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// Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
// Copyright 2019-2020 Guillaume Becquin
// Copyright 2020 Maarten van Gompel
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//! # Token classification pipeline (Named Entity Recognition, Part-of-Speech tagging)
//! More generic token classification pipeline, works with multiple models (Bert, Roberta)
//!
//! ```no_run
//! use rust_bert::pipelines::token_classification::{TokenClassificationModel,TokenClassificationConfig};
//! use rust_bert::resources::RemoteResource;
//! use rust_bert::bert::{BertModelResources, BertVocabResources, BertConfigResources};
//! use rust_bert::pipelines::common::ModelType;
//! # fn main() -> anyhow::Result<()> {
//!
//! use rust_bert::pipelines::common::ModelResource;
//! //Load a configuration
//! use rust_bert::pipelines::token_classification::LabelAggregationOption;
//! let config = TokenClassificationConfig::new(
//! ModelType::Bert,
//! ModelResource::Torch(Box::new(RemoteResource::from_pretrained(BertModelResources::BERT_NER))),
//! RemoteResource::from_pretrained(BertVocabResources::BERT_NER),
//! RemoteResource::from_pretrained(BertConfigResources::BERT_NER),
//! None, //merges resource only relevant with ModelType::Roberta
//! false, //lowercase
//! None, //strip_accents
//! None, //add_prefix_space
//! LabelAggregationOption::Mode
//! );
//!
//! //Create the model
//! let token_classification_model = TokenClassificationModel::new(config)?;
//!
//! let input = [
//! "My name is Amy. I live in Paris.",
//! "Paris is a city in France."
//! ];
//! let output = token_classification_model.predict(&input, true, true); //ignore_first_label = true (only returns the NER parts, ignoring first label O)
//! # Ok(())
//! # }
//! ```
//! Output: \
//! ```no_run
//! # use rust_bert::pipelines::token_classification::Token;
//! use rust_tokenizers::{Mask, Offset};
//! # let output =
//! [
//! Token {
//! text: String::from("[CLS]"),
//! score: 0.9995001554489136,
//! label: String::from("O"),
//! label_index: 0,
//! sentence: 0,
//! index: 0,
//! word_index: 0,
//! offset: None,
//! mask: Mask::Special,
//! },
//! Token {
//! text: String::from("My"),
//! score: 0.9980450868606567,
//! label: String::from("O"),
//! label_index: 0,
//! sentence: 0,
//! index: 1,
//! word_index: 1,
//! offset: Some(Offset { begin: 0, end: 2 }),
//! mask: Mask::None,
//! },
//! Token {
//! text: String::from("name"),
//! score: 0.9995062351226807,
//! label: String::from("O"),
//! label_index: 0,
//! sentence: 0,
//! index: 2,
//! word_index: 2,
//! offset: Some(Offset { begin: 3, end: 7 }),
//! mask: Mask::None,
//! },
//! Token {
//! text: String::from("is"),
//! score: 0.9997343420982361,
//! label: String::from("O"),
//! label_index: 0,
//! sentence: 0,
//! index: 3,
//! word_index: 3,
//! offset: Some(Offset { begin: 8, end: 10 }),
//! mask: Mask::None,
//! },
//! Token {
//! text: String::from("Amélie"),
//! score: 0.9913727683112525,
//! label: String::from("I-PER"),
//! label_index: 4,
//! sentence: 0,
//! index: 4,
//! word_index: 4,
//! offset: Some(Offset { begin: 11, end: 17 }),
//! mask: Mask::None,
//! }, // ...
