Struct QuestionAnsweringModel

pub struct QuestionAnsweringModel { /* private fields */ }

QuestionAnsweringModel to perform extractive question answering

Implementations

impl QuestionAnsweringModel

pub fn new( question_answering_config: QuestionAnsweringConfig, ) -> Result<QuestionAnsweringModel, RustBertError>

Build a new QuestionAnsweringModel

Arguments

• question_answering_config - QuestionAnsweringConfig object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)

Example

use rust_bert::pipelines::question_answering::QuestionAnsweringModel;

let qa_model = QuestionAnsweringModel::new(Default::default())?;

pub fn new_with_tokenizer( question_answering_config: QuestionAnsweringConfig, tokenizer: TokenizerOption, ) -> Result<QuestionAnsweringModel, RustBertError>

Build a new QuestionAnsweringModel with a provided tokenizer.

Arguments

• question_answering_config - QuestionAnsweringConfig object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
• tokenizer - TokenizerOption tokenizer to use for question answering.

Example

use rust_bert::pipelines::common::{ModelType, TokenizerOption};
use rust_bert::pipelines::question_answering::QuestionAnsweringModel;
let tokenizer = TokenizerOption::from_file(
    ModelType::Bert,
    "path/to/vocab.txt",
    None,
    false,
    None,
    None,
)?;
let qa_model = QuestionAnsweringModel::new_with_tokenizer(Default::default(), tokenizer)?;

pub fn get_tokenizer(&self) -> &TokenizerOption

Get a reference to the model tokenizer.

pub fn get_tokenizer_mut(&mut self) -> &mut TokenizerOption

Get a mutable reference to the model tokenizer.

pub fn predict( &self, qa_inputs: &[QaInput], top_k: i64, batch_size: usize, ) -> Vec<Vec<Answer>>

Perform extractive question answering given a list of QaInputs

Arguments

• qa_inputs - &[QaInput] Array of Question Answering inputs (context and question pairs)
• top_k - return the top-k answers for each QaInput. Set to 1 to return only the best answer.
• batch_size - maximum batch size for the model forward pass.

Returns

• Vec<Vec<Answer>> Vector (same length as qa_inputs) of vectors (each of length top_k) containing the extracted answers.

Example

use rust_bert::pipelines::question_answering::{QaInput, QuestionAnsweringModel};

let qa_model = QuestionAnsweringModel::new(Default::default())?;

let question_1 = String::from("Where does Amy live ?");
let context_1 = String::from("Amy lives in Amsterdam");
let question_2 = String::from("Where does Eric live");
let context_2 = String::from("While Amy lives in Amsterdam, Eric is in The Hague.");

let qa_input_1 = QaInput {
    question: question_1,
    context: context_1,
};
let qa_input_2 = QaInput {
    question: question_2,
    context: context_2,
};
let answers = qa_model.predict(&[qa_input_1, qa_input_2], 1, 32);