[−][src]Struct rust_bert::pipelines::summarization::SummarizationConfig

pub struct SummarizationConfig {
    pub model_resource: Resource,
    pub config_resource: Resource,
    pub vocab_resource: Resource,
    pub merges_resource: Resource,
    pub min_length: u64,
    pub max_length: u64,
    pub do_sample: bool,
    pub early_stopping: bool,
    pub num_beams: u64,
    pub temperature: f64,
    pub top_k: u64,
    pub top_p: f64,
    pub repetition_penalty: f64,
    pub length_penalty: f64,
    pub no_repeat_ngram_size: u64,
    pub num_return_sequences: u64,
    pub device: Device,
}

Configuration for text summarization

Contains information regarding the model to load, mirrors the GenerationConfig, with a different set of default parameters and sets the device to place the model on.

Fields

model_resource: Resource

Model weights resource (default: pretrained BART model on CNN-DM)

config_resource: Resource

Config resource (default: pretrained BART model on CNN-DM)

vocab_resource: Resource

Vocab resource (default: pretrained BART model on CNN-DM)

merges_resource: Resource

Merges resource (default: pretrained BART model on CNN-DM)

min_length: u64

Minimum sequence length (default: 0)

max_length: u64

Maximum sequence length (default: 20)

do_sample: bool

Sampling flag. If true, will perform top-k and/or nucleus sampling on generated tokens, otherwise greedy (deterministic) decoding (default: true)

early_stopping: bool

Early stopping flag indicating if the beam search should stop as soon as num_beam hypotheses have been generated (default: false)

num_beams: u64

Number of beams for beam search (default: 5)

temperature: f64

Temperature setting. Values higher than 1 will improve originality at the risk of reducing relevance (default: 1.0)

top_k: u64

Top_k values for sampling tokens. Value higher than 0 will enable the feature (default: 0)

top_p: f64

Top_p value for Nucleus sampling, Holtzman et al.. Keep top tokens until cumulative probability reaches top_p (default: 0.9)

repetition_penalty: f64

Repetition penalty (mostly useful for CTRL decoders). Values higher than 1 will penalize tokens that have been already generated. (default: 1.0)

length_penalty: f64

Exponential penalty based on the length of the hypotheses generated (default: 1.0)

no_repeat_ngram_size: u64

Number of allowed repetitions of n-grams. Values higher than 0 turn on this feature (default: 3)

num_return_sequences: u64

Number of sequences to return for each prompt text (default: 1)

device: Device

Device to place the model on (default: CUDA/GPU when available)

Trait Implementations

impl Default for SummarizationConfig[src]

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