//! ]
//! # ;
//! ```
use crate::albert::AlbertForTokenClassification;
use crate::bert::BertForTokenClassification;
use crate::common::error::RustBertError;
use crate::deberta::DebertaForTokenClassification;
use crate::distilbert::DistilBertForTokenClassification;
use crate::electra::ElectraForTokenClassification;
use crate::fnet::FNetForTokenClassification;
use crate::longformer::LongformerForTokenClassification;
use crate::mobilebert::MobileBertForTokenClassification;
use crate::pipelines::common::{
cast_var_store, get_device, ConfigOption, ModelResource, ModelType, TokenizerOption,
};
use crate::resources::ResourceProvider;
use crate::roberta::RobertaForTokenClassification;
use crate::xlnet::XLNetForTokenClassification;
use ordered_float::OrderedFloat;
use rust_tokenizers::{
ConsolidatableTokens, ConsolidatedTokenIterator, Mask, Offset, TokenIdsWithOffsets, TokenTrait,
TokenizedInput,
};
use serde::{Deserialize, Serialize};
use std::cmp::min;
use std::collections::HashMap;
use tch::nn::VarStore;
use tch::{no_grad, Device, Kind, Tensor};
use crate::deberta_v2::DebertaV2ForTokenClassification;
#[cfg(feature = "onnx")]
use crate::pipelines::onnx::{config::ONNXEnvironmentConfig, ONNXEncoder};
#[cfg(feature = "remote")]
use crate::{
bert::{BertConfigResources, BertModelResources, BertVocabResources},
resources::RemoteResource,
};
#[derive(Debug, Clone, Serialize, Deserialize)]
/// # Token generated by a `TokenClassificationModel`
pub struct Token {
/// String representation of the Token
pub text: String,
/// Confidence score
pub score: f64,
/// Token label (e.g. ORG, LOC in case of NER)
pub label: String,
/// Label index
pub label_index: i64,
/// Sentence index
pub sentence: usize,
/// Token position index
pub index: u16,
/// Token word position index
pub word_index: u16,
/// Token offsets
pub offset: Option<Offset>,
/// Token mask
pub mask: Mask,
}
impl TokenTrait for Token {
fn offset(&self) -> Option<Offset> {
self.offset
}
fn mask(&self) -> Mask {
self.mask
}
fn as_str(&self) -> &str {
self.text.as_str()
}
}
impl ConsolidatableTokens<Token> for Vec<Token> {
fn iter_consolidate_tokens(&self) -> ConsolidatedTokenIterator<Token> {
ConsolidatedTokenIterator::new(self)
}
}
#[derive(Debug)]
struct InputFeature {
/// Encoded input ids
input_ids: Vec<i64>,
/// Offsets reference to the original string
offsets: Vec<Option<Offset>>,
/// Token category (mask)
mask: Vec<Mask>,
/// Token type ids (mask)
token_type_ids: Vec<i64>,
/// per-token flag indicating if this feature carries the output label for this token
reference_feature: Vec<bool>,
/// Reference example index (long inputs may be broken into multiple input features)
example_index: usize,
}
type LabelAggregationFunction = Box<fn(&[Token]) -> (i64, String)>;
/// # Enum defining the label aggregation method for sub tokens
/// Defines the behaviour for labels aggregation if the consolidation of sub-tokens is enabled.
pub enum LabelAggregationOption {
/// The label of the first sub token is assigned to the entire token
First,
/// The label of the last sub token is assigned to the entire token
Last,
/// The most frequent sub- token is assigned to the entire token
Mode,
/// The user can provide a function mapping a `&Vec<Token>` to a `(i64, String)` tuple corresponding to the label index, label String to return
Custom(LabelAggregationFunction),
}
/// # Configuration for TokenClassificationModel
/// Contains information regarding the model to load and device to place the model on.
pub struct TokenClassificationConfig {
/// Model type
pub model_type: ModelType,
/// Model weights resource (default: pretrained BERT model on CoNLL)
pub model_resource: ModelResource,
/// Config resource (default: pretrained BERT model on CoNLL)
pub config_resource: Box<dyn ResourceProvider + Send>,
/// Vocab resource (default: pretrained BERT model on CoNLL)
pub vocab_resource: Box<dyn ResourceProvider + Send>,
/// Merges resource (default: pretrained BERT model on CoNLL)
pub merges_resource: Option<Box<dyn ResourceProvider + Send>>,
/// Automatically lower case all input upon tokenization (assumes a lower-cased model)
pub lower_case: bool,
/// Flag indicating if the tokenizer should strip accents (normalization). Only used for BERT / ALBERT models
pub strip_accents: Option<bool>,
/// Flag indicating if the tokenizer should add a white space before each tokenized input (needed for some Roberta models)
pub add_prefix_space: Option<bool>,
/// Device to place the model on (default: CUDA/GPU when available)
pub device: Device,
/// Model weights precision. If not provided, will default to full precision on CPU, or the loaded weights precision otherwise
pub kind: Option<Kind>,
/// Sub-tokens aggregation method (default: `LabelAggregationOption::First`)
pub label_aggregation_function: LabelAggregationOption,
/// Batch size for predictions
pub batch_size: usize,
}
impl TokenClassificationConfig {
/// Instantiate a new token classification configuration of the supplied type.
///
/// # Arguments
///
/// * `model_type` - `ModelType` indicating the model type to load (must match with the actual data to be loaded!)
