Update README.md

README.md

@@ -36,7 +36,7 @@ datasets:
 >>> re_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-relation-extraction")
 >>> re_pip = pipeline("text-classification", model=re_model, tokenizer=re_tokenizer)
 
-def process_ner_output(entity_mention, input):
+def process_ner_output(entity_mention, inputs):
     re_input = []
     for idx1 in range(len(entity_mention) - 1):
         for idx2 in range(idx1 + 1, len(entity_mention)):
@@ -50,7 +50,7 @@ def process_ner_output(entity_mention, input):
             ent_2_s = ent_2['start']
             ent_2_e = ent_2['end']
             new_re_input = ""
-            for c_idx, c in enumerate(input):
+            for c_idx, c in enumerate(inputs):
                 if c_idx == ent_1_s:
                     new_re_input += "<{}>".format(ent_1_type)
                 elif c_idx == ent_1_e:
@@ -60,7 +60,7 @@ def process_ner_output(entity_mention, input):
                 elif c_idx == ent_2_e:
                     new_re_input += "</{}>".format(ent_2_type)
                 new_re_input += c
-            re_input.append({"re_input": new_re_input, "arg1": ent_1, "arg2": ent_2, "input": input})
+            re_input.append({"re_input": new_re_input, "arg1": ent_1, "arg2": ent_2, "input": inputs})
     return re_input
     
 def post_process_re_output(re_output, re_input, ner_output):