metadata
license: mit
task_categories:
- feature-extraction
language:
- fr
- en
This dataset comes from a request on the HAL API (the French national open archive) limited to the UNIV-COTEDAZUR portal instance. The request collects the bibliographic records of the SHS articles with abstract published between 2013 and 2023
The parameters passed in the url request are :
The embeddings column stores the embeddings of the "combined" column values converted in vectors with the sentence-transformers/all-MiniLM-L6-v2 embeddinsg model.
Metadata extraction
url = ""https://api.archives-ouvertes.fr/search/UNIV-COTEDAZUR/?q=docType_s:ART&fq=abstract_s:[%22%22%20TO%20*]&fq=domain_s:*shs*&fq=publicationDateY_i:[2013%20TO%202023]&fl=doiId_s,uri_s,title_s,subTitle_s,authFullName_s,producedDate_s,journalTitle_s,journalPublisher_s,abstract_s,domain_s,openAccess_bool"
# Get the total number of records
url_for_total_count = f"{url}&wt=json&rows=0"
response = requests.request("GET", url_for_total_count).text
data = json.loads(response)
total_count = data["response"]["numFound"]
print(total_cout)
# return 3601
# Loop over the records and get metadata
step = 500
appended_data = []
for i in range(1, int(total_count), int(step)):
url = f"{url}&rows={step}&start={i}&wt=csv"
df = pd.read_csv(url, encoding="utf-8")
appended_data.append(df)
appended_data = pd.concat(appended_data)
# dedup
appended_data = appended_data.drop_duplicates(subset=['uri_s'])
appended_data.shape
# returns 2405
Add embeddings (CPU)
Solution 1 : with HF Inference API
import requests
import json
from typing import Optional, List, Dict, Any
HF_TOKEN = "<hf_token>"
model_id = "sentence-transformers/all-MiniLM-L6-v2"
embeddings_api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
headers = {"Authorization": f"Bearer {HF_TOKEN}"}
def embeddings_query(text:str) -> List:
response = requests.post(embeddings_api_url, headers=headers, json={"inputs": text, "options":{"wait_for_model":True}})
return response.json()
df = appended_data.replace(np.nan, '')
df['embeddings'] = df.combined.apply(lambda x:embeddings_query(x.strip()))
Solution 2 : with sentence-transformers library
from sentence_transformers import SentenceTransformer
model_id = "sentence-transformers/all-MiniLM-L6-v2"
embedder = SentenceTransformer(model_id)
def embeddings_query(text:str) -> List:
return embedder.encode(text,convert_to_tensor=True)
df['embeddings'] = df.combined.apply(lambda x:embeddings_query(x.strip().to_list()))