Struct rust_bert::pipelines::token_classification::TokenClassificationConfig

pub struct TokenClassificationConfig {
    pub model_type: ModelType,
    pub model_resource: ModelResource,
    pub config_resource: Box<dyn ResourceProvider + Send>,
    pub vocab_resource: Box<dyn ResourceProvider + Send>,
    pub merges_resource: Option<Box<dyn ResourceProvider + Send>>,
    pub lower_case: bool,
    pub strip_accents: Option<bool>,
    pub add_prefix_space: Option<bool>,
    pub device: Device,
    pub kind: Option<Kind>,
    pub label_aggregation_function: LabelAggregationOption,
    pub batch_size: usize,
}

Configuration for TokenClassificationModel

Contains information regarding the model to load and device to place the model on.

Fields

model_type: ModelType

Model type

model_resource: ModelResource

Model weights resource (default: pretrained BERT model on CoNLL)

config_resource: Box<dyn ResourceProvider + Send>

Config resource (default: pretrained BERT model on CoNLL)

vocab_resource: Box<dyn ResourceProvider + Send>

Vocab resource (default: pretrained BERT model on CoNLL)

merges_resource: Option<Box<dyn ResourceProvider + Send>>

Merges resource (default: pretrained BERT model on CoNLL)

lower_case: bool

Automatically lower case all input upon tokenization (assumes a lower-cased model)

strip_accents: Option<bool>

Flag indicating if the tokenizer should strip accents (normalization). Only used for BERT / ALBERT models

add_prefix_space: Option<bool>

Flag indicating if the tokenizer should add a white space before each tokenized input (needed for some Roberta models)

device: Device

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

kind: Option<Kind>

Model weights precision. If not provided, will default to full precision on CPU, or the loaded weights precision otherwise

label_aggregation_function: LabelAggregationOption

Sub-tokens aggregation method (default: LabelAggregationOption::First)

batch_size: usize

Batch size for predictions

Implementations

impl TokenClassificationConfig

pub fn new<RC, RV>( model_type: ModelType, model_resource: ModelResource, config_resource: RC, vocab_resource: RV, merges_resource: Option<RV>, lower_case: bool, strip_accents: impl Into<Option<bool>>, add_prefix_space: impl Into<Option<bool>>, label_aggregation_function: LabelAggregationOption ) -> TokenClassificationConfig

where RC: ResourceProvider + Send + 'static, RV: ResourceProvider + Send + 'static,

Instantiate a new token classification configuration of the supplied type.

Arguments

• model_type - ModelType indicating the model type to load (must match with the actual data to be loaded!)
• model - The ResourceProvider pointing to the model to load (e.g. model.ot)
• config - The ResourceProvider pointing to the model configuration to load (e.g. config.json)
• vocab - The ResourceProvider pointing to the tokenizers’ vocabulary to load (e.g. vocab.txt/vocab.json)
• vocab - An optional ResourceProvider pointing to the tokenizers’ merge file to load (e.g. merges.txt), needed only for Roberta.
• lower_case - A bool indicating whether the tokenizer should lower case all input (in case of a lower-cased model)

Trait Implementations

impl Default for TokenClassificationConfig

fn default() -> TokenClassificationConfig

Provides a default CoNLL-2003 NER model (English)

impl From<POSConfig> for TokenClassificationConfig

fn from(pos_config: POSConfig) -> Self

Converts to this type from the input type.

impl From<TokenClassificationConfig> for POSConfig

fn from(token_classification_config: TokenClassificationConfig) -> Self

Converts to this type from the input type.