1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72

//! # BART (Lewis et al.)

//!

//! Implementation of the BART language model ([BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) Lewis, Liu, Goyal, Ghazvininejad, Mohamed, Levy, Stoyanov, Zettlemoyer, 2019).

//! The base model is implemented in the `bart::BartModel` struct. The model also includes a language model head: `bart::BartForConditionalGeneration`

//! implementing the common `generation::LMHeadModel` trait shared between the models used for generation (see `pipelines` for more information).

//!

//! # Model set-up and pre-trained weights loading

//!

//! A full working example is provided in `examples/bart`, run with `cargo run --example bart`.

//! Alternatively, the summarization capabilities are illustrated in `examples/summarization.rs`, run with `cargo run --example summarization`.

//! 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.txt` vocabulary and `merges.txt` 2-gram merges

//! Pretrained models are available and can be downloaded using RemoteResources.

//!

//! ```no_run

//! # fn main() -> anyhow::Result<()> {

//! #

//! use tch::{nn, Device};

//! # use std::path::PathBuf;

//! use rust_bert::bart::{BartConfig, BartModel};

//! use rust_bert::resources::{LocalResource, Resource};

//! use rust_bert::Config;

//! use rust_tokenizers::tokenizer::RobertaTokenizer;

//!

//! 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 merges_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 = config_resource.get_local_path()?;

//! let vocab_path = vocab_resource.get_local_path()?;

//! let merges_path = merges_resource.get_local_path()?;

//! let weights_path = weights_resource.get_local_path()?;

//!

//! let device = Device::cuda_if_available();

//! let mut vs = nn::VarStore::new(device);

//! let tokenizer: RobertaTokenizer = RobertaTokenizer::from_file(

//!     vocab_path.to_str().unwrap(),

//!     merges_path.to_str().unwrap(),

//!     true,

//!     false,

//! )?;

//! let config = BartConfig::from_file(config_path);

//! let bart_model = BartModel::new(&vs.root(), &config, false);

//! vs.load(weights_path)?;

//!

//! # Ok(())

//! # }

//! ```


mod attention;
mod bart_model;
mod decoder;
mod embeddings;
mod encoder;

pub use attention::LayerState;
pub use bart_model::{
    BartConfig, BartConfigResources, BartForConditionalGeneration, BartForSequenceClassification,
    BartMergesResources, BartModel, BartModelOutput, BartModelResources, BartVocabResources,
};

pub(crate) use encoder::BartEncoderOutput;