What is Text Classification?
Text classification is the process of categorizing a block of text based on one or several labels.
Let's say you have the following block of text:
Perseverance is just getting started, and already has provided some of the most iconic visuals in space exploration history. It reinforces the remarkable level of engineering and precision that is required to build and fly a vehicle to the Red Planet.
Let's also say that you also have the following labels: space, science, and food.
Now the question is: which ones of these labels apply best to this block of text? Answer is space and science of course.
Why Use Text Classification?
Text classification can be used in many useful situations. Let's give you a couple of examples.
Sort Incoming Messages
Are you flooded with incoming messages at work? Well, properly labelling these messages in advance can definitely make you more productive. You could know in advance which messages are advertising, and which one are customer requests, for example
Some customers requests must sometimes be addressed as a priority. If that's the case it can be very interesting to detect them in advance and address them right away.
Let's say you are looking for companies in the automotive field. You could scan websites and only keep those who have the "automotive" label applied.
You might want to monitor new content from various sources and categorize it accordingly. Text classification is the right way to do so.
Control whether you want to use the model on a GPU. Machine learning models run much faster on GPUs.
Whether multiple labels should be applied to your text, meaning that the model will calculate an independent score for each label. Defaults to true.
A list of labels you want to use to classify your text. Optional if you're using Fast GPT-J, Fine-tuned GPT-NeoX 20B, or Dolphin.
AI models don't always work well with non-English languages.
We do our best to add non-English models when it's possible. See for example Fine-tuned GPT-NeoX 20B, Dolphin, ChatDolphin, XLM Roberta Large XNLI, Paraphrase Multilingual Mpnet Base V2, or spaCy. Unfortunately not all the models are good at handling non-English languages.
In order to solve this challenge, we developed a multilingual module that automatically translates your input into English, performs the actual NLP operation, and then translates the result back to your original language. It makes your requests a bit slower but often returns very good results.
Even for models that natively understand non-English languages, they actually sometimes work even better with the multilingual addon.
Simply select your language in the list, and from now on you can write the input text in your own language!
This multilingual add-on is a free feature.