readme.md

README.md

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+import pandas as pd
+import os
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers.optimization import  Adafactor 
+import time
+import warnings
+warnings.filterwarnings('ignore')
+
+tokenizer = T5Tokenizer.from_pretrained('Sachinkelenjaguri/sa_T5_Table_to_text')
+model = T5ForConditionalGeneration.from_pretrained('Sachinkelenjaguri/sa_T5_Table_to_text', return_dict=True)
+#moving the model to device(GPU/CPU)
+
+def generate(text):
+  model.eval()
+  input_ids = tokenizer.encode("WebNLG:{} </s>".format(text), return_tensors="pt")  # Batch size 1
+  # input_ids.to(dev)
+  s = time.time()
+  outputs = model.generate(input_ids)
+  gen_text=tokenizer.decode(outputs[0]).replace('<pad>','').replace('</s>','')
+  elapsed = time.time() - s
+  print('Generated in {} seconds'.format(str(elapsed)[:4]))
+  
+  return gen_text
+
+
+generate(' Russia | leader | Putin')