"] not in duplicate_ids) # ...delete them from the relevant documents in queries alloprof_queries = alloprof_queries.map(lambda x: {"relevant": [i for i in x["relevant"] if i not in duplicate_ids]}) # remove the queries that have no remaining relevant documents alloprof_queries = alloprof_queries.filter(lambda x: len(x["relevant"]) > 0) # split queries into train-test alloprof_queries = alloprof_queries.train_test_split(test_size=.2) #################### # Upload to HF Hub # #################### # create HF repo repo_id = "lyon-nlp/alloprof" try: create_repo(repo_id, repo_type="dataset") except HfHubHTTPError as e: print("HF repo already exist") # save datasets as json alloprof_queries["train"].to_pandas().to_json("queries-train.json", orient="records") alloprof_queries["test"].to_pandas().to_json("queries-test.json", orient="records") alloprof_docs.to_pandas().to_json("documents.json", orient="records") upload_file(path_or_fileobj="queries-train.json", path_in_repo="queries-train.json", repo_id=repo_id, repo_type="dataset") upload_file(path_or_fileobj="queries-test.json", path_in_repo="queries-test.json", repo_id=repo_id, repo_type="dataset") upload_file(path_or_fileobj="documents.json", path_in_repo="documents.json", repo_id=repo_id, repo_type="dataset")