Struct SentimentModel

pub struct SentimentModel { /* private fields */ }

SentimentClassifier to perform sentiment analysis

Implementations

impl SentimentModel

pub fn new( sentiment_config: SentimentConfig, ) -> Result<SentimentModel, RustBertError>

Build a new SentimentModel

Arguments

• sentiment_config - SentimentConfig object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)

Example

use rust_bert::pipelines::sentiment::SentimentModel;

let sentiment_model = SentimentModel::new(Default::default())?;

pub fn new_with_tokenizer( sentiment_config: SentimentConfig, tokenizer: TokenizerOption, ) -> Result<SentimentModel, RustBertError>

Build a new SentimentModel with a provided tokenizer.

Arguments

• sentiment_config - SentimentConfig object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
• tokenizer - TokenizerOption tokenizer to use for sentiment classification.

Example

use rust_bert::pipelines::common::{ModelType, TokenizerOption};
use rust_bert::pipelines::sentiment::SentimentModel;
let tokenizer = TokenizerOption::from_file(
    ModelType::Bert,
    "path/to/vocab.txt",
    None,
    false,
    None,
    None,
)?;
let sentiment_model = SentimentModel::new_with_tokenizer(Default::default(), tokenizer)?;

pub fn get_tokenizer(&self) -> &TokenizerOption

Get a reference to the model tokenizer.

pub fn get_tokenizer_mut(&mut self) -> &mut TokenizerOption

Get a mutable reference to the model tokenizer.

pub fn predict<'a, S>(&self, input: S) -> Vec<Sentiment>

where S: AsRef<[&'a str]>,

Extract sentiment form an array of text inputs

Arguments

• input - &[&str] Array of texts to extract the sentiment from.

Returns

• Vec<Sentiment> Sentiments extracted from texts.

Example

use rust_bert::pipelines::sentiment::SentimentModel;

let sentiment_classifier =  SentimentModel::new(Default::default())?;

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 = sentiment_classifier.predict(&input);