Update README.md

README.md

@@ -1,3 +1,84 @@
----
-license: mit
----
+---
+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()))
+
+```