sa_T5_Table_to_text

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:{} </s>".format(text), return_tensors="pt") # Batch size 1

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')