Update README.md

README.md

@@ -16,14 +16,14 @@ The parameters passed in the url request are :
 - 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,fr_keyword_s,openAccess_bool,submitType_s
+- fl=halId_s,doiId_s,uri_s,title_s,subTitle_s,authFullName_s,producedDate_s,journalTitle_s,journalPublisher_s,abstract_s,fr_keyword_s,openAccess_bool,submitType_s
 
 The embeddings corpus hal_embeddings.pkl stores the embeddings of the "combined" column values converted in vectors with the sentence-transformers/all-MiniLM-L6-v2 embeddings 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,fr_keyword_s,openAccess_bool,submitType_s"
+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=halId_s,doiId_s,uri_s,title_s,subTitle_s,authFullName_s,producedDate_s,journalTitle_s,journalPublisher_s,abstract_s,fr_keyword_s,openAccess_bool,submitType_s"
 
 # Get the total number of records
 url_for_total_count = f"{url}&wt=json&rows=0"
@@ -31,7 +31,7 @@ response = requests.request("GET", url_for_total_count).text
 data = json.loads(response)
 total_count = data["response"]["numFound"]
 print(total_cout)
-# return 3601
+# return 3613
 
 # Loop over the records and get metadata
 step = 500
@@ -43,10 +43,13 @@ for i in range(1, int(total_count), int(step)):
 df = pd.concat(appended_data)
 
 # dedup
-df = appended_data.drop_duplicates(subset=['uri_s'])
+df = appended_data.drop_duplicates(subset=['halId_s'])
+
+# clean date
+df["producedDate_s"] = df["producedDate_s"].apply(lambda x: str(x)[0:4])
 
 df.shape
-# returns 2405
+# returns 2652
 
 # New column of concatenated textuel data
 df["combined"] = df.title_s + ". " + df.subTitle_s + ". " +df.abstract_s