29.05.2017 · The best classification accuracy I have managed to get is 61% and I need it to be at least 85%. Any help on how I can improve the accuracy would be greatly appreciated. Thanks a lot. Please let me know if you need any more details.
Use this cheat sheet if you want to use BERT, and your input data consists of English text with a classification tag. What is BERT? BERT is a state-of-the-art ...
In this tutorial, we will learn how to use BERT for text classification. We will begin with a brief introduction of BERT, its architecture and fine-tuning mechanism. Then we will learn how to fine-tune BERT for text classification on following classification tasks: Binary Text Classification: IMDB sentiment analysis with BERT [88% accuracy].
Feb 18, 2021 · Text Classification: How BERT boost the performance. ... AUC and Accuracy. Also we trained our models using 15 epochs. We can find that BERT has more than 167 times params than others, it takes ...
From [How to Fine-tune BERT for Text Classification] how_to_bert. Get words from both the beginning and the end. The figure above shows how to use the first 3 ...
May 29, 2017 · The best classification accuracy I have managed to get is 61% and I need it to be at least 85%. Any help on how I can improve the accuracy would be greatly appreciated. Thanks a lot. Please let me know if you need any more details.
04.11.2020 · Then we will use our knowledge of the text to improve the understanding of our classifier and see if this has a real impact on our model. and used as a training basis for our classifier. This matrix is constructed in such a way that the words become columns, the comments id are the rows and each row is composed of 0 or 1 which are indicators of the …
08.12.2020 · Automated text classification has become a staple toolkit for ... is necessary for improving classification tasks ... the model outputs but …
Because we use only the text data to classify, we will drop unimportant columns and only keep id , tweet and label columns. In [0]:. # Load data and set labels ...
29.10.2015 · Adding bigrams to feature set will improve the accuracy of text classification model. Similarly considering Part of Speech tags combined with with words/n-grams will give an extra set of feature space. also increase the classifications. For example –
I'm attempting to fine-tune the HuggingFace TFBertModel to be able to classify some text to a single label. I have the model up and running, however the accuracy is extremely low from the start. My expectation is that the accuracy would be high given that it is using the BERT pre-trained weights as a starting point.