Customizable NLP Models

Dec 1, 2022

Strategic decision making is the key to all businesses’ success, but in order for a company to make accurate predictions and decisions at the right time, they must obtain accurate insights from their data in a short period of time and with the least amount of effort possible.

However, businesses deal with massive amounts of data. And because the meaning of data varies depending on its type, context, and importance to the organization dealing with it, the process of extracting data can never be a one-size-fits-all solution.


That’s where machine learning-based natural language processing models, specifically customizable NLP models, come into play.

“Off-the-shelf” Machine learning models can accelerate the extraction and discovery of new insights from existing documents. However, because data