I have a bunch of training data for classifying product names, around 30,000 samples. The task is to classify these product names into types of product, around 100 classes (single words).
dutch lady sweetened uht milk => milk samsung galaxy note 10 => electronics cocacola zero => softdrink ...
All words in inputs are indexed to numbers, and so classes. I've tried to use tf.estimator.DNNClassifier to classify them but no good results. The outcome is just an accuracy of 4% which is no meaning.
Should it be I'm in the case that classes (Y values) are distributed kinda randomly and too hard to do multi-time linear separation?
Are there any existing solutions to classify a list of names? like my product names?