TextRASA

crowdkit.aggregation.texts.text_rasa.TextRASA | Source code

TextRASA(
    self,
    encoder: Callable[[str], ...],
    n_iter: int = 100,
    tol: float = 1e-05,
    alpha: float = 0.05
)

RASA on text embeddings.

Given a sentence encoder, encodes texts provided by workers and runs the RASA algorithm for embedding aggregation.

Parameters description

Parameters	Type	Description
`encoder`	Callable[[str], ...]	A callable that takes a text and returns a NumPy array containing the corresponding embedding.
`n_iter`	int	A number of RASA iterations.
`alpha`	float	Confidence level of chi-squared distribution quantiles in beta parameter formula.

Examples:

We suggest to use sentence encoders provided by Sentence Transformers.

from crowdkit.datasets import load_dataset
from crowdkit.aggregation import TextRASA
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('all-mpnet-base-v2')
hrrasa = TextRASA(encoder=encoder.encode)
df, gt = load_dataset('crowdspeech-test-clean')
df['text'] = df['text'].str.lower()
result = hrrasa.fit_predict(df)

Methods summary

Method	Description
fit	Fit the model.
fit_predict	Fit the model and return aggregated texts.
fit_predict_scores	Fit the model and return scores.

Last updated: March 31, 2023

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