ContrastiveSampler

• Original Link : https://keras.io/api/keras_hub/samplers/contrastive_sampler/
• Last Checked at : 2024-11-26

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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 k value 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"])