Text augmentation was performed with synonym replacement using BERT embeddings.Īny files or folders with unbalanced or balanced in the name is in relation to these two datasets. Until we reached a 50-50 class distribution. To get our balanced dataset, we used nlpaug to augment the minority class of the unbalanced dataset To get our unbalanced dataset, we undersampled the majority class of this intermediate dataset until toxic comments make up 20.15% of all data. Toxic comments make up 9.58% of this intermediate dataset. Toxic ('isToxic = 1) or non-toxic ('isToxic = 0). Instead, we converted the original dataset to a binary classification task where labels are either classified as Toxic, Severe Toxic, Obscene, Threat, Insult, Identity Hate),īut we decided against using these labels due to their subjectivity. This dataset has labels intended for a multi-label classification task (e.g. If anything is confusing, please see the accompanying Medium article for an explanation of my methodologies! DatasetsĪs mentioned previously, the datasets used to train our models are based on the Jigsaw Toxic Comment dataset found on Kaggle. To predict toxic comments on a modified version of the Jigsaw Toxic Comment dataset on Kaggle.
#Finetune synonym code#
In this repository, we propose code to be used as a reference point for fine-tuning pretrained modelsįrom the Hugging Face Transformers Library on binary classification tasks using TF 2.0. You can add partial matching logic to Synonym Search Term(s) by adding a wildcard character * to control partial matching.Hugging Face Transformers: Fine-tuning DistilBERT for Binary Classification Tasks AboutĬreating high-performing natural language models is as time-consuming as it is expensive,īut recent advances in transfer learning as applied to the domain of NLP have made it easyįor companies to use pretrained models for their natural language tasks. If the Replace checkbox is ticked, the Search Term(s) will be replaced by the Synonym(s), if that checkbox is not ticked the Search Term(s) are left in place. These are the words that are in your site content. The Synonym(s) column contains the synonym(s) for those search terms. These are the words that your visitors are searching for. The Search Term(s) column contains the search terms for each synonym. Note: Synonyms can be sorted by drag and drop. Utilizing Synonyms allows you to be sure of the substitutions that are taking place, whereas partial matches can result in some unexpected pairings.
Taking advantage of Synonyms based on analysis of your Statistics is recommended before a blanket solution such as enabling partial matches. Synonyms are a very powerful feature of SearchWP. Clear Stopwords Removes all Stopwords either to start fresh, or disable the functionality entirely. Restore Defaults Restore the default list of Stopwords used at the time of installation. Sort Alphabetically For long, customized lists, you can quickly alphabetize your Stopwords. Using the Action menu, you can automatically manipulate your Stopwords like so: View Suggestions SearchWP will analyze its index to find the most common words and allow you to pick and choose which should be considered Stopwords. A default list of Stopwords is present upon installation of SearchWP, but you can customize it by adding or removing any number of Stopwords to better suit your purpose. Stopwords are ignored so as to improve the relevancy and speed of search results.
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