Module rust_bert::pipelines::sequence_classification

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Sequence classification pipeline (e.g. Sentiment Analysis)

More generic sequence classification pipeline, works with multiple models (Bert, Roberta)

use rust_bert::pipelines::sequence_classification::SequenceClassificationConfig;
use rust_bert::resources::{RemoteResource};
use rust_bert::distilbert::{DistilBertModelResources, DistilBertVocabResources, DistilBertConfigResources};
use rust_bert::pipelines::sequence_classification::SequenceClassificationModel;
use rust_bert::pipelines::common::ModelType;

//Load a configuration
use rust_bert::pipelines::common::ModelResource;
let config = SequenceClassificationConfig::new(ModelType::DistilBert,
   ModelResource::Torch(Box::new(RemoteResource::from_pretrained(DistilBertModelResources::DISTIL_BERT_SST2))),
   RemoteResource::from_pretrained(DistilBertVocabResources::DISTIL_BERT_SST2),
   RemoteResource::from_pretrained(DistilBertConfigResources::DISTIL_BERT_SST2),
   None, // Merge resources
   true, //lowercase
   None, //strip_accents
   None, //add_prefix_space
);

//Create the model
let sequence_classification_model = SequenceClassificationModel::new(config)?;

let input = [
    "Probably my all-time favorite movie, a story of selflessness, sacrifice and dedication to a noble cause, but it's not preachy or boring.",
    "This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo...",
    "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.",
];
let output = sequence_classification_model.predict(&input);

(Example courtesy of IMDb)

Output: 

let output =
[
   Label { text: String::from("POSITIVE"), score: 0.9986, id: 1, sentence: 0},
   Label { text: String::from("NEGATIVE"), score: 0.9985, id: 0, sentence: 1},
   Label { text: String::from("POSITIVE"), score: 0.9988, id: 1, sentence: 12},
]

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