| 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') |