pub fn new(
        question_answering_config: QuestionAnsweringConfig,
    ) -> Result<QuestionAnsweringModel, RustBertError> {
        let config_path = question_answering_config.config_resource.get_local_path()?;
        let vocab_path = question_answering_config.vocab_resource.get_local_path()?;
        let weights_path = question_answering_config.model_resource.get_local_path()?;
        let merges_path = if let Some(merges_resource) = &question_answering_config.merges_resource
        {
            Some(merges_resource.get_local_path()?)
        } else {
            None
        };
        let device = question_answering_config.device;

        let tokenizer = TokenizerOption::from_file(
            question_answering_config.model_type,
            vocab_path.to_str().unwrap(),
            merges_path.as_deref().map(|path| path.to_str().unwrap()),
            question_answering_config.lower_case,
            question_answering_config.strip_accents,
            question_answering_config.add_prefix_space,
        )?;
        let pad_idx = tokenizer
            .get_pad_id()
            .expect("The Tokenizer used for Question Answering should contain a PAD id");
        let sep_idx = tokenizer
            .get_sep_id()
            .expect("The Tokenizer used for Question Answering should contain a SEP id");
        let mut var_store = VarStore::new(device);
        let mut model_config =
            ConfigOption::from_file(question_answering_config.model_type, config_path);
        match model_config {
            //        The config for the current pre-trained question answering model indicates position embeddings which does not seem accurate
            ConfigOption::DistilBert(ref mut config) => {
                config.sinusoidal_pos_embds = false;
            }
            _ => (),
        };

        let qa_model = QuestionAnsweringOption::new(
            question_answering_config.model_type,
            &var_store.root(),
            &model_config,
        );
        var_store.load(weights_path)?;
        Ok(QuestionAnsweringModel {
            tokenizer,
            pad_idx,
            sep_idx,
            max_seq_len: 384,
            doc_stride: 128,
            max_query_length: 64,
            max_answer_len: 15,
            qa_model,
            var_store,
        })
    }
/// Build a new `QuestionAnsweringModel`
///
/// # Arguments
///
/// * `question_answering_config` - `QuestionAnsweringConfig` object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
///
/// # Example
///
/// ```no_run
/// # fn main() -> anyhow::Result<()> {
/// use rust_bert::pipelines::question_answering::QuestionAnsweringModel;
///
/// let qa_model = QuestionAnsweringModel::new(Default::default())?;
/// # Ok(())
/// # }
/// ```
Build a new `QuestionAnsweringModel`
Build a new ` QuestionAnsweringModel `
pub fn new(
        question_answering_config: QuestionAnsweringConfig,
    ) -> Result<QuestionAnsweringModel, RustBertError> {
        let config_path = question_answering_config.config_resource.get_local_path()?;
        let vocab_path = question_answering_config.vocab_resource.get_local_path()?;
        let weights_path = question_answering_config.model_resource.get_local_path()?;
        let merges_path = if let Some(merges_resource) = &question_answering_config.merges_resource
        {
            Some(merges_resource.get_local_path()?)
        } else {
            None
        };
        let device = question_answering_config.device;
        let tokenizer = TokenizerOption::from_file(
            question_answering_config.model_type,
            vocab_path.to_str().unwrap(),
            merges_path.as_deref().map(|path| path.to_str().unwrap()),
            question_answering_config.lower_case,
            question_answering_config.strip_accents,
            question_answering_config.add_prefix_space,
        )?;
        let pad_idx = tokenizer
            .get_pad_id()
            .expect("The Tokenizer used for Question Answering should contain a PAD id");
        let sep_idx = tokenizer
            .get_sep_id()
            .expect("The Tokenizer used for Question Answering should contain a SEP id");
        let mut var_store = VarStore::new(device);
        let mut model_config =
            ConfigOption::from_file(question_answering_config.model_type, config_path);
        match model_config {
            //        The config for the current pre-trained question answering model indicates position embeddings which does not seem accurate
            ConfigOption::DistilBert(ref mut config) => {
                config.sinusoidal_pos_embds = false;
            }
            _ => (),
        };
        let qa_model = QuestionAnsweringOption::new(
            question_answering_config.model_type,
            &var_store.root(),
            &model_config,
        );
        var_store.load(weights_path)?;
        Ok(QuestionAnsweringModel {
            tokenizer,
            pad_idx,
            sep_idx,
            max_seq_len: 384,
            doc_stride: 128,
            max_query_length: 64,
            max_answer_len: 15,
            qa_model,
            var_store,
        })
    }
pub fn new ( question_answering_config : QuestionAnsweringConfig , ) -> Result < QuestionAnsweringModel , RustBertError > { let config_path = question_answering_config . config_resource . get_local_path ( ) ? ; let vocab_path = question_answering_config . vocab_resource . get_local_path ( ) ? ; let weights_path = question_answering_config . model_resource . get_local_path ( ) ? ; let merges_path = if let Some ( merges_resource ) = & question_answering_config . merges_resource { Some ( merges_resource . get_local_path ( ) ? ) } else { None } ; let device = question_answering_config . device ; let tokenizer = TokenizerOption :: from_file ( question_answering_config . model_type , vocab_path . to_str ( ) . unwrap ( ) , merges_path . as_deref ( ) . map ( | path | path . to_str ( ) . unwrap ( ) ) , question_answering_config . lower_case , question_answering_config . strip_accents , question_answering_config . add_prefix_space , ) ? ; let pad_idx = tokenizer . get_pad_id ( ) . expect ( " " ) ; let sep_idx = tokenizer . get_sep_id ( ) . expect ( " " ) ; let mut var_store = VarStore :: new ( device ) ; let mut model_config = ConfigOption :: from_file ( question_answering_config . model_type , config_path ) ; match model_config { //        The config for the current pre-trained question answering model indicates position embeddings which does not seem accurate ConfigOption :: DistilBert ( ref mut config ) => { config . sinusoidal_pos_embds = false ; } _ => ( ) , } ; let qa_model = QuestionAnsweringOption :: new ( question_answering_config . model_type , & var_store . root ( ) , & model_config , ) ; var_store . load ( weights_path ) ? ; Ok ( QuestionAnsweringModel { tokenizer , pad_idx , sep_idx , max_seq_len : 384 , doc_stride : 128 , max_query_length : 64 , max_answer_len : 15 , qa_model , var_store , } ) }
Build a new `QuestionAnsweringModel`
Build a new ` QuestionAnsweringModel `
question_answering_config
QuestionAnsweringConfig
question_answering_config
QuestionAnsweringConfig
example
None
fn main() -> anyhow::result<()> {
use rust_bert::pipelines::question_answering::QuestionAnsweringModel.

let qa_model = QuestionAnsweringModel::new(Default::default())?.
use rust_bert :: pipelines :: question_answering :: QuestionAnsweringModel . let qa_model = QuestionAnsweringModel :: new ( Default :: default () ) ? .
ok(())
None
}
None