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//! # DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (Sanh et al.)
//!
//! Implementation of the DistilBERT language model ([https://arxiv.org/abs/1910.01108](https://arxiv.org/abs/1910.01108) Sanh, Debut, Chaumond, Wolf, 2019).
//! The base model is implemented in the `distilbert::DistilBertModel` struct. Several language model heads have also been implemented, including:
//! - Masked language model: `distilbert::DistilBertForMaskedLM`
//! - Question answering: `distilbert::DistilBertForQuestionAnswering`
//! - Sequence classification: `distilbert::DistilBertForSequenceClassification`
//! - Token classification (e.g. NER, POS tagging): `distilbert::DistilBertForTokenClassification`
//!
//! # Model set-up and pre-trained weights loading
//!
//! A full working example is provided in `examples/distilbert_masked_lm.rs`, run with `cargo run --example distilbert_masked_lm`.
//! The example below illustrate a DistilBERT Masked language model example, the structure is similar for other models.
//! 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.
//! - `BertTokenizer` using a `vocab.txt` vocabulary
//! Pretrained models are available and can be downloaded using RemoteResources.
//!
//! ```no_run
//!# fn main() -> failure::Fallible<()> {
//!#
//! use rust_tokenizers::BertTokenizer;
//! use tch::{nn, Device};
//!# use std::path::PathBuf;
//! use rust_bert::Config;
//! use rust_bert::distilbert::{DistilBertModelMaskedLM, DistilBertConfig, DistilBertConfigResources, DistilBertVocabResources, DistilBertModelResources};
//! use rust_bert::resources::{Resource, download_resource, RemoteResource, LocalResource};
//!
//! let config_resource = Resource::Local(LocalResource { local_path: PathBuf::from("path/to/config.json")});
//! let vocab_resource = Resource::Local(LocalResource { local_path: PathBuf::from("path/to/vocab.txt")});
//! let weights_resource = Resource::Local(LocalResource { local_path: PathBuf::from("path/to/model.ot")});
//! let config_path = download_resource(&config_resource)?;
//! let vocab_path = download_resource(&vocab_resource)?;
//! let weights_path = download_resource(&weights_resource)?;
//! let device = Device::cuda_if_available();
//! let mut vs = nn::VarStore::new(device);
//! let tokenizer: BertTokenizer = BertTokenizer::from_file(vocab_path.to_str().unwrap(), true);
//! let config = DistilBertConfig::from_file(config_path);
//! let bert_model = DistilBertModelMaskedLM::new(&vs.root(), &config);
//! vs.load(weights_path)?;
//!
//!# Ok(())
//!# }
//! ```



mod distilbert;
mod embeddings;
mod attention;
mod transformer;

pub use distilbert::{DistilBertModelResources, DistilBertConfigResources, DistilBertVocabResources,
                     DistilBertConfig, Activation, DistilBertModel, DistilBertForQuestionAnswering, DistilBertForTokenClassification,
                     DistilBertModelMaskedLM, DistilBertModelClassifier};