Update README.md

README.md

@@ -27,12 +27,18 @@ Training hyperparameters
 
 The following hyperparameters were used during training:
 
-num_train_epochs=2, \n
-learning_rate=1e-5, \n
+num_train_epochs=2,
+
+learning_rate=1e-5, 
+
 warmup_ratio=0.05,
+
 weight_decay=0.01,
+
 per_device_train_batch_size=4,
+
 per_device_eval_batch_size=4,
+
 group_by_length=True,
 
 I also evaluated the model on 20K dataset of video from youtube. We extract the title and tags (if possible) which is the input of the model. With videos with tags, we directly compare our tags with the existing tags. Otherwise, the obtained tags are evaluated by human. We see the results on link: https://drive.google.com/drive/folders/1RvywNl41QYNa2lthp-O8hakVCMsfX456
@@ -44,11 +50,15 @@ 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/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,