/// * model - The `ResourceProvider` pointing to the model to load (e.g. model.ot)
/// * config - The `ResourceProvider` pointing to the model configuration to load (e.g. config.json)
/// * vocab - The `ResourceProvider` pointing to the tokenizers' vocabulary to load (e.g. vocab.txt/vocab.json)
/// * vocab - An optional `ResourceProvider` pointing to the tokenizers' merge file to load (e.g. merges.txt), needed only for Roberta.
/// * lower_case - A `bool` indicating whether the tokenizer should lower case all input (in case of a lower-cased model)
pub fn new<RC, RV>(
model_type: ModelType,
model_resource: ModelResource,
config_resource: RC,
vocab_resource: RV,
merges_resource: Option<RV>,
lower_case: bool,
strip_accents: impl Into<Option<bool>>,
add_prefix_space: impl Into<Option<bool>>,
label_aggregation_function: LabelAggregationOption,
) -> TokenClassificationConfig
where
RC: ResourceProvider + Send + 'static,
RV: ResourceProvider + Send + 'static,
{
TokenClassificationConfig {
model_type,
model_resource,
config_resource: Box::new(config_resource),
vocab_resource: Box::new(vocab_resource),
merges_resource: merges_resource.map(|r| Box::new(r) as Box<_>),
lower_case,
strip_accents: strip_accents.into(),
add_prefix_space: add_prefix_space.into(),
device: Device::cuda_if_available(),
kind: None,
label_aggregation_function,
batch_size: 64,
}
}
}
#[cfg(feature = "remote")]
impl Default for TokenClassificationConfig {
/// Provides a default CoNLL-2003 NER model (English)
fn default() -> TokenClassificationConfig {
TokenClassificationConfig::new(
ModelType::Bert,
ModelResource::Torch(Box::new(RemoteResource::from_pretrained(
BertModelResources::BERT_NER,
))),
RemoteResource::from_pretrained(BertConfigResources::BERT_NER),
RemoteResource::from_pretrained(BertVocabResources::BERT_NER),
None,
false,
None,
None,
LabelAggregationOption::First,
)
}
}
#[allow(clippy::large_enum_variant)]
/// # Abstraction that holds one particular token sequence classifier model, for any of the supported models
pub enum TokenClassificationOption {
/// Bert for Token Classification
Bert(BertForTokenClassification),
/// DeBERTa for Token Classification
Deberta(DebertaForTokenClassification),
/// DeBERTa V2 for Token Classification
DebertaV2(DebertaV2ForTokenClassification),
/// DistilBert for Token Classification
DistilBert(DistilBertForTokenClassification),
/// MobileBert for Token Classification
MobileBert(MobileBertForTokenClassification),
/// Roberta for Token Classification
Roberta(RobertaForTokenClassification),
/// XLM Roberta for Token Classification
XLMRoberta(RobertaForTokenClassification),
/// Electra for Token Classification
Electra(ElectraForTokenClassification),
/// Albert for Token Classification
Albert(AlbertForTokenClassification),
/// XLNet for Token Classification
XLNet(XLNetForTokenClassification),
/// Longformer for Token Classification
Longformer(LongformerForTokenClassification),
/// FNet for Token Classification
FNet(FNetForTokenClassification),
/// ONNX model for Token Classification
#[cfg(feature = "onnx")]
ONNX(ONNXEncoder),
}
impl TokenClassificationOption {
/// Instantiate a new sequence classification model of the supplied type.
