def get_likeliest_prediction(cls, model, context: str, options: List[str]):
"""Return the likeliest option
:param model: the model
:param context: prefix string
:param options: list of possible suffixes
:return: index of likeliest option
"""
log_probs = model.cond_log_prob(
inputs=context,
targets=options,
)
option_log_probs = np.array(
[np.sum(token_log_probs) for token_log_probs in log_probs]
)
return np.argmax(option_log_probs)
prompts = ["These are the times that", "Stately, plump Buck Mulligan"]
responses = model.generate_text(inputs=prompts,
max_length=32,
stop_string=".")
for p, r in zip(prompts, responses):
print(f"prompt: {p}")
print(f"response: {r}")
# for testing, the prompt here has no trailing space, while the
# next scoring example has a trailing space
prompt = (f"What color is the sky? Answer: blue\n"
f"What color is grass? Answer:")
choices = ("red", "blue", "green")
scores = model.cond_log_prob(inputs=prompt, targets=choices)
print("\n")
print(f"prompt:\n{prompt}")
print(f"scores:")
for c, s in zip(choices, scores):
print(f" {c:>8}: {s:0.2f}")
prompts = [
f"What color is the sky? Answer: blue\n"
f"What color is grass? Answer: ",
f"What is 1+1? Answer: 2\n"
f"What is 2+2? Answer: ",
]
choices = [("red", "blue", "green"), ("1", "2", "3", "4")]