Pre-trained laptop classifier accuracy does not match what is stated in README

[Aishwarya1023]

I replaced the domain and classifier in test_performance.py from restaurants to laptops and I get an accuracy of 0.38 (the accuracy stated in the README is 0.8)

This was the code:

import numpy as np
import aspect_based_sentiment_analysis as absa
from aspect_based_sentiment_analysis.training import ConfusionMatrix

def test_semeval_classification_laptops():
    examples = absa.load_examples(dataset='semeval',
                                  domain='laptop',
                                  test=True)
    nlp = absa.load('absa/bert-lapt-0.1')

    metric = ConfusionMatrix(num_classes=3)
    confusion_matrix = nlp.evaluate(examples, metric, batch_size=32)
    confusion_matrix = confusion_matrix.numpy()
    accuracy = np.diagonal(confusion_matrix).sum() / confusion_matrix.sum()

    print(confusion_matrix)
    print(accuracy)

[rolczynski]

hey @Aishwarya1023
thank you for this issue. In this snippet you use the post-trained language model absa/bert-lapt-0.1. Instead you should use this: absa/classifier-lapt-0.1. Please wait two weeks and the solution (the uploaded pretrained model) will be ready ☺️

[Aishwarya1023]

@rolczynski Sounds good! Please let me know here when it's ready. :) Great work btw!

[rolczynski]

It's done - check it out 😊