Struct DebertaV2ForSequenceClassification

pub struct DebertaV2ForSequenceClassification { /* private fields */ }

DeBERTa V2 for sequence classification

Base DeBERTa V2 model with a classifier head to perform sentence or document-level classification It is made of the following blocks:

• deberta: Base Deberta (V2) Model
• classifier: BERT linear layer for classification

Implementations

impl DebertaV2ForSequenceClassification

pub fn new<'p, P>( p: P, config: &DebertaV2Config, ) -> Result<DebertaV2ForSequenceClassification, RustBertError>

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

Build a new DebertaV2ForSequenceClassification

Arguments

• p - Variable store path for the root of the DebertaForSequenceClassification model
• config - DebertaV2Config object defining the model architecture and number of classes

Example

use rust_bert::deberta_v2::{DebertaV2Config, DebertaV2ForSequenceClassification};
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 = DebertaV2Config::from_file(config_path);
let model = DebertaV2ForSequenceClassification::new(&p.root(), &config).unwrap();

pub fn forward_t( &self, input_ids: Option<&Tensor>, attention_mask: Option<&Tensor>, token_type_ids: Option<&Tensor>, position_ids: Option<&Tensor>, input_embeds: Option<&Tensor>, train: bool, ) -> Result<DebertaV2SequenceClassificationOutput, RustBertError>

Forward pass through the model

Arguments

• input_ids - Optional input tensor of shape (batch size, sequence_length). If None, pre-computed embeddings must be provided (see input_embeds)
• attention_mask - Optional mask of shape (batch size, sequence_length). Masked position have value 0, non-masked value 1. If None set to 1
• 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

• DebertaV2SequenceClassificationOutput containing:
  • logits - Tensor of shape (batch size, num_labels)
  • all_hidden_states - Option<Vec<Tensor>> of length num_hidden_layers with shape (batch size, sequence_length, hidden_size)
  • all_attentions - Option<Vec<Tensor>> of length num_hidden_layers with shape (batch size, sequence_length, hidden_size)

Example

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

let model_output = no_grad(|| {
    model.forward_t(
        Some(&input_tensor),
        Some(&mask),
        Some(&token_type_ids),
        Some(&position_ids),
        None,
        false,
    )
});