mirror of
https://github.com/guillaume-be/rust-bert.git
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* Fixed Clippy warnings
* Revert "Shallow clone optimization (#243)"
This reverts commit ba584653bc
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* 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
63 lines
2.3 KiB
Rust
63 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::longformer::{
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LongformerConfigResources, LongformerMergesResources, LongformerModelResources,
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LongformerVocabResources,
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};
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use rust_bert::pipelines::common::{ModelResource, ModelType};
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use rust_bert::pipelines::question_answering::{
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QaInput, QuestionAnsweringConfig, QuestionAnsweringModel,
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};
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use rust_bert::resources::RemoteResource;
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fn main() -> anyhow::Result<()> {
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// Set-up Question Answering model
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let config = QuestionAnsweringConfig::new(
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ModelType::Longformer,
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ModelResource::Torch(Box::new(RemoteResource::from_pretrained(
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LongformerModelResources::LONGFORMER_BASE_SQUAD1,
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))),
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RemoteResource::from_pretrained(LongformerConfigResources::LONGFORMER_BASE_SQUAD1),
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RemoteResource::from_pretrained(LongformerVocabResources::LONGFORMER_BASE_SQUAD1),
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Some(RemoteResource::from_pretrained(
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LongformerMergesResources::LONGFORMER_BASE_SQUAD1,
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)),
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false,
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None,
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false,
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);
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let qa_model = QuestionAnsweringModel::new(config)?;
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// Define input
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let question_1 = String::from("Where does Amy live ?");
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let context_1 = String::from("Amy lives in Amsterdam");
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let question_2 = String::from("Where does Eric live");
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let context_2 = String::from("While Amy lives in Amsterdam, Eric is in The Hague.");
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let qa_input_1 = QaInput {
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question: question_1,
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context: context_1,
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};
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let qa_input_2 = QaInput {
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question: question_2,
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context: context_2,
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};
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// Get answer
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let answers = qa_model.predict(&[qa_input_1, qa_input_2], 1, 32);
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println!("{answers:?}");
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Ok(())
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}
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