---
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 :

https://api.archives-ouvertes.fr/search/UNIV-COTEDAZUR/?q=docType_s:ART&fq=abstract_s:[%22%22%20TO%20*]&fq=domain_s:*shs*&fq=submittedDateY_i:[2020%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

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-mpnet-base-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()))

```