mirror of
https://github.com/guillaume-be/rust-bert.git
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100 lines
3.5 KiB
Rust
100 lines
3.5 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::bert::{
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BertConfig, BertConfigResources, BertForMaskedLM, BertModelResources, BertVocabResources,
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};
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use rust_bert::resources::{RemoteResource, Resource};
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use rust_bert::Config;
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use rust_tokenizers::tokenizer::{BertTokenizer, MultiThreadedTokenizer, TruncationStrategy};
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use rust_tokenizers::vocab::Vocab;
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use tch::{nn, no_grad, Device, Tensor};
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fn main() -> anyhow::Result<()> {
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// Resources paths
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(BertConfigResources::BERT));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(BertVocabResources::BERT));
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let weights_resource =
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Resource::Remote(RemoteResource::from_pretrained(BertModelResources::BERT));
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let config_path = config_resource.get_local_path()?;
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let vocab_path = vocab_resource.get_local_path()?;
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let weights_path = weights_resource.get_local_path()?;
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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 =
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BertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
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let config = BertConfig::from_file(config_path);
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let bert_model = BertForMaskedLM::new(&vs.root(), &config);
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vs.load(weights_path)?;
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// Define input
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let input = [
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"Looks like one [MASK] is missing",
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"It was a very nice and [MASK] day",
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];
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let tokenized_input = tokenizer.encode_list(&input, 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 tokenized_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(tokenized_input.as_slice(), 0).to(device);
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// Forward pass
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let model_output = no_grad(|| {
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bert_model.forward_t(
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Some(&input_tensor),
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None,
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None,
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None,
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None,
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None,
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None,
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false,
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)
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});
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// Print masked tokens
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let index_1 = model_output
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.prediction_scores
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.get(0)
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.get(4)
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.argmax(0, false);
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let index_2 = model_output
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.prediction_scores
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.get(1)
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.get(7)
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.argmax(0, false);
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let word_1 = tokenizer.vocab().id_to_token(&index_1.int64_value(&[]));
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let word_2 = tokenizer.vocab().id_to_token(&index_2.int64_value(&[]));
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println!("{}", word_1); // Outputs "person" : "Looks like one [person] is missing"
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println!("{}", word_2); // Outputs "pear" : "It was a very nice and [pleasant] day"
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Ok(())
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}
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