mirror of
https://github.com/guillaume-be/rust-bert.git
synced 2024-09-11 12:55:34 +03:00
540c9268e7
* Fixed Clippy warnings
* Revert "Shallow clone optimization (#243)"
This reverts commit ba584653bc.
* updated dependencies
* tryouts
* GPT2 tryouts
* WIP GPT2
* input mapping
* Cache storage
* Initial GPT2 prototype
* Initial ONNX Config and decoder implementation
* ONNXDecoder first draft
* Use Decoders in example
* Automated tch-ort conversion, decoder implementation
* ONNXCausalDecoder implementation
* Refactored _get_var_store to be optional, added get_device to gen trait
* updated example
* Added decoder_start_token_id to ConfigOption
* Addition of ONNXModelConfig, make max_position_embeddigs optional
* Addition of forward pass function for ONNXModel
* Working ONNX causal decoder
* Simplify tensor conversion
* refactor translation to facilitate ONNX integration
* Implementation of ONNXEncoder
* Implementation of ONNXConditionalGenerator
* working ONNXCausalGenerator
* - Reworked model resources type for pipelines and generators
* Aligned ONNXConditionalGenerator with other generators to use GenerateConfig for creation
* Moved force_token_id_generation to common utils function, fixed tests, Translation implementation
* generalized forced_bos and forced_eos tokens generation
* Aligned the `encode_prompt_text` method across language models
* Fix prompt encoding for causal generation
* Fix prompt encoding for causal generation
* Support for ONNX models for SequenceClassification
* Support for ONNX models for TokenClassification
* Support for ONNX models for POS and NER pipelines
* Support for ONNX models for ZeroShotClassification pipeline
* Support for ONNX models for QuestionAnswering pipeline
* Support for ONNX models for MaskedLM pipeline
* Added token_type_ids , updated layer cache i/o parsing for ONNX pipelines
* Support for ONNX models for TextGenerationPipeline, updated examples for remote resources
* Remove ONNX zero-shot classification example (lack of correct pretrained model)
* Addition of tests for ONNX pipelines support
* Made onnx feature optional
* Fix device lookup with onnx feature enabled
* Updates from main branch
* Flexible tokenizer creation for M2M100 (NLLB support), make NLLB test optional du to their size
* Fixed Clippy warnings
* Addition of documentation for ONNX
* Added documentation for ONNX support
* upcoming tch 1.12 fixes
* Fix merge conflicts
* Fix merge conflicts (2)
* Add download libtorch feature to ONNX tests
* Add download-onnx feature
* attempt to enable onnx download
* add remote resources feature
* onnx download
* pin ort version
* Update ort version
64 lines
2.3 KiB
Rust
64 lines
2.3 KiB
Rust
use rust_bert::m2m_100::{M2M100SourceLanguages, M2M100TargetLanguages};
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use tch::Device;
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use rust_bert::pipelines::common::{ModelResource, ModelType, ONNXModelResources};
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use rust_bert::pipelines::translation::{Language, TranslationConfig, TranslationModel};
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use rust_bert::resources::RemoteResource;
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fn main() -> anyhow::Result<()> {
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let translation_model = TranslationModel::new(TranslationConfig::new(
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ModelType::M2M100,
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ModelResource::ONNX(ONNXModelResources {
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encoder_resource: Some(Box::new(RemoteResource::new(
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"https://huggingface.co/optimum/m2m100_418M/resolve/main/encoder_model.onnx",
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"onnx-m2m100_418M",
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))),
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decoder_resource: Some(Box::new(RemoteResource::new(
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"https://huggingface.co/optimum/m2m100_418M/resolve/main/decoder_model.onnx",
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"onnx-m2m100_418M",
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))),
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decoder_with_past_resource: Some(Box::new(RemoteResource::new(
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"https://huggingface.co/optimum/m2m100_418M/resolve/main/decoder_with_past_model.onnx",
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"onnx-m2m100_418M",
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))),
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}),
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RemoteResource::new(
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"https://huggingface.co/optimum/m2m100_418M/resolve/main/config.json",
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"onnx-m2m100_418M",
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),
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RemoteResource::new(
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"https://huggingface.co/optimum/m2m100_418M/resolve/main/vocab.json",
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"onnx-m2m100_418M",
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),
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Some(RemoteResource::new(
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"https://huggingface.co/optimum/m2m100_418M/resolve/main/sentencepiece.bpe.model",
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"onnx-m2m100_418M",
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)),
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M2M100SourceLanguages::M2M100_418M,
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M2M100TargetLanguages::M2M100_418M,
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Device::cuda_if_available(),
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))?;
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let source_sentence = "This sentence will be translated in multiple languages.";
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let mut outputs = Vec::new();
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outputs.extend(translation_model.translate(
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&[source_sentence],
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Language::English,
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Language::French,
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)?);
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outputs.extend(translation_model.translate(
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&[source_sentence],
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Language::English,
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Language::Spanish,
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)?);
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outputs.extend(translation_model.translate(
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&[source_sentence],
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Language::English,
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Language::Hindi,
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)?);
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println!("{:?}", outputs);
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Ok(())
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}
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