///
/// # Arguments
///
/// * `TokenClassificationConfig` - Token classification pipeline configuration. The type of model created will be inferred from the
/// `ModelResources` (Torch or ONNX) and `ModelType` (Architecture for Torch models) variants provided and
pub fn new(config: &TokenClassificationConfig) -> Result<Self, RustBertError> {
match config.model_resource {
ModelResource::Torch(_) => Self::new_torch(config),
#[cfg(feature = "onnx")]
ModelResource::ONNX(_) => Self::new_onnx(config),
}
}
fn new_torch(config: &TokenClassificationConfig) -> Result<Self, RustBertError> {
let device = config.device;
let weights_path = config.model_resource.get_torch_local_path()?;
let mut var_store = VarStore::new(device);
let model_config =
&ConfigOption::from_file(config.model_type, config.config_resource.get_local_path()?);
let model_type = config.model_type;
let model = match model_type {
ModelType::Bert => {
if let ConfigOption::Bert(config) = model_config {
Ok(Self::Bert(
BertForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a BertConfig for Bert!".to_string(),
))
}
}
ModelType::Deberta => {
if let ConfigOption::Deberta(config) = model_config {
Ok(Self::Deberta(
DebertaForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a DebertaConfig for DeBERTa!".to_string(),
))
}
}
ModelType::DebertaV2 => {
if let ConfigOption::DebertaV2(config) = model_config {
Ok(Self::DebertaV2(
DebertaV2ForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a DebertaConfig for DeBERTa V2!".to_string(),
))
}
}
ModelType::DistilBert => {
if let ConfigOption::DistilBert(config) = model_config {
Ok(Self::DistilBert(
DistilBertForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a DistilBertConfig for DistilBert!".to_string(),
))
}
}
ModelType::MobileBert => {
if let ConfigOption::MobileBert(config) = model_config {
Ok(Self::MobileBert(
MobileBertForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a MobileBertConfig for MobileBert!".to_string(),
))
}
}
ModelType::Roberta => {
if let ConfigOption::Roberta(config) = model_config {
Ok(Self::Roberta(
RobertaForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a RobertaConfig for Roberta!".to_string(),
))
}
}
ModelType::XLMRoberta => {
if let ConfigOption::Roberta(config) = model_config {
Ok(Self::XLMRoberta(
RobertaForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a RobertaConfig for XLMRoberta!".to_string(),
))
}
}
ModelType::Electra => {
if let ConfigOption::Electra(config) = model_config {
Ok(Self::Electra(
ElectraForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a BertConfig for Roberta!".to_string(),
))
}
}
ModelType::Albert => {
if let ConfigOption::Albert(config) = model_config {
Ok(Self::Albert(
AlbertForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply an AlbertConfig for Albert!".to_string(),
))
}
}
ModelType::XLNet => {
if let ConfigOption::XLNet(config) = model_config {
Ok(Self::XLNet(
XLNetForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply an AlbertConfig for Albert!".to_string(),
))
}
}
ModelType::Longformer => {
if let ConfigOption::Longformer(config) = model_config {
Ok(Self::Longformer(
LongformerForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply a LongformerConfig for Longformer!".to_string(),
))
}
}
ModelType::FNet => {
if let ConfigOption::FNet(config) = model_config {
Ok(Self::FNet(
FNetForTokenClassification::new(var_store.root(), config)?,
))
} else {
Err(RustBertError::InvalidConfigurationError(
"You can only supply an FNetConfig for FNet!".to_string(),
))
}
}
#[cfg(feature = "onnx")]
ModelType::ONNX => Err(RustBertError::InvalidConfigurationError(
"A `ModelType::ONNX` ModelType was provided in the configuration with `ModelResources::TORCH`, these are incompatible".to_string(),
)),
_ => Err(RustBertError::InvalidConfigurationError(format!(
"Token classification not implemented for {model_type:?}!"
))),
}?;
var_store.load(weights_path)?;
cast_var_store(&mut var_store, config.kind, device);
Ok(model)
}
#[cfg(feature = "onnx")]
pub fn new_onnx(config: &TokenClassificationConfig) -> Result<Self, RustBertError> {
let onnx_config = ONNXEnvironmentConfig::from_device(config.device);
let environment = onnx_config.get_environment()?;
let encoder_file = config
.model_resource
.get_onnx_local_paths()?
.encoder_path
.ok_or(RustBertError::InvalidConfigurationError(
"An encoder file must be provided for token classification ONNX models."
.to_string(),
))?;
Ok(Self::ONNX(ONNXEncoder::new(
encoder_file,
&environment,
&onnx_config,
)?))
