Struct rust_bert::models::bert::BertEmbeddings

pub struct BertEmbeddings { /* private fields */ }

BertEmbeddings implementation for BERT model

Implementation of the BertEmbedding trait for BERT models

Trait Implementations

impl BertEmbedding for BertEmbeddings

fn new<'p, P>(p: P, config: &BertConfig) -> BertEmbeddings

where P: Borrow<Path<'p>>,

Build a new BertEmbeddings

Arguments

• p - Variable store path for the root of the BertEmbeddings model
• config - BertConfig object defining the model architecture and vocab/hidden size

Example

use rust_bert::bert::{BertConfig, BertEmbedding, BertEmbeddings};
use rust_bert::Config;
use std::path::Path;
use tch::{nn, Device};

let config_path = Path::new("path/to/config.json");
let device = Device::Cpu;
let p = nn::VarStore::new(device);
let config = BertConfig::from_file(config_path);
let bert_embeddings = BertEmbeddings::new(&p.root() / "bert_embeddings", &config);

fn forward_t( &self, input_ids: Option<&Tensor>, token_type_ids: Option<&Tensor>, position_ids: Option<&Tensor>, input_embeds: Option<&Tensor>, train: bool ) -> Result<Tensor, RustBertError>

Forward pass through the embedding layer

Arguments

• input_ids - Optional input tensor of shape (batch size, sequence_length). If None, pre-computed embeddings must be provided (see input_embeds)
• token_type_ids -Optional segment id of shape (batch size, sequence_length). Convention is value of 0 for the first sentence (incl. SEP) and 1 for the second sentence. If None set to 0.
• position_ids - Optional position ids of shape (batch size, sequence_length). If None, will be incremented from 0.
• input_embeds - Optional pre-computed input embeddings of shape (batch size, sequence_length, hidden_size). If None, input ids must be provided (see input_ids)
• train - boolean flag to turn on/off the dropout layers in the model. Should be set to false for inference.

Returns

• embedded_output - Tensor of shape (batch size, sequence_length, hidden_size)

Example

let (batch_size, sequence_length) = (64, 128);
let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device));
let token_type_ids = Tensor::zeros(&[batch_size, sequence_length], (Int64, device));
let position_ids = Tensor::arange(sequence_length, (Int64, device))
    .expand(&[batch_size, sequence_length], true);

let embedded_output = no_grad(|| {
    bert_embeddings
        .forward_t(
            Some(&input_tensor),
            Some(&token_type_ids),
            Some(&position_ids),
            None,
            false,
        )
        .unwrap()
});

impl Debug for BertEmbeddings

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.