Update README.md

README.md

@@ -42,16 +42,16 @@ I also evaluated the model on 20K dataset of video from youtube. We extract the
 
 How to use the model
 
-tokenizer = AutoTokenizer.from_pretrained("banhabang/t5_model_75000")
-model = AutoModelForSeq2SeqLM.from_pretrained("banhabang/t5_model_75000")
+tokenizer = AutoTokenizer.from_pretrained("banhabang/vit5-base-tag-generation")
+model = AutoModelForSeq2SeqLM.from_pretrained("banhabang/vit5-base-tag-generation")
 model.to('cuda')
 
- encoding = tokenizer(ytb['Title'][i], return_tensors="pt")
- input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
- outputs = model.generate(
-      input_ids=input_ids, attention_mask=attention_masks,
-      max_length=30,
-      early_stopping=True
-  )
-  for output in outputs:
-      outputs = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+encoding = tokenizer(ytb['Title'][i], return_tensors="pt")
+input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
+outputs = model.generate(
+    input_ids=input_ids, attention_mask=attention_masks,
+    max_length=30,
+    early_stopping=True
+)
+for output in outputs:
+  outputs = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)