}
/// Returns the `ModelType` for this TokenClassificationOption
pub fn model_type(&self) -> ModelType {
match *self {
Self::Bert(_) => ModelType::Bert,
Self::Deberta(_) => ModelType::Deberta,
Self::DebertaV2(_) => ModelType::DebertaV2,
Self::Roberta(_) => ModelType::Roberta,
Self::XLMRoberta(_) => ModelType::XLMRoberta,
Self::DistilBert(_) => ModelType::DistilBert,
Self::MobileBert(_) => ModelType::MobileBert,
Self::Electra(_) => ModelType::Electra,
Self::Albert(_) => ModelType::Albert,
Self::XLNet(_) => ModelType::XLNet,
Self::Longformer(_) => ModelType::Longformer,
Self::FNet(_) => ModelType::FNet,
#[cfg(feature = "onnx")]
Self::ONNX(_) => ModelType::ONNX,
}
}
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 {
match *self {
Self::Bert(ref model) => {
model
.forward_t(
input_ids,
mask,
token_type_ids,
position_ids,
input_embeds,
train,
)
.logits
}
Self::Deberta(ref model) => {
model
.forward_t(
input_ids,
mask,
token_type_ids,
position_ids,
input_embeds,
train,
)
.expect("Error in DeBERTa forward_t")
.logits
}
Self::DebertaV2(ref model) => {
model
.forward_t(
input_ids,
mask,
token_type_ids,
position_ids,
input_embeds,
train,
)
.expect("Error in DeBERTa V2 forward_t")
.logits
}
Self::DistilBert(ref model) => {
model
.forward_t(input_ids, mask, input_embeds, train)
.expect("Error in distilbert forward_t")
.logits
}
Self::MobileBert(ref model) => {
model
.forward_t(input_ids, None, None, input_embeds, mask, train)
.expect("Error in mobilebert forward_t")
.logits
}
Self::Roberta(ref model) | Self::XLMRoberta(ref model) => {
model
.forward_t(
input_ids,
mask,
token_type_ids,
position_ids,
input_embeds,
train,
)
.logits
}
Self::Electra(ref model) => {
model
.forward_t(
input_ids,
mask,
token_type_ids,
position_ids,
input_embeds,
train,
)
.logits
}
Self::Albert(ref model) => {
model
.forward_t(
input_ids,
mask,
token_type_ids,
position_ids,
input_embeds,
train,
)
.logits
}
Self::XLNet(ref model) => {
model
.forward_t(
input_ids,
mask,
None,
None,
None,
token_type_ids,
input_embeds,
train,
)
.logits
}
Self::Longformer(ref model) => {
model
.forward_t(
input_ids,
mask,
None,
token_type_ids,
position_ids,
input_embeds,
train,
)
.expect("Error in longformer forward_t")
.logits
}
Self::FNet(ref model) => {
model
.forward_t(input_ids, token_type_ids, position_ids, input_embeds, train)
.expect("Error in fnet forward_t")
.logits
}
#[cfg(feature = "onnx")]
Self::ONNX(ref model) => model
.forward(input_ids, mask, token_type_ids, position_ids, input_embeds)
.expect("Error in ONNX forward pass.")
.logits
.unwrap(),
}
}
}
/// # TokenClassificationModel for Named Entity Recognition or Part-of-Speech tagging
pub struct TokenClassificationModel {
tokenizer: TokenizerOption,
token_sequence_classifier: TokenClassificationOption,
label_mapping: HashMap<i64, String>,
device: Device,
label_aggregation_function: LabelAggregationOption,
max_length: usize,
batch_size: usize,
}
impl TokenClassificationModel {
/// Build a new `TokenClassificationModel`
///
/// # Arguments
///
/// * `config` - `TokenClassificationConfig` object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
///
/// # Example
///
/// ```no_run
/// # fn main() -> anyhow::Result<()> {
/// use rust_bert::pipelines::token_classification::TokenClassificationModel;
///
/// let model = TokenClassificationModel::new(Default::default())?;
/// # Ok(())
/// # }
/// ```
pub fn new(
config: TokenClassificationConfig,
) -> Result<TokenClassificationModel, RustBertError> {
let vocab_path = config.vocab_resource.get_local_path()?;
let merges_path = config
.merges_resource
.as_ref()
.map(|resource| resource.get_local_path())
.transpose()?;
let tokenizer = TokenizerOption::from_file(
config.model_type,
vocab_path.to_str().unwrap(),
merges_path.as_deref().map(|path| path.to_str().unwrap()),
config.lower_case,
config.strip_accents,
config.add_prefix_space,
)?;
Self::new_with_tokenizer(config, tokenizer)
}
/// Build a new `TokenClassificationModel` with a provided tokenizer.
