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//! # Longformer: The Long-Document Transformer (Betalgy et al.)
//!
//! Implementation of the Longformer language model ([Longformer: The Long-Document Transformer](https://arxiv.org/abs/2001.04063) Betalgy, Peters, Cohan, 2020).
//! The base model is implemented in the `longformer_model::LongformerModel` struct. Several language model heads have also been implemented, including:
//! - Masked language model: `longformer_model::LongformerForMaskedLM`
//! - Multiple choices: `longformer_model:LongformerForMultipleChoice`
//! - Question answering: `longformer_model::LongformerForQuestionAnswering`
//! - Sequence classification: `longformer_model::LongformerForSequenceClassification`
//! - Token classification (e.g. NER, POS tagging): `longformer_model::LongformerForTokenClassification`
//!
//! # Model set-up and pre-trained weights loading
//!
//! A full working example (question answering) is provided in `examples/question_answering_longformer`, run with `cargo run --example question_answering_longformer`.
//! All models expect the following resources:
//! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)
//! - Model weights are expected to have a structure and parameter names following the [Transformers library](https://github.com/huggingface/transformers). A conversion using the Python utility scripts is required to convert the `.bin` weights to the `.ot` format.
//! - `RobertaTokenizer` using a `vocab.json` vocabulary and `merges.txt` byte pair encoding merges
//!
//! # Question answering example below:
//!
//! ```no_run
//! use rust_bert::longformer::{
//! LongformerConfigResources, LongformerMergesResources, LongformerModelResources,
//! LongformerVocabResources,
//! };
//! use rust_bert::pipelines::common::ModelType;
//! use rust_bert::pipelines::question_answering::{
//! QaInput, QuestionAnsweringConfig, QuestionAnsweringModel,
//! };
//! use rust_bert::resources::{RemoteResource};
//!
//! fn main() -> anyhow::Result<()> {
//! // Set-up Question Answering model
//! use rust_bert::pipelines::common::ModelResource;
//! let config = QuestionAnsweringConfig::new(
//! ModelType::Longformer,
//! ModelResource::Torch(Box::new(RemoteResource::from_pretrained(
//! LongformerModelResources::LONGFORMER_BASE_SQUAD1,
//! ))),
//! RemoteResource::from_pretrained(
//! LongformerConfigResources::LONGFORMER_BASE_SQUAD1,
//! ),
//! RemoteResource::from_pretrained(
//! LongformerVocabResources::LONGFORMER_BASE_SQUAD1,
//! ),
//! Some(RemoteResource::from_pretrained(
//! LongformerMergesResources::LONGFORMER_BASE_SQUAD1,
//! )),
//! false,
//! None,
//! false,
//! );
//!
//! let qa_model = QuestionAnsweringModel::new(config)?;
//!
//! // Define input
//! 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,
//! };
//!
//! // Get answer
//! let answers = qa_model.predict(&[qa_input_1, qa_input_2], 1, 32);
//! println!("{:?}", answers);
//! Ok(())
//! }
//! ```
pub use ;