TextHRRASA
crowdkit.aggregation.texts.text_hrrasa.TextHRRASA | Source code
TextHRRASA(
self,
encoder: Callable,
n_iter: int = 100,
tol: float = 1e-05,
lambda_emb: float = 0.5,
lambda_out: float = 0.5,
alpha: float = 0.05,
calculate_ranks: bool = False,
output_similarity: Callable = glue_similarity
)
HRRASA on text embeddings.
Given a sentence encoder, encodes texts provided by workers and runs the HRRASA algorithm for embedding aggregation.
Parameters Description
| Parameters | Type | Description |
|---|---|---|
encoder |
Callable | A callable that takes a text and returns a NumPy array containing the corresponding embedding. |
n_iter |
int | A number of HRRASA iterations. |
lambda_emb |
float | A weight of reliability calculated on embeddigs. |
lambda_out |
float | A weight of reliability calculated on outputs. |
alpha |
float | Confidence level of chi-squared distribution quantiles in beta parameter formula. |
calculate_ranks |
bool | If true, calculate additional attribute |
Examples:
We suggest to use sentence encoders provided by Sentence Transformers.
from crowdkit.datasets import load_dataset
from crowdkit.aggregation import TextHRRASA
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('all-mpnet-base-v2')
hrrasa = TextHRRASA(encoder=encoder.encode)
df, gt = load_dataset('crowdspeech-test-clean')
df['text'] = df['text'].apply(lambda s: s.lower())
result = hrrasa.fit_predict(df)
Methods Summary
| Method | Description |
|---|---|
| fit_predict | Fit the model and return aggregated texts. |
| fit_predict_scores | Fit the model and return scores. |