[−][src]Struct rust_bert::bert::BertForSequenceClassification
BERT for sequence classification
Base BERT model with a classifier head to perform sentence or document-level classification It is made of the following blocks:
bert
: Base BertModelclassifier
: BERT linear layer for classification
Methods
impl BertForSequenceClassification
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pub fn new(p: &Path, config: &BertConfig) -> BertForSequenceClassification
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Build a new BertForSequenceClassification
Arguments
p
- Variable store path for the root of the BertForSequenceClassification modelconfig
-BertConfig
object defining the model architecture and number of classes
Example
use rust_bert::bert::{BertConfig, BertForSequenceClassification}; 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 = BertForSequenceClassification::new(&(&p.root() / "bert"), &config);
pub fn forward_t(
&self,
input_ids: Option<Tensor>,
mask: Option<Tensor>,
token_type_ids: Option<Tensor>,
position_ids: Option<Tensor>,
input_embeds: Option<Tensor>,
train: bool
) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Tensor>>)
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&self,
input_ids: Option<Tensor>,
mask: Option<Tensor>,
token_type_ids: Option<Tensor>,
position_ids: Option<Tensor>,
input_embeds: Option<Tensor>,
train: bool
) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Tensor>>)
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 (seeinput_embeds
)mask
- Optional mask of shape (batch size, sequence_length). Masked position have value 0, non-masked value 1. If None set to 1token_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 (seeinput_ids
)train
- boolean flag to turn on/off the dropout layers in the model. Should be set to false for inference.
Returns
labels
-Tensor
of shape (batch size, num_labels)hidden_states
-Option<Vec<Tensor>>
of length num_hidden_layers with shape (batch size, sequence_length, hidden_size)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], (Int64, device)); let mask = Tensor::zeros(&[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 (labels, all_hidden_states, all_attentions) = no_grad(|| { bert_model .forward_t(Some(input_tensor), Some(mask), Some(token_type_ids), Some(position_ids), None, false) });
Auto Trait Implementations
impl !RefUnwindSafe for BertForSequenceClassification
impl !Send for BertForSequenceClassification
impl !Sync for BertForSequenceClassification
impl Unpin for BertForSequenceClassification
impl !UnwindSafe for BertForSequenceClassification
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,