pub fn new(generate_config: GenerateConfig) -> Result<BartGenerator, RustBertError> {
        //        The following allow keeping the same GenerationConfig Default for GPT, GPT2 and BART models
        let model_resource = if &generate_config.model_resource
            == &Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2))
        {
            Resource::Remote(RemoteResource::from_pretrained(BartModelResources::BART))
        } else {
            generate_config.model_resource.clone()
        };

        let config_resource = if &generate_config.config_resource
            == &Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2))
        {
            Resource::Remote(RemoteResource::from_pretrained(BartConfigResources::BART))
        } else {
            generate_config.config_resource.clone()
        };

        let vocab_resource = if &generate_config.vocab_resource
            == &Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2))
        {
            Resource::Remote(RemoteResource::from_pretrained(BartVocabResources::BART))
        } else {
            generate_config.vocab_resource.clone()
        };

        let merges_resource = if &generate_config.merges_resource
            == &Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2))
        {
            Resource::Remote(RemoteResource::from_pretrained(BartMergesResources::BART))
        } else {
            generate_config.merges_resource.clone()
        };

        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 = model_resource.get_local_path()?;
        let device = generate_config.device;

        generate_config.validate();
        let mut var_store = nn::VarStore::new(device);
        let tokenizer = RobertaTokenizer::from_file(
            vocab_path.to_str().unwrap(),
            merges_path.to_str().unwrap(),
            false,
            false,
        )?;
        let config = BartConfig::from_file(config_path);
        let model = BartForConditionalGeneration::new(&var_store.root(), &config, true);
        var_store.load(weights_path)?;

        let bos_token_id = Some(0);
        let eos_token_ids = Some(match config.eos_token_id {
            Some(value) => vec![value],
            None => vec![2],
        });
        let pad_token_id = Some(match config.pad_token_id {
            Some(value) => value,
            None => 1,
        });
        let vocab_size = config.vocab_size;
        let is_encoder_decoder = true;
        let decoder_start_id = Some(2);

