Struct BertEncoder 

pub struct BertEncoder {
    pub hp: BertHparams,
    pub tok_embd: WeightMatrix,
    pub type_embd_row0: Option<Vec<f32>>,
    pub pos_embd: WeightMatrix,
    pub tok_norm_w: Vec<f32>,
    pub tok_norm_b: Vec<f32>,
    pub layers: Vec<BertLayer>,
}

Fields

hp: BertHparams

tok_embd: WeightMatrix

type_embd_row0: Option<Vec<f32>>

Row 0 of token_types.weight — “Sentence A”. See the module docs.

pos_embd: WeightMatrix

tok_norm_w: Vec<f32>

tok_norm_b: Vec<f32>

layers: Vec<BertLayer>

Implementations

impl BertEncoder

pub fn vocab_size(&self) -> usize

Trait Implementations

impl TextEncoder for BertEncoder

fn wrap_special(&self, pieces: &[u32]) -> Vec<u32>

[CLS] … [SEP], which is what llama.cpp’s WPM branch adds when add_special is set: it pushes special_bos_id before the pieces and special_sep_id after, unconditionally — the add_bos/add_eos flags are not consulted on that path (llama-vocab.cpp, case LLAMA_VOCAB_TYPE_WPM).

fn n_embd(&self) -> usize

Width of one hidden-state row, and of the pooled embedding.

fn n_ctx_train(&self) -> usize

Longest sequence this checkpoint can represent. For a learned position table this is the table’s height, and exceeding it is an error rather than a degradation.

fn pooling_type(&self) -> PoolingType

What the checkpoint’s own {arch}.pooling_type said.

fn encode_tokens(&self, tokens: &[u32]) -> Result<Vec<f32>, EncodeError>

n_tokens × n_embd hidden states, in row order.

fn embed_tokens(&self, tokens: &[u32]) -> Result<Vec<f32>, EncodeError>

Self::encode_tokens followed by the checkpoint’s own pooling. Not L2-normalized — see crate::pooling::pool.