Struct rust_bert::fnet::FNetForTokenClassification

pub struct FNetForTokenClassification { /* private fields */ }

FNet for token classification (e.g. NER, POS)

Token-level classifier predicting a label for each token provided. Note that because of wordpiece tokenization, the labels predicted are not necessarily aligned with words in the sentence. It is made of the following blocks:

• fnet: Base FNet
• dropout: Dropout layer before the last token-level predictions layer
• classifier: Linear layer for token classification

Implementations

impl FNetForTokenClassification

pub fn new<'p, P>(
    p: P,
    config: &FNetConfig
) -> Result<FNetForTokenClassification, RustBertError>where
    P: Borrow<Path<'p>>,

Build a new FNetForTokenClassification

Arguments

• p - Variable store path for the root of the FNet model
• config - FNetConfig object defining the model architecture

Example

use rust_bert::fnet::{FNetConfig, FNetForTokenClassification};
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 = FNetConfig::from_file(config_path);
let fnet = FNetForTokenClassification::new(&p.root() / "fnet", &config).unwrap();

pub fn forward_t(
    &self,
    input_ids: Option<&Tensor>,
    token_type_ids: Option<&Tensor>,
    position_ids: Option<&Tensor>,
    input_embeddings: Option<&Tensor>,
    train: bool
) -> Result<FNetTokenClassificationOutput, 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)
• 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

• FNetTokenClassificationOutput containing:
  • logits - Tensor of shape (batch size, sequence_length, num_labels) containing the logits for each of the input tokens and classes
  • all_hidden_states - Option<Vec<Tensor>> of length num_hidden_layers with shape (batch size, sequence_length, hidden_size)

Example

use rust_bert::fnet::{FNetConfig, FNetForTokenClassification};
let model = FNetForTokenClassification::new(&vs.root(), &config).unwrap();
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 model_output = no_grad(|| {
    model
        .forward_t(
            Some(&input_tensor),
            Some(&token_type_ids),
            Some(&position_ids),
            None,
            false,
        )
        .unwrap()
});