///
/// # Arguments
///
/// * `config` - `TokenClassificationConfig` object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
/// * `tokenizer` - `TokenizerOption` tokenizer to use for token classification
///
/// # Example
///
/// ```no_run
/// # fn main() -> anyhow::Result<()> {
/// use rust_bert::pipelines::common::{ModelType, TokenizerOption};
/// use rust_bert::pipelines::token_classification::TokenClassificationModel;
/// let tokenizer = TokenizerOption::from_file(
/// ModelType::Bert,
/// "path/to/vocab.txt",
/// None,
/// false,
/// None,
/// None,
/// )?;
/// let model = TokenClassificationModel::new_with_tokenizer(Default::default(), tokenizer)?;
/// # Ok(())
/// # }
/// ```
pub fn new_with_tokenizer(
config: TokenClassificationConfig,
tokenizer: TokenizerOption,
) -> Result<TokenClassificationModel, RustBertError> {
let config_path = config.config_resource.get_local_path()?;
let token_sequence_classifier = TokenClassificationOption::new(&config)?;
let label_aggregation_function = config.label_aggregation_function;
let model_config = ConfigOption::from_file(config.model_type, config_path);
let max_length = model_config
.get_max_len()
.map(|v| v as usize)
.unwrap_or(usize::MAX);
let label_mapping = model_config.get_label_mapping().clone();
let batch_size = config.batch_size;
let device = get_device(config.model_resource, config.device);
Ok(TokenClassificationModel {
tokenizer,
token_sequence_classifier,
label_mapping,
device,
label_aggregation_function,
max_length,
batch_size,
})
}
/// Get a reference to the model tokenizer.
pub fn get_tokenizer(&self) -> &TokenizerOption {
&self.tokenizer
}
/// Get a mutable reference to the model tokenizer.
pub fn get_tokenizer_mut(&mut self) -> &mut TokenizerOption {
&mut self.tokenizer
}
fn generate_features<S>(&self, input: S, example_index: usize) -> Vec<InputFeature>
where
S: AsRef<str>,
{
let tokenized_input = self.tokenizer.tokenize_with_offsets(input.as_ref());
let encoded_input = TokenIdsWithOffsets {
ids: self
.tokenizer
.convert_tokens_to_ids(&tokenized_input.tokens),
offsets: tokenized_input.offsets,
reference_offsets: tokenized_input.reference_offsets,
masks: tokenized_input.masks,
};
let sequence_added_tokens = self
.tokenizer
.build_input_with_special_tokens(
TokenIdsWithOffsets {
ids: vec![],
offsets: vec![],
reference_offsets: vec![],
masks: vec![],
},
None,
)
.token_ids
.len();
let max_content_length = self.max_length - sequence_added_tokens;
let doc_stride = self.max_length / 4;
let mut spans: Vec<InputFeature> = vec![];
let mut start_token = 0_usize;
let total_length = encoded_input.ids.len();
while (spans.len() * doc_stride) < encoded_input.ids.len() {
let end_token = min(start_token + max_content_length, total_length);
let sub_encoded_input = TokenIdsWithOffsets {
ids: encoded_input.ids[start_token..end_token].to_vec(),
offsets: encoded_input.offsets[start_token..end_token].to_vec(),
reference_offsets: encoded_input.reference_offsets[start_token..end_token].to_vec(),
masks: encoded_input.masks[start_token..end_token].to_vec(),
};
let encoded_span = self
.tokenizer
.build_input_with_special_tokens(sub_encoded_input, None);
let reference_feature = self.get_reference_feature_flag(
start_token,
end_token,
total_length,
doc_stride,
&encoded_span,
);
let feature = InputFeature {
input_ids: encoded_span.token_ids,
offsets: encoded_span.token_offsets,
mask: encoded_span.mask,
token_type_ids: encoded_span
.segment_ids
.into_iter()
.map(|segment_id| segment_id as i64)
.collect(),
reference_feature,
example_index,
};
spans.push(feature);
if end_token == encoded_input.ids.len() {
break;
}
start_token = end_token - doc_stride;
}
spans
}
fn get_reference_feature_flag(
&self,
start_token: usize,
end_token: usize,
total_length: usize,
doc_stride: usize,
encoded_span: &TokenizedInput,
) -> Vec<bool> {
// set halfway through the doc_stride to be false if the feature is not the first/last
let start_cutoff = if start_token > 0 {
let leading_special_tokens = {
let mut counter = 0;
let mut masks = encoded_span.mask.iter();
while masks.next().unwrap_or(&Mask::None) == &Mask::Special {
counter += 1;
}
counter
};
doc_stride / 2 + leading_special_tokens
} else {
0
};
let end_cutoff = if end_token < total_length {
let trailing_special_tokens = {
let mut counter = 0;
let mut masks = encoded_span.mask.iter().rev();
while masks.next().unwrap_or(&Mask::None) == &Mask::Special {
counter += 1;
}
counter
};
encoded_span.token_ids.len() - doc_stride / 2 - trailing_special_tokens
} else {
encoded_span.token_ids.len()
};
let mut reference_feature = vec![true; encoded_span.token_ids.len()];
reference_feature[..start_cutoff]
.iter_mut()
.for_each(|v| *v = false);
reference_feature[end_cutoff..]
