Struct BartForSequenceClassification

pub struct BartForSequenceClassification { /* private fields */ }

BART Model for sequence classification

BART model with a classification head It is made of the following blocks:

• base_model: BartModel Base BART model
• classification_head: BartClassificationHead made of 2 linear layers mapping hidden states to a target class
• eos_token_id: token id for the EOS token carrying the pooled representation for classification

Implementations

impl BartForSequenceClassification

pub fn new<'p, P>( p: P, config: &BartConfig, ) -> Result<BartForSequenceClassification, RustBertError>

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

Build a new BartForSequenceClassification

Arguments

• p - Variable store path for the root of the BART model
• config - BartConfig object defining the model architecture

Example

use rust_bert::bart::{BartConfig, BartForSequenceClassification};
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 = BartConfig::from_file(config_path);
let bart: BartForSequenceClassification =
    BartForSequenceClassification::new(&p.root() / "bart", &config).unwrap();

pub fn forward_t( &self, input_ids: &Tensor, attention_mask: Option<&Tensor>, encoder_output: Option<&Tensor>, decoder_input_ids: Option<&Tensor>, decoder_attention_mask: Option<&Tensor>, train: bool, ) -> BartModelOutput

Forward pass through the model

Arguments

• input_ids - Optional input tensor of shape (batch size, source_sequence_length). Must be provided when not running in generation mode
• attention_mask - Optional attention mask of shape (batch size, source_sequence_length) for the encoder positions. Positions with a mask with value 0 will be masked.
• encoder_outputs - Optional tuple made of a tensor of shape (batch size, source_sequence_length, encoder_hidden_dim) and optional vectors of tensors of length num_encoder_layers with shape (batch size, source_sequence_length, hidden_size). These correspond to the encoder last hidden state and optional hidden states/attention weights for encoder layers. When provided, the encoder hidden state will not be recalculated. Useful for generation tasks.
• decoder_input_ids - Optional input tensor of shape (batch size, target_sequence_length). Must be provided when running in generation mode (e.g. initialized with a BOS token)
• decoder_attention_mask - Optional attention mask of shape (batch size, target_sequence_length) for the decoder positions. Positions with a mask with value 0 will be masked.
• train - boolean flag to turn on/off the dropout layers in the model. Should be set to false for inference.

Returns

• BartModelOutput containing:
  • decoder_output - Tensor of shape (batch size, num_classes) representing the activations for each class and batch item
  • encoder_hidden_states - Option<Tensor> of shape (batch size, source_sequence_length, hidden_size) representing the activations of the last encoder hidden state if it was not provided, otherwise None.
  • cache - (Option<Tensor>, Option<Vec<&LayerState, &LayerState>>) of length n_layer containing the encoder padding mask and past keys and values for both the self attention and the encoder cross attention of each layer of the decoder.
  • all_encoder_hidden_states - Option<Vec<Tensor>> of length num_encoder_layers with shape (batch size, source_sequence_length, hidden_size)
  • all_encoder_attentions - Option<Vec<Tensor>> of length num_encoder_layers with shape (batch size, source_sequence_length, hidden_size)
  • all_decoder_hidden_states - Option<Vec<Tensor>> of length num_decoder_layers with shape (batch size, target_sequence_length, hidden_size)
  • all_decoder_attentions - Option<Vec<Tensor>> of length num_decoder_layers with shape (batch size, target_sequence_length, hidden_size)

Example

use rust_bert::bart::{BartConfig, BartForSequenceClassification};
 let (batch_size, source_sequence_length, target_sequence_length) = (64, 128, 56);
 let input_tensor = Tensor::rand(&[batch_size, source_sequence_length], (Int64, device));
 let target_tensor = Tensor::rand(&[batch_size, target_sequence_length], (Int64, device));
 let encoder_attention_mask = Tensor::ones(&[batch_size, source_sequence_length], (Int64, device));
 let decoder_attention_mask = Tensor::ones(&[batch_size, source_sequence_length], (Int64, device));

 let model_output = no_grad(|| {
   bart_model
        .forward_t(&input_tensor,
                   Some(&encoder_attention_mask),
                   None,
                   Some(&target_tensor),
                   Some(&decoder_attention_mask),
                   false)
   });