siup

hf.py

@@ -3,14 +3,15 @@ from transformers import GPT2Tokenizer, GPT2LMHeadModel, AutoModelForCausalLM
 import sys
 import regex as re
 
-import sys
+import pdb
-
 tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
 model = AutoModelForCausalLM.from_pretrained('gpt2').to('cuda')
 
-
+a = ['I took part in many conferences and competitions at the \t and international']
 for line in sys.stdin:
+# for line in a:
     input_text = line.split('\t')[-2].rstrip()
+    right_context = line.split('\t')[-1].rstrip()
     input_ids = tokenizer.encode(input_text, return_tensors='pt').to('cuda')
 
     with torch.no_grad():
@@ -20,24 +21,40 @@ for line in sys.stdin:
     token_logits = outputs.logits[:, -1, : ]
     probs = torch.nn.functional.softmax(token_logits, dim=1)[0]
 
-    top = torch.topk(probs, 10)
+    top = torch.topk(probs, 4)
+    
     top_indices = top.indices.tolist()
     top_probs = top.values.tolist()
     top_words = [tokenizer.decode(x) for x in top_indices]
+    # pdb.set_trace()
+    
+    
+    
+    right_encoded = tokenizer.encode(right_context, return_tensors='pt').to('cuda')
+    first_word = right_encoded[0][0].unsqueeze(0).unsqueeze(0)
+    second_word = right_encoded[0][1]  
     
     string_to_print = ''
     sum_probs = 0
-
+    for p, w_i, w in zip(top_probs, top_indices, top_words):
-    for w, p in zip(top_words, top_probs):
-        if '<unk>' in w:
-            continue
         if re.search(r'\p{L}+', w):
-            string_to_print += f"{w}:{p} "
+            # pdb.set_trace()
-            sum_probs += p
+            buff = torch.tensor([w_i]).unsqueeze(0).to('cuda')
-    if string_to_print == '':
+            input_ids = torch.cat((buff, first_word), dim=-1)
-        print(f"the:0.2 a:0.3 :0.5")
+            with torch.no_grad():
-        continue
+                outputs = model(input_ids)
+                
+            token_logits = outputs.logits[:, -1, : ]
+
+            probs = torch.nn.functional.softmax(token_logits, dim=1)[0]
+            
+            new_probs = (p + probs[second_word]) * 0.5
+            string_to_print += f"{w}:{new_probs} "
+            sum_probs += new_probs
+    
+    
     unknow_prob = 1 - sum_probs
     string_to_print += f":{unknow_prob}"
     string_to_print = re.sub(' +', ' ', string_to_print)
     print(string_to_print.rstrip().strip())
+