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  • Writer's pictureJuhi Jain

Get started with Entity Recognition on the NeuralSpace Platform

Entities, or keywords, play a major role in understanding short and long text. To perform an action on a certain user query you not only need to understand the intent behind it but also the entities present in it. In a given piece of text, entities can be anything from names, addresses, account numbers to very domain-specific terms like names of chemicals, medicines, etc. Essentially any valuable information that can be extracted from text.

The NeuralSpace Platform is a collection of pre-trained Natural Language Processing (NLP) models that can be fine-tuned to each unique use case with AutoNLP.

In this short tutorial, we will explain to you how to get started with Entity Recognition, on the NeuralSpace Platform.

It’s an easy 8 step process and you will have trained your first Entity Recognition model with AutoNLP in one of NeuralSpace’s 87 natively supported languages. Best of all, we will use NeuralSpace’s no-code web interface and achieve state-of-the-art results without writing a single line of code.

We will train an Entity Recognition model in code-mixed Hinglish (Hindi + English).

Let's Get Started


Without any credit card or other payment details, sign up to NeuralSpace here, activate your account through your verification email and log in.


After logging in, you will be welcomed by a virtual tour that will Import a dataset, then Train and Deploy a model which you can test. We know the Skip button is enticing but just take it and you will save yourself a ton of time later on.


Select Entity Recognition from All Services in the column menu on the left. Then click on Projects.


Navigate to the top right corner and click on Create project. If you want to read more about the concepts behind projects, check out our Docs.

After you click on Create Project, you will see three options as can be seen in the image below. For this tutorial, we will create a project using the "Import from NeuralSpace Datasets" option.


To import a dataset, select the language you want to train your model in.

Here, we select Multilingual/Code-mixed as our language. Next, select a dataset from the available options, click on Import and give your project a name.

Now, you will be redirected to the Projects page where you can see all your current Entity Recognition projects. Wait for a couple of minutes as your dataset gets uploaded. Click on the project tile once the dataset is 100% uploaded.


Now, you can see your project details. You are now prepared to train your custom model with AutoNLP. Select the number of training jobs (read more about what training jobs are here), click on Train with AutoNLP and you are ready to — CLICK, TRAIN, CHILL at its best.

You can follow the progress in the Model List at the bottom of your Project Details page.

The Training Status will go from Queued to Training to Completed within a few seconds, but you may need to wait a bit longer for larger datasets. Whereas a small dataset with 20 examples only takes 3–5 seconds to train, a dataset with 5000 examples will be trained in about 15 minutes.


Once your model is trained and you can deploy it now by clicking on the Deploy with AutoMLOps button.


Once it’s deployed, you can evaluate it by clicking on the model in the Model List and then Test model in the right menu. Type in any sentence you want to test your model on and see its output by clicking on Parse Text.

That was it! 8 simple steps to train and deploy a state-of-the-art transformer-based deep learning model and evaluate it in code-mixed Hinglish (English + Hindi).


Get started now: NeuralSpace Platform

Read about other tutorials in our Docs and sign up to the NeuralSpace Community to get involved and collaborate with fellow users.

Happy NLP!

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