| from __future__ import annotations |
|
|
| import os |
|
|
| import pandas as pd |
| import requests |
| from huggingface_hub import create_repo, upload_file |
|
|
| MAX_OUTPUT = 10000 |
| NB_RESULTS = 100000 |
| ORGANIZATION = "lyon-nlp/" |
| REPO_NAME = "clustering-hal-s2s" |
| SAVE_PATH = "test.jsonl" |
|
|
| df_papers = pd.DataFrame(columns=["hal_id", "title", "domain"]) |
|
|
|
|
| start_index = 0 |
| while start_index < NB_RESULTS: |
| response = requests.request( |
| "GET", |
| f"https://api.archives-ouvertes.fr/search/?q=*:*&wt=json&fl=halId_s,title_s,level0_domain_s&fq=language_s:fr&fq=submittedDateY_i:[2000%20TO%20*]&rows={MAX_OUTPUT}&start={start_index}", |
| ) |
| if "response" in response.json(): |
| papers = response.json()["response"]["docs"] |
| for paper in papers: |
| if ("title_s" in paper) and ("level0_domain_s" in paper): |
| paper_info = { |
| "hal_id": paper["halId_s"], |
| "title": paper["title_s"][0], |
| "domain": paper["level0_domain_s"][0], |
| } |
| df_papers = pd.concat( |
| [df_papers, pd.DataFrame([paper_info])], ignore_index=True |
| ) |
| start_index += MAX_OUTPUT |
|
|
| df_papers = df_papers.drop_duplicates() |
| df_papers.to_json(SAVE_PATH, orient="records", lines=True) |
|
|
| create_repo(ORGANIZATION + REPO_NAME, repo_type="dataset") |
|
|
| upload_file( |
| path_or_fileobj=SAVE_PATH, |
| path_in_repo=SAVE_PATH, |
| repo_id=ORGANIZATION + REPO_NAME, |
| repo_type="dataset", |
| ) |
| os.system(f"rm {SAVE_PATH}") |
|
|
