Best Python NLP library for supervised topic classification I have a labeled dataset that I have ingested into a dataframe. It consists of news articles,
&gt;&gt;&gt; df.columns

Index(['title', 'headline', 'byline', 'dateline', 'text', 'copyright',
       'country', 'industry', 'topic', 'file'],
      dtype='object')

where the text column contains the body (text) of the article and the topic column contains a list of associated topics.
I want to train a model from this dataset to predict the article topics. I was considering using transformers (https://huggingface.co/transformers/index.html) to do this, along with tensorflow, but I from what I know of transformers, it's not really good for this.
What would be the best NLP library to perform this task with high accuracy?
The recommendation is going to be still Huggingface transformers. You extract the features from BERT, then, some dense layers and then, feed them into sigmoid layer with unit equal to number of classes. Pose it as a multi-label classification.