        Ok(BartGenerator {
            model,
            tokenizer,
            var_store,
            generate_config,
            bos_token_id,
            eos_token_ids,
            pad_token_id,
            is_encoder_decoder,
            vocab_size,
            decoder_start_id,
        })
    }
/// Build a new `BartGenerator`
///
/// # Arguments
///
/// * `vocab_path` - Path to the model vocabulary, expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers) convention
/// * `merges_path` - Path to the bpe merges, expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers) convention
/// * `config_path` - Path to the model configuration, expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers) convention
/// * `weights_path` - Path to the model weight files. These need to be converted form the `.bin` to `.ot` format using the utility script provided.
/// * `device` - Device to run the model on, e.g. `Device::Cpu` or `Device::Cuda(0)`
///
/// # Example
///
/// ```no_run
/// # use std::path::PathBuf;
/// # use tch::Device;
/// # fn main() -> anyhow::Result<()> {
/// use rust_bert::pipelines::generation::{BartGenerator, GenerateConfig};
/// # let mut home: PathBuf = dirs::home_dir().unwrap();
/// # home.push("rustbert");
/// # home.push("openai-gpt");
/// # let config_path = &home.as_path().join("config.json");
/// # let vocab_path = &home.as_path().join("vocab.txt");
/// # let merges_path = &home.as_path().join("merges.txt");
/// # let weights_path = &home.as_path().join("model.ot");
/// let device = Device::cuda_if_available();
/// let generate_config = GenerateConfig {
///     max_length: 30,
///     do_sample: true,
///     num_beams: 5,
///     temperature: 1.1,
///     num_return_sequences: 3,
///     ..Default::default()
/// };
/// let bart_generator = BartGenerator::new(generate_config)?;
/// # Ok(())
/// # }
/// ```
Build a new `BartGenerator`
Build a new ` BartGenerator `
pub fn new(generate_config: GenerateConfig) -> Result<BartGenerator, RustBertError> {
        //        The following allow keeping the same GenerationConfig Default for GPT, GPT2 and BART models
        let model_resource = if &generate_config.model_resource
            == &Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2))
        {
            Resource::Remote(RemoteResource::from_pretrained(BartModelResources::BART))
        } else {
            generate_config.model_resource.clone()
        };
        let config_resource = if &generate_config.config_resource
            == &Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2))
        {
            Resource::Remote(RemoteResource::from_pretrained(BartConfigResources::BART))
        } else {
            generate_config.config_resource.clone()
        };
        let vocab_resource = if &generate_config.vocab_resource
            == &Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2))
        {
            Resource::Remote(RemoteResource::from_pretrained(BartVocabResources::BART))
        } else {
            generate_config.vocab_resource.clone()
        };
        let merges_resource = if &generate_config.merges_resource
            == &Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2))
        {
            Resource::Remote(RemoteResource::from_pretrained(BartMergesResources::BART))
        } else {
            generate_config.merges_resource.clone()
        };
        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 = model_resource.get_local_path()?;
        let device = generate_config.device;
        generate_config.validate();
        let mut var_store = nn::VarStore::new(device);
        let tokenizer = RobertaTokenizer::from_file(
            vocab_path.to_str().unwrap(),
            merges_path.to_str().unwrap(),
            false,
            false,
        )?;
        let config = BartConfig::from_file(config_path);
        let model = BartForConditionalGeneration::new(&var_store.root(), &config, true);
        var_store.load(weights_path)?;
        let bos_token_id = Some(0);
        let eos_token_ids = Some(match config.eos_token_id {
            Some(value) => vec![value],
            None => vec![2],
        });
        let pad_token_id = Some(match config.pad_token_id {
            Some(value) => value,
            None => 1,
        });
        let vocab_size = config.vocab_size;
        let is_encoder_decoder = true;
        let decoder_start_id = Some(2);
        Ok(BartGenerator {
            model,
            tokenizer,
            var_store,
            generate_config,
            bos_token_id,
            eos_token_ids,
            pad_token_id,
            is_encoder_decoder,
            vocab_size,
            decoder_start_id,
        })
    }
pub fn new ( generate_config : GenerateConfig ) -> Result < BartGenerator , RustBertError > { //        The following allow keeping the same GenerationConfig Default for GPT, GPT2 and BART models let model_resource = if & generate_config . model_resource == & Resource :: Remote ( RemoteResource :: from_pretrained ( Gpt2ModelResources :: GPT2 ) ) { Resource :: Remote ( RemoteResource :: from_pretrained ( BartModelResources :: BART ) ) } else { generate_config . model_resource . clone ( ) } ; let config_resource = if & generate_config . config_resource == & Resource :: Remote ( RemoteResource :: from_pretrained ( Gpt2ConfigResources :: GPT2 ) ) { Resource :: Remote ( RemoteResource :: from_pretrained ( BartConfigResources :: BART ) ) } else { generate_config . config_resource . clone ( ) } ; let vocab_resource = if & generate_config . vocab_resource == & Resource :: Remote ( RemoteResource :: from_pretrained ( Gpt2VocabResources :: GPT2 ) ) { Resource :: Remote ( RemoteResource :: from_pretrained ( BartVocabResources :: BART ) ) } else { generate_config . vocab_resource . clone ( ) } ; let merges_resource = if & generate_config . merges_resource == & Resource :: Remote ( RemoteResource :: from_pretrained ( Gpt2MergesResources :: GPT2 ) ) { Resource :: Remote ( RemoteResource :: from_pretrained ( BartMergesResources :: BART ) ) } else { generate_config . merges_resource . clone ( ) } ; 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 = model_resource . get_local_path ( ) ? ; let device = generate_config . device ; generate_config . validate ( ) ; let mut var_store = nn :: VarStore :: new ( device ) ; let tokenizer = RobertaTokenizer :: from_file ( vocab_path . to_str ( ) . unwrap ( ) , merges_path . to_str ( ) . unwrap ( ) , false , false , ) ? ; let config = BartConfig :: from_file ( config_path ) ; let model = BartForConditionalGeneration :: new ( & var_store . root ( ) , & config , true ) ; var_store . load ( weights_path ) ? ; let bos_token_id = Some ( 0 ) ; let eos_token_ids = Some ( match config . eos_token_id { Some ( value ) => vec ! [ value ] , None => vec ! [ 2 ] , } ) ; let pad_token_id = Some ( match config . pad_token_id { Some ( value ) => value , None => 1 , } ) ; let vocab_size = config . vocab_size ; let is_encoder_decoder = true ; let decoder_start_id = Some ( 2 ) ; Ok ( BartGenerator { model , tokenizer , var_store , generate_config , bos_token_id , eos_token_ids , pad_token_id , is_encoder_decoder , vocab_size , decoder_start_id , } ) }
Build a new `BartGenerator`
Build a new ` BartGenerator `
generate_config
GenerateConfig
generate_config
GenerateConfig
example
None
use std::path::pathbuf;
None
use tch::device;
None
fn main() -> anyhow::result<()> {
use rust_bert::pipelines::generation::{BartGenerator, GenerateConfig}.
use rust_bert :: pipelines :: generation :: { BartGenerator GenerateConfig } .
let mut home: pathbuf = dirs::home_dir().unwrap();
None
home.push("rustbert");
None
home.push("openai-gpt");
None
let config_path = &home.as_path().join("config.json");
None
let vocab_path = &home.as_path().join("vocab.txt");
None
let merges_path = &home.as_path().join("merges.txt");
None
let weights_path = &home.as_path().join("model.ot");
let device = Device::cuda_if_available();
let generate_config = GenerateConfig {
max_length: 30,
do_sample: true,
num_beams: 5,
temperature: 1.1,
num_return_sequences: 3,
Default::default()
};
let bart_generator = BartGenerator::new(generate_config)?.
let device = Device :: cuda_if_available () ; let generate_config = GenerateConfig { max_length : 30 do_sample : true num_beams : 5 temperature : 1 . 1 num_return_sequences : 3 Default :: default () } ; let bart_generator = BartGenerator :: new ( generate_config ) ? .
ok(())
None
}
None