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from transformers import T5ForConditionalGeneration
from transformers import T5TokenizerFast as T5Tokenizer
import pandas as pd
model = "svjack/comet-atomic-en"
device = "cpu"
#device = "cuda:0"
tokenizer = T5Tokenizer.from_pretrained(model)
model = T5ForConditionalGeneration.from_pretrained(model).to(device).eval()
NEED_PREFIX = 'What are the necessary preconditions for the next event?'
EFFECT_PREFIX = 'What could happen after the next event?'
INTENT_PREFIX = 'What is the motivation for the next event?'
REACT_PREFIX = 'What are your feelings after the following event?'
event = "X had a big meal."
for prefix in [NEED_PREFIX, EFFECT_PREFIX, INTENT_PREFIX, REACT_PREFIX]:
prompt = "{}{}".format(prefix, event)
encode = tokenizer(prompt, return_tensors='pt').to(device)
answer = model.generate(encode.input_ids,
max_length = 128,
num_beams=2,
top_p = 0.95,
top_k = 50,
repetition_penalty = 2.5,
length_penalty=1.0,
early_stopping=True,
)[0]
decoded = tokenizer.decode(answer, skip_special_tokens=True)
print(prompt, "\n---Answer：", decoded, "----\n")
What are the necessary preconditions for the next event?X had a big meal.
---Answer： X goes shopping at the supermarket ----
What could happen after the next event?X had a big meal.
---Answer： X gets fat ----
What is the motivation for the next event?X had a big meal.
---Answer： X wants to eat ----
What are your feelings after the following event?X had a big meal.
---Answer： X tastes good ----
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