Update README.md

README.md

@@ -1,24 +1,33 @@
 ---
 license: mit
+base_model:
+- paust/pko-t5-base
+pipeline_tag: text2text-generation
 ---
 
 paust/pko-t5-base model based
 
-Since this model based on paust/pko-t5-base tokenizer, you need to import it.
+Since this model is based on paust/pko-t5-base tokenizer, you need to import it.
 
-'''
-import torch
-from transformers import T5Model, T5Tokenizer, T5ForConditionalGeneration
+from transformers import T5TokenizerFast, T5ForConditionalGeneration
+tokenizer = T5TokenizerFast.from_pretrained("paust/pko-t5-base")
+model = T5ForConditionalGeneration.from_pretrained(emotionanalysis/diaryempathizer-t5-ko)
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
 
-tokenizer = T5Tokenizer.from_pretrained("KETI-AIR/ke-t5-base-ko")
+  import torch
+  from transformers import T5TokenizerFast, T5ForConditionalGeneration
 
-inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True, padding="max_length")
-inputs = {key: value.to(device) for key, value in inputs.items()}
-outputs = model.generate(input_ids=inputs["input_ids"], max_length=128, num_beams=4, early_stopping=True)
-
-generated_comment = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(generated_comment)
-'''
+  model = T5ForConditionalGeneration.from_pretrained(emotionanalysis/diaryempathizer-t5-ko)
+  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+  model.to(device)
+  
+  tokenizer = T5TokenizerFast.from_pretrained("paust/pko-t5-base")
+  input_text = """
+  """
+  
+  inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True, padding="max_length")
+  inputs = {key: value.to(device) for key, value in inputs.items()}
+  outputs = model.generate(input_ids=inputs["input_ids"], max_length=128, num_beams=4, early_stopping=True)
+  
+  generated_comment = tokenizer.decode(outputs[0], skip_special_tokens=True)
+  print(generated_comment)