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Update logits_process.py docstrings (#25971)

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larekrow 2023-09-12 19:36:31 +08:00 committed by GitHub
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src/transformers/generation/logits_process.py
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@ -272,7 +272,7 @@ class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
[`LogitsProcessor`] that prevents the repetition of previous tokens through an exponential penalty. This technique [`LogitsProcessor`] that prevents the repetition of previous tokens through an exponential penalty. This technique
shares some similarities with coverage mechanisms and other aimed at reducing repetition. During the text shares some similarities with coverage mechanisms and other aimed at reducing repetition. During the text
generation process, the probability distribution for the next token is determined using a formula that incorporates generation process, the probability distribution for the next token is determined using a formula that incorporates
token scores based on their occurrence in the generated sequence. Tokens with higher scores are less likely to be token scores based on their occurrence in the generated sequence. Tokens with higher scores are more likely to be
selected. The formula can be seen in the original [paper](https://arxiv.org/pdf/1909.05858.pdf). According to the selected. The formula can be seen in the original [paper](https://arxiv.org/pdf/1909.05858.pdf). According to the
paper a penalty of around 1.2 yields a good balance between truthful generation and lack of repetition. paper a penalty of around 1.2 yields a good balance between truthful generation and lack of repetition.
@ -328,7 +328,7 @@ class EncoderRepetitionPenaltyLogitsProcessor(LogitsProcessor):
hallucination_penalty (`float`): hallucination_penalty (`float`):
The parameter for hallucination penalty. 1.0 means no penalty. The parameter for hallucination penalty. 1.0 means no penalty.
encoder_input_ids (`torch.LongTensor`): encoder_input_ids (`torch.LongTensor`):
The encoder_input_ids that should not be repeated within the decoder ids. The encoder_input_ids that should be repeated within the decoder ids.
""" """
def __init__(self, penalty: float, encoder_input_ids: torch.LongTensor): def __init__(self, penalty: float, encoder_input_ids: torch.LongTensor):