[−][src]Module rust_bert::openai_gpt
GPT (Radford et al.)
Implementation of the GPT2 language model (Improving Language Understanding by Generative Pre-Training Radford, Narasimhan, Salimans, Sutskever 2018).
The base model is implemented in the openai_gpt::OpenAiGptModel
struct. The model also includes a language model head: openai_gpt::OpenAIGPTLMHeadModel
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/openai_gpt.rs
, run with cargo run --example openai_gpt
.
All models expect the following resources:
- Configuration file expected to have a structure following the Transformers library
- Model weights are expected to have a structure and parameter names following the Transformers library. A conversion using the Python utility scripts is required to convert the
.bin
weights to the.ot
format. GptTokenizer
using avocab.txt
vocabulary andmerges.txt
2-gram merges Pretrained models are available and can be downloaded using RemoteResources.
use rust_tokenizers::OpenAiGptTokenizer; use tch::{nn, Device}; use rust_bert::Config; use rust_bert::gpt2::Gpt2Config; use rust_bert::openai_gpt::OpenAiGptModel; use rust_bert::resources::{Resource, download_resource, 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 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 = download_resource(&config_resource)?; let vocab_path = download_resource(&vocab_resource)?; let merges_path = download_resource(&merges_resource)?; let weights_path = download_resource(&weights_resource)?; let device = Device::cuda_if_available(); let mut vs = nn::VarStore::new(device); let tokenizer: OpenAiGptTokenizer = OpenAiGptTokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), true); let config = Gpt2Config::from_file(config_path); let gpt_model = OpenAiGptModel::new(&vs.root(), &config); vs.load(weights_path)?;
Structs
OpenAIGPTLMHeadModel | GPT Language Modeling head |
OpenAiGptConfigResources | GPT Pretrained model config files |
OpenAiGptMergesResources | GPT Pretrained model merges files |
OpenAiGptModel | GPT Base model |
OpenAiGptModelResources | GPT Pretrained model weight files |
OpenAiGptVocabResources | GPT Pretrained model vocab files |