mirror of
https://github.com/guillaume-be/rust-bert.git
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89 lines
3.4 KiB
Rust
89 lines
3.4 KiB
Rust
// Copyright 2020 The Google Research Authors.
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// Copyright 2019-present, the HuggingFace Inc. team
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// Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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use rust_bert::electra::{
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ElectraConfig, ElectraConfigResources, ElectraDiscriminator, ElectraModelResources,
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ElectraVocabResources,
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};
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use rust_bert::resources::{download_resource, RemoteResource, Resource};
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use rust_bert::Config;
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use rust_tokenizers::{BertTokenizer, Tokenizer, TruncationStrategy};
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use tch::{nn, no_grad, Device, Tensor};
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fn main() -> failure::Fallible<()> {
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// Resources paths
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let config_resource = Resource::Remote(RemoteResource::from_pretrained(
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ElectraConfigResources::BASE_DISCRIMINATOR,
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));
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let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(
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ElectraVocabResources::BASE_DISCRIMINATOR,
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));
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let weights_resource = Resource::Remote(RemoteResource::from_pretrained(
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ElectraModelResources::BASE_DISCRIMINATOR,
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));
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let config_path = download_resource(&config_resource)?;
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let vocab_path = download_resource(&vocab_resource)?;
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let weights_path = download_resource(&weights_resource)?;
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// Set-up masked LM model
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let device = Device::Cpu;
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let mut vs = nn::VarStore::new(device);
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let tokenizer: BertTokenizer = BertTokenizer::from_file(vocab_path.to_str().unwrap(), true);
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let config = ElectraConfig::from_file(config_path);
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let electra_model = ElectraDiscriminator::new(&vs.root(), &config);
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vs.load(weights_path)?;
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// Define input
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let input = ["One Two Three Ten Five Six Seven Eight"];
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let tokenized_input =
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tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
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let max_len = tokenized_input
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.iter()
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.map(|input| input.token_ids.len())
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.max()
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.unwrap();
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let encoded_input = tokenized_input
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.iter()
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.map(|input| input.token_ids.clone())
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.map(|mut input| {
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input.extend(vec![0; max_len - input.len()]);
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input
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})
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.map(|input| Tensor::of_slice(&(input)))
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.collect::<Vec<_>>();
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let input_tensor = Tensor::stack(encoded_input.as_slice(), 0).to(device);
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// Forward pass
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let (output, _, _) =
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no_grad(|| electra_model.forward_t(Some(input_tensor), None, None, None, None, false));
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// Print model predictions
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for (position, token) in tokenized_input[0].token_ids.iter().enumerate() {
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let probability = output.double_value(&[position as i64]);
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let generated = if probability > 0.5 {
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"generated"
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} else {
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"original"
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};
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println!(
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"{:?}: {} ({:.1}%)",
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tokenizer.decode([*token].to_vec(), false, false),
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generated,
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100f64 * probability
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)
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}
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Ok(())
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}
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