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//! # Ready-to-use NLP pipelines and Transformer-based models //! //! Rust native Transformer-based models implementation. Port of the [Transformers](https://github.com/huggingface/transformers) library, using the tch-rs crate and pre-processing from rust-tokenizers. //! Supports multithreaded tokenization and GPU inference. This repository exposes the model base architecture, task-specific heads (see below) and ready-to-use pipelines. //! //! # Quick Start //! //! This crate can be used in two different ways: //! - Ready-to-use NLP pipelines for: //! - Translation //! - Summarization //! - Multi-turn dialogue //! - Zero-shot classification //! - Sentiment Analysis //! - Named Entity Recognition //! - Question-Answering //! - Language Generation. //! //! More information on these can be found in the [`pipelines` module](./pipelines/index.html) //! ```no_run //! use rust_bert::pipelines::question_answering::{QaInput, QuestionAnsweringModel}; //! //! # fn main() -> anyhow::Result<()> { //! let qa_model = QuestionAnsweringModel::new(Default::default())?; //! //! let question = String::from("Where does Amy live ?"); //! let context = String::from("Amy lives in Amsterdam"); //! let answers = qa_model.predict(&vec![QaInput { question, context }], 1, 32); //! # Ok(()) //! # } //! ``` //! - Transformer models base architectures with customized heads. These allow to load pre-trained models for customized inference in Rust //! //! | |**DistilBERT**|**BERT**|**RoBERTa**|**GPT**|**GPT2**|**BART**|**Electra**|**Marian**|**ALBERT**|**T5**|**XLNet** //! :-----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----: //! Masked LM|✅ |✅ |✅ | | | |✅| |✅ | |✅| //! Sequence classification|✅ |✅ |✅| | |✅| | |✅ | |✅| //! Token classification|✅ |✅ | ✅| | | |✅| |✅ | |✅| //! Question answering|✅ |✅ |✅| | | | | |✅ | |✅| //! Multiple choices| |✅ |✅| | | | | |✅ | |✅| //! Next token prediction| | | |✅|✅| | | | | |✅| //! Natural Language Generation| | | |✅|✅| | | | | |✅| //! Summarization| | | | | |✅| | | | | | //! Translation| | | | | | | |✅| |✅| | //! //! # Loading pre-trained models //! //! A number of pretrained model configuration, weights and vocabulary are downloaded directly from [Huggingface's model repository](https://huggingface.co/models). //! The list of models available with Rust-compatible weights is available in the example ./examples/download_all_dependencies.rs. Additional models can be added if of interest, please raise an issue. //! //! In order to load custom weights to the library, these need to be converter to a binary format that can be read by Libtorch (the original `.bin` files are pickles and cannot be used directly). //! Several Python scripts to load Pytorch weights and convert them to the appropriate format are provided and can be adapted based on the model needs. //! //! The procedure for building custom weights or re-building pretrained weights is as follows: //! 1. Compile the package: cargo build --release //! 2. Download the model files & perform necessary conversions //! - Set-up a virtual environment and install dependencies //! - run the conversion script python /utils/download-dependencies_{MODEL_TO_DOWNLOAD}.py. The dependencies will be downloaded to the user's home directory, under ~/rustbert/{} //! 3. Run the example cargo run --release pub mod albert; pub mod bart; pub mod bert; mod common; pub mod distilbert; pub mod electra; pub mod gpt2; pub mod marian; pub mod openai_gpt; pub mod pipelines; pub mod roberta; pub mod t5; pub mod xlnet; pub use common::error::RustBertError; pub use common::resources; pub use common::Config;