[−][src]Struct rust_bert::pipelines::sequence_classification::SequenceClassificationModel
Implementations
impl SequenceClassificationModel
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pub fn new(
config: SequenceClassificationConfig
) -> Fallible<SequenceClassificationModel>
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config: SequenceClassificationConfig
) -> Fallible<SequenceClassificationModel>
Build a new SequenceClassificationModel
Arguments
config
-SequenceClassificationConfig
object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
Example
use rust_bert::pipelines::sequence_classification::SequenceClassificationModel; let model = SequenceClassificationModel::new(Default::default())?;
pub fn predict(&self, input: &[&str]) -> Vec<Label>
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Classify texts
Arguments
input
-&[&str]
Array of texts to classify.
Returns
Vec<Label>
containing labels for input texts
Example
let sequence_classification_model = SequenceClassificationModel::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 = sequence_classification_model.predict(&input);
pub fn predict_multilabel(
&self,
input: &[&str],
threshold: f64
) -> Vec<Vec<Label>>
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&self,
input: &[&str],
threshold: f64
) -> Vec<Vec<Label>>
Multi-label classification of texts
Arguments
input
-&[&str]
Array of texts to classify.threshold
-f64
threshold above which a label will be considered true by the classifier
Returns
Vec<Vec<Label>>
containing a vector of true labels for each input text
Example
let sequence_classification_model = SequenceClassificationModel::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 = sequence_classification_model.predict_multilabel(&input, 0.5);
Auto Trait Implementations
impl !RefUnwindSafe for SequenceClassificationModel
impl !Send for SequenceClassificationModel
impl !Sync for SequenceClassificationModel
impl Unpin for SequenceClassificationModel
impl !UnwindSafe for SequenceClassificationModel
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<V, T> VZip<V> for T where
V: MultiLane<T>,
V: MultiLane<T>,