.iter_mut()
.for_each(|v| *v = false);
reference_feature
}
/// Classify tokens in a text sequence
///
/// # Arguments
///
/// * `input` - `&[&str]` Array of texts to extract entities from.
/// * `consolidate_subtokens` - bool flag indicating if subtokens should be consolidated at the token level
/// * `return_special` - bool flag indicating if labels for special tokens should be returned
///
/// # Returns
///
/// * `Vec<Vec<Token>>` containing Tokens with associated labels (for example POS tags) for each input provided
///
/// # Example
///
/// ```no_run
/// # fn main() -> anyhow::Result<()> {
/// # use rust_bert::pipelines::token_classification::TokenClassificationModel;
///
/// let ner_model = TokenClassificationModel::new(Default::default())?;
/// let input = [
/// "My name is Amy. I live in Paris.",
/// "Paris is a city in France.",
/// ];
/// let output = ner_model.predict(&input, true, true);
/// # Ok(())
/// # }
/// ```
pub fn predict<S>(
&self,
input: &[S],
consolidate_sub_tokens: bool,
return_special: bool,
) -> Vec<Vec<Token>>
where
S: AsRef<str>,
{
let mut features: Vec<InputFeature> = input
.iter()
.enumerate()
.flat_map(|(example_index, example)| self.generate_features(example, example_index))
.collect();
let mut example_tokens_map: Vec<Vec<Token>> = vec![Vec::new(); input.len()];
let mut start = 0usize;
let len_features = features.len();
while start < len_features {
let end = start + min(len_features - start, self.batch_size);
no_grad(|| {
let batch_features = &mut features[start..end];
let (input_ids, attention_masks, token_type_ids) =
self.pad_features(batch_features);
let output = self.token_sequence_classifier.forward_t(
Some(&input_ids),
Some(&attention_masks),
Some(&token_type_ids),
None,
None,
false,
);
let score = output.exp()
/ output
.exp()
.sum_dim_intlist([-1].as_slice(), true, Kind::Float);
let label_indices = score.argmax(-1, true);
for sentence_idx in 0..label_indices.size()[0] {
let labels = label_indices.get(sentence_idx);
let feature = &features[sentence_idx as usize];
let sentence_reference_flag = &feature.reference_feature;
let original_chars = input[feature.example_index]
.as_ref()
.chars()
.collect::<Vec<char>>();
let mut word_idx: u16 = 0;
for position_idx in sentence_reference_flag
.iter()
.enumerate()
.filter(|(_, flag)| **flag)
.map(|(pos, _)| pos)
{
let mask = feature.mask[position_idx];
if (mask == Mask::Special) & (!return_special) {
continue;
}
if !(mask == Mask::Continuation) {
word_idx += 1;
}
let token = {
self.decode_token(
&original_chars,
feature,
&input_ids,
&labels,
&score,
sentence_idx,
position_idx as i64,
word_idx,
)
};
example_tokens_map[feature.example_index].push(token);
}
}
});
start = end;
}
let mut tokens = example_tokens_map;
if consolidate_sub_tokens {
self.consolidate_tokens(&mut tokens, &self.label_aggregation_function);
}
tokens
}
fn pad_features(&self, features: &mut [InputFeature]) -> (Tensor, Tensor, Tensor) {
let max_len = features
.iter()
.map(|feature| feature.input_ids.len())
.max()
.unwrap();
let attention_masks = features
.iter()
.map(|feature| &feature.input_ids)
.map(|input| {
let mut attention_mask = Vec::with_capacity(max_len);
attention_mask.resize(input.len(), 1i64);
attention_mask.resize(max_len, 0i64);
attention_mask
})
.map(|input| Tensor::from_slice(&(input)))
.collect::<Vec<_>>();
let padding_index = self
.tokenizer
.get_pad_id()
.expect("Only tokenizers with a padding index can be used for token classification");
for feature in features.iter_mut() {
feature.input_ids.resize(max_len, padding_index);
feature.offsets.resize(max_len, None);
feature
.token_type_ids
.resize(max_len, *feature.token_type_ids.last().unwrap_or(&0));
feature.reference_feature.resize(max_len, false);
}
let padded_input_ids = features
.iter()
.map(|input| Tensor::from_slice(input.input_ids.as_slice()))
