ContrastiveSampler
ContrastiveSampler class
keras_hub.samplers.ContrastiveSampler(k=5, alpha=0.6, **kwargs)
Contrastive Sampler class.
This sampler implements contrastive search algorithm. In short, the sampler chooses the token having the max "score" as the next token. The "score" is a weighted sum between token's probability and max similarity against previous tokens. By using this joint score, contrastive sampler reduces the behavior of duplicating seen tokens.
Arguments
- k: int, the
kvalue of top-k. Next token will be chosen from k tokens. - alpha: float, the weight of minus max similarity in joint score
computation. The larger the value of
alpha, the score relies more on the similarity than the token probability.
Call arguments
{{call_args}}
Examples
causal_lm = keras_hub.models.GPT2CausalLM.from_preset("gpt2_base_en")
# Pass by name to compile.
causal_lm.compile(sampler="contrastive")
causal_lm.generate(["Keras is a"])
# Pass by object to compile.
sampler = keras_hub.samplers.ContrastiveSampler(k=5)
causal_lm.compile(sampler=sampler)
causal_lm.generate(["Keras is a"])