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https://github.com/guillaume-be/rust-bert.git
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Addition of FNet tests
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1
.github/workflows/continuous-integration.yml
vendored
1
.github/workflows/continuous-integration.yml
vendored
@ -71,6 +71,7 @@ jobs:
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--test electra
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--test gpt2
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--test marian
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--test fnet
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test-batch-1:
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name: Integration tests (batch 1)
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@ -1,3 +1,55 @@
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//! # FNet, Mixing Tokens with Fourier Transforms (Lee-Thorp et al.)
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//!
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//! Implementation of the FNet language model ([https://arxiv.org/abs/2105.03824](https://arxiv.org/abs/2105.03824) Lee-Thorp, Ainslie, Eckstein, Ontanon, 2021).
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//! The base model is implemented in the `fnet_model::FNetModel` struct. Several language model heads have also been implemented, including:
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//! - Masked language model: `fnet_model::FNetForMaskedLM`
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//! - Question answering: `fnet_model::FNetForQuestionAnswering`
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//! - Sequence classification: `fnet_model::FNetForSequenceClassification`
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//! - Token classification (e.g. NER, POS tagging): `fnet_model::FNetForTokenClassification`
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//!
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//! # Model set-up and pre-trained weights loading
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//!
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//! The example below illustrate a FNet Masked language model example, the structure is similar for other models.
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//! All models expect the following resources:
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//! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)
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//! - Model weights are expected to have a structure and parameter names following the [Transformers library](https://github.com/huggingface/transformers). A conversion using the Python utility scripts is required to convert the `.bin` weights to the `.ot` format.
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//! - `FNetTokenizer` using a `spiece.model` SentencePiece (BPE) model file
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//! Pretrained models are available and can be downloaded using RemoteResources.
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//!
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//! ```no_run
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//! # fn main() -> anyhow::Result<()> {
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//! #
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//! use tch::{nn, Device};
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//! # use std::path::PathBuf;
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//! use rust_bert::resources::{LocalResource, RemoteResource, Resource};
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//! use rust_bert::fnet::{FNetConfig, FNetForMaskedLM};
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//! use rust_bert::Config;
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//! use rust_tokenizers::tokenizer::{BertTokenizer, FNetTokenizer};
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//!
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//! let config_resource = Resource::Local(LocalResource {
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//! local_path: PathBuf::from("path/to/config.json"),
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//! });
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//! let vocab_resource = Resource::Local(LocalResource {
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//! local_path: PathBuf::from("path/to/spiece.model"),
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//! });
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//! let weights_resource = Resource::Local(LocalResource {
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//! local_path: PathBuf::from("path/to/model.ot"),
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//! });
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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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//! let device = Device::cuda_if_available();
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//! let mut vs = nn::VarStore::new(device);
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//! let tokenizer: FNetTokenizer =
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//! FNetTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
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//! let config = FNetConfig::from_file(config_path);
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//! let bert_model = FNetForMaskedLM::new(&vs.root(), &config);
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//! vs.load(weights_path)?;
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//!
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//! # Ok(())
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//! # }
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//! ```
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mod attention;
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mod embeddings;
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mod encoder;
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@ -242,7 +242,7 @@ fn albert_for_token_classification() -> anyhow::Result<()> {
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config.id2label = Some(dummy_label_mapping);
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config.output_attentions = Some(true);
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config.output_hidden_states = Some(true);
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let bert_model = AlbertForTokenClassification::new(&vs.root(), &config);
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let albert_model = AlbertForTokenClassification::new(&vs.root(), &config);
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// Define input
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let input = [
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@ -268,7 +268,7 @@ fn albert_for_token_classification() -> anyhow::Result<()> {
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// Forward pass
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let model_output =
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no_grad(|| bert_model.forward_t(Some(&input_tensor), None, None, None, None, false));
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no_grad(|| albert_model.forward_t(Some(&input_tensor), None, None, None, None, false));
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assert_eq!(model_output.logits.size(), &[2, 12, 4]);
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assert_eq!(
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307
tests/fnet.rs
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307
tests/fnet.rs
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@ -0,0 +1,307 @@
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extern crate anyhow;
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extern crate dirs;
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use rust_bert::fnet::{
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FNetConfig, FNetConfigResources, FNetForMaskedLM, FNetForMultipleChoice,
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FNetForQuestionAnswering, FNetForTokenClassification, FNetModelResources, FNetVocabResources,
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};
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use rust_bert::pipelines::common::ModelType;
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use rust_bert::pipelines::sentiment::{SentimentConfig, SentimentModel, SentimentPolarity};
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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::{FNetTokenizer, MultiThreadedTokenizer, TruncationStrategy};
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use rust_tokenizers::vocab::Vocab;
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use std::collections::HashMap;
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use tch::{nn, no_grad, Device, Tensor};
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#[test]
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fn fnet_masked_lm() -> anyhow::Result<()> {
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// Resources paths
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetConfigResources::BASE));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetVocabResources::BASE));
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let weights_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetModelResources::BASE));
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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: FNetTokenizer =
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FNetTokenizer::from_file(vocab_path.to_str().unwrap(), false, false)?;
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let config = FNetConfig::from_file(config_path);
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let fnet_model = FNetForMaskedLM::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![3; 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 =
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no_grad(|| fnet_model.forward_t(Some(&input_tensor), None, None, None, false))?;
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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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assert_eq!("▁one", word_1);
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assert_eq!("▁the", word_2);
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assert!((f64::from(model_output.prediction_scores.get(0).get(4).max()) - 13.1721).abs() < 1e-4);
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Ok(())
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}
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#[test]
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fn fnet_for_sequence_classification() -> anyhow::Result<()> {
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// Set up classifier
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let config_resource = Resource::Remote(RemoteResource::from_pretrained(
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FNetConfigResources::BASE_SST2,
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));
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let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(