.collect::<Vec<_>>();
let padded_token_type_ids = features
.iter()
.map(|input| Tensor::from_slice(input.token_type_ids.as_slice()))
.collect::<Vec<_>>();
let input_ids = Tensor::stack(&padded_input_ids, 0).to(self.device);
let attention_masks = Tensor::stack(&attention_masks, 0).to(self.device);
let token_type_ids = Tensor::stack(&padded_token_type_ids, 0).to(self.device);
(input_ids, attention_masks, token_type_ids)
}
fn decode_token(
&self,
original_sentence_chars: &[char],
sentence_tokens: &InputFeature,
input_tensor: &Tensor,
labels: &Tensor,
score: &Tensor,
sentence_idx: i64,
position_idx: i64,
word_index: u16,
) -> Token {
let label_id = labels.int64_value(&[position_idx]);
let token_id = input_tensor.int64_value(&[sentence_idx, position_idx]);
let offsets = &sentence_tokens.offsets[position_idx as usize];
let text = match offsets {
None => self.tokenizer.decode(&[token_id], false, false),
Some(offsets) => {
let (start_char, end_char) = (offsets.begin as usize, offsets.end as usize);
let end_char = min(end_char, original_sentence_chars.len());
let text = original_sentence_chars[start_char..end_char]
.iter()
.collect();
text
}
};
Token {
text,
score: score.double_value(&[sentence_idx, position_idx, label_id]),
label: self
.label_mapping
.get(&label_id)
.expect("Index out of vocabulary bounds.")
.to_owned(),
label_index: label_id,
sentence: sentence_idx as usize,
index: position_idx as u16,
word_index,
offset: offsets.to_owned(),
mask: sentence_tokens.mask[position_idx as usize],
}
}
fn consolidate_tokens(
&self,
tokens: &mut Vec<Vec<Token>>,
label_aggregation_function: &LabelAggregationOption,
) {
for sequence_tokens in tokens {
let mut tokens_to_replace = vec![];
let token_iter = sequence_tokens.iter_consolidate_tokens();
let mut cursor = 0;
for sub_tokens in token_iter {
if sub_tokens.len() > 1 {
let (label_index, label) =
self.consolidate_labels(sub_tokens, label_aggregation_function);
let sentence = (sub_tokens[0]).sentence;
let index = (sub_tokens[0]).index;
let word_index = (sub_tokens[0]).word_index;
let offset_start = sub_tokens
.first()
.unwrap()
.offset
.as_ref()
.map(|offset| offset.begin);
let offset_end = sub_tokens
.last()
.unwrap()
.offset
.as_ref()
.map(|offset| offset.end);
let offset = if let (Some(offset_start), Some(offset_end)) =
(offset_start, offset_end)
{
Some(Offset::new(offset_start, offset_end))
} else {
None
};
let mut text = String::new();
let mut score = 1f64;
for current_sub_token in sub_tokens.iter() {
text.push_str(current_sub_token.text.as_str());
score *= if current_sub_token.label_index == label_index {
current_sub_token.score
} else {
1.0 - current_sub_token.score
};
}
let token = Token {
text,
score,
label,
label_index,
sentence,
index,
word_index,
offset,
mask: Default::default(),
};
tokens_to_replace.push(((cursor, cursor + sub_tokens.len()), token));
}
cursor += sub_tokens.len();
}
for ((start, end), token) in tokens_to_replace.into_iter().rev() {
sequence_tokens.splice(start..end, [token].iter().cloned());
}
}
}
fn consolidate_labels(
&self,
tokens: &[Token],
aggregation: &LabelAggregationOption,
) -> (i64, String) {
match aggregation {
LabelAggregationOption::First => {
let token = tokens.first().unwrap();
(token.label_index, token.label.clone())
}
LabelAggregationOption::Last => {
let token = tokens.last().unwrap();
(token.label_index, token.label.clone())
}
LabelAggregationOption::Mode => {
let counts = tokens.iter().fold(HashMap::new(), |mut m, c| {
let (ref mut count, ref mut score) = m
.entry((c.label_index, c.label.as_str()))
.or_insert((0, 0.0_f64));
*count += 1;
*score = score.max(c.score);
m
});
counts
.into_iter()
.max_by_key(|&(_, (count, score))| (count, OrderedFloat(score)))
.map(|((label_index, label), _)| (label_index, label.to_owned()))
.unwrap()
}
LabelAggregationOption::Custom(function) => function(tokens),
}
}
}