Struct rust_bert::bert::BertEmbeddings

pub struct BertEmbeddings { /* fields omitted */ }

BertEmbeddings implementation for BERT model

Implementation of the BertEmbedding trait for BERT models

Trait Implementations

impl BertEmbedding for BertEmbeddings

fn new(p: &Path, config: &BertConfig) -> BertEmbeddings

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

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, &'static str>

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.