mirror of
https://github.com/guillaume-be/rust-bert.git
synced 2024-07-14 16:00:23 +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
57 lines
2.3 KiB
Rust
57 lines
2.3 KiB
Rust
// Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
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// Copyright 2019 Guillaume Becquin
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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extern crate anyhow;
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use rust_bert::mbart::{
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MBartConfigResources, MBartModelResources, MBartSourceLanguages, MBartTargetLanguages,
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MBartVocabResources,
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};
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use rust_bert::pipelines::common::{ModelResource, ModelType};
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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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use tch::Device;
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fn main() -> anyhow::Result<()> {
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let model_resource = RemoteResource::from_pretrained(MBartModelResources::MBART50_MANY_TO_MANY);
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let config_resource =
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RemoteResource::from_pretrained(MBartConfigResources::MBART50_MANY_TO_MANY);
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let vocab_resource = RemoteResource::from_pretrained(MBartVocabResources::MBART50_MANY_TO_MANY);
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let source_languages = MBartSourceLanguages::MBART50_MANY_TO_MANY;
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let target_languages = MBartTargetLanguages::MBART50_MANY_TO_MANY;
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let translation_config = TranslationConfig::new(
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ModelType::MBart,
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ModelResource::Torch(Box::new(model_resource)),
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config_resource,
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vocab_resource,
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None,
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source_languages,
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target_languages,
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Device::cuda_if_available(),
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);
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let model = TranslationModel::new(translation_config)?;
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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(model.translate(&[source_sentence], Language::English, Language::French)?);
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outputs.extend(model.translate(&[source_sentence], Language::English, Language::Spanish)?);
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outputs.extend(model.translate(&[source_sentence], Language::English, Language::Hindi)?);
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for sentence in outputs {
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println!("{sentence}");
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}
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Ok(())
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}
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