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)
def generate(text):
model.eval() input_ids = tokenizer.encode("WebNLG:{} ".format(text), return_tensors="pt") # Batch size 1
s = time.time() outputs = model.generate(input_ids) gen_text=tokenizer.decode(outputs[0]).replace('','').replace('','') elapsed = time.time() - s print('Generated in {} seconds'.format(str(elapsed)[:4]))
return gen_text
generate(' Russia | leader | Putin')