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FNetVocabResources::BASE_SST2,
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));
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let model_resource = Resource::Remote(RemoteResource::from_pretrained(
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FNetModelResources::BASE_SST2,
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));
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let sentiment_config = SentimentConfig {
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model_type: ModelType::FNet,
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model_resource,
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config_resource,
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vocab_resource,
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..Default::default()
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};
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let sentiment_classifier = SentimentModel::new(sentiment_config)?;
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// Get sentiments
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let input = [
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"Probably my all-time favorite movie, a story of selflessness, sacrifice and dedication to a noble cause, but it's not preachy or boring.",
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"This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo...",
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"If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.",
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];
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let output = sentiment_classifier.predict(&input);
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assert_eq!(output.len(), 3usize);
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assert_eq!(output[0].polarity, SentimentPolarity::Negative);
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assert!((output[0].score - 0.9978).abs() < 1e-4);
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assert_eq!(output[1].polarity, SentimentPolarity::Negative);
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assert!((output[1].score - 0.9982).abs() < 1e-4);
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assert_eq!(output[2].polarity, SentimentPolarity::Positive);
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assert!((output[2].score - 0.7570).abs() < 1e-4);
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Ok(())
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}
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//
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#[test]
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fn fnet_for_multiple_choice() -> anyhow::Result<()> {
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// Resources paths
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetConfigResources::BASE));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetVocabResources::BASE));
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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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// Set-up model
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let device = Device::Cpu;
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let vs = nn::VarStore::new(device);
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let tokenizer: FNetTokenizer =
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FNetTokenizer::from_file(vocab_path.to_str().unwrap(), false, false)?;
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let mut config = FNetConfig::from_file(config_path);
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config.output_attentions = Some(true);
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config.output_hidden_states = Some(true);
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let fnet_model = FNetForMultipleChoice::new(&vs.root(), &config);
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// Define input
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let input = [
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"Looks like one thing is missing",
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"It\'s like comparing oranges to apples",
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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)
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.to(device)
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.unsqueeze(0);
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// Forward pass
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let model_output = no_grad(|| {
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fnet_model
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.forward_t(Some(&input_tensor), None, None, None, false)
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.unwrap()
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});
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assert_eq!(model_output.logits.size(), &[1, 2]);
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assert_eq!(
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config.num_hidden_layers as usize,
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model_output.all_hidden_states.unwrap().len()
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);
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Ok(())
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}
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#[test]
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fn fnet_for_token_classification() -> anyhow::Result<()> {
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// Resources paths
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetConfigResources::BASE));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetVocabResources::BASE));
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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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// Set-up model
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let device = Device::Cpu;
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let vs = nn::VarStore::new(device);
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let tokenizer: FNetTokenizer =
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FNetTokenizer::from_file(vocab_path.to_str().unwrap(), false, false)?;
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let mut config = FNetConfig::from_file(config_path);
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let mut dummy_label_mapping = HashMap::new();
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dummy_label_mapping.insert(0, String::from("O"));
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dummy_label_mapping.insert(1, String::from("LOC"));
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dummy_label_mapping.insert(2, String::from("PER"));
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dummy_label_mapping.insert(3, String::from("ORG"));
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config.id2label = Some(dummy_label_mapping);
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config.output_hidden_states = Some(true);
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let fnet_model = FNetForTokenClassification::new(&vs.root(), &config);
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// Define input
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let input = [
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"Looks like one thing is missing",
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"It\'s like comparing oranges to apples",
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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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fnet_model
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.forward_t(Some(&input_tensor), None, None, None, false)
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.unwrap()
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});
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assert_eq!(model_output.logits.size(), &[2, 11, 4]);
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assert_eq!(
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config.num_hidden_layers as usize,
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model_output.all_hidden_states.unwrap().len()
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);
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Ok(())
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}
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#[test]
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fn fnet_for_question_answering() -> anyhow::Result<()> {
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// Resources paths
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetConfigResources::BASE));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(FNetVocabResources::BASE));
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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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// Set-up model
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let device = Device::Cpu;
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let vs = nn::VarStore::new(device);
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let tokenizer: FNetTokenizer =
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FNetTokenizer::from_file(vocab_path.to_str().unwrap(), false, false)?;
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let mut config = FNetConfig::from_file(config_path);
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config.output_hidden_states = Some(true);
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let fnet_model = FNetForQuestionAnswering::new(&vs.root(), &config);
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// Define input
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let input = [
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"Looks like one thing is missing",
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"It\'s like comparing oranges to apples",
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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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fnet_model
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.forward_t(Some(&input_tensor), None, None, None, false)
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.unwrap()
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});
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assert_eq!(model_output.start_logits.size(), &[2, 11]);
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assert_eq!(model_output.end_logits.size(), &[2, 11]);
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assert_eq!(
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config.num_hidden_layers as usize,
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model_output.all_hidden_states.unwrap().len()
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);
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Ok(())
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}
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