mirror of
https://github.com/guillaume-be/rust-bert.git
synced 2024-10-26 14:07:25 +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
51 lines
1.9 KiB
Rust
51 lines
1.9 KiB
Rust
// Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
|
|
// Copyright 2019 Guillaume Becquin
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
use rust_bert::bert::{BertConfigResources, BertModelResources, BertVocabResources};
|
|
use rust_bert::pipelines::common::{ModelResource, ModelType};
|
|
use rust_bert::pipelines::ner::NERModel;
|
|
use rust_bert::pipelines::token_classification::{
|
|
LabelAggregationOption, TokenClassificationConfig,
|
|
};
|
|
use rust_bert::resources::RemoteResource;
|
|
|
|
fn main() -> anyhow::Result<()> {
|
|
// Load a configuration
|
|
let config = TokenClassificationConfig::new(
|
|
ModelType::Bert,
|
|
ModelResource::Torch(Box::new(RemoteResource::from_pretrained(
|
|
BertModelResources::BERT_NER,
|
|
))),
|
|
RemoteResource::from_pretrained(BertConfigResources::BERT_NER),
|
|
RemoteResource::from_pretrained(BertVocabResources::BERT_NER),
|
|
None, //merges resource only relevant with ModelType::Roberta
|
|
false, //lowercase
|
|
false,
|
|
None,
|
|
LabelAggregationOption::Mode,
|
|
);
|
|
|
|
// Create the model
|
|
let token_classification_model = NERModel::new(config)?;
|
|
let input = [
|
|
"My name is Amélie. I live in Москва.",
|
|
"Chongqing is a city in China.",
|
|
];
|
|
let token_outputs = token_classification_model.predict(&input);
|
|
|
|
for token in token_outputs {
|
|
println!("{token:?}");
|
|
}
|
|
|
|
Ok(())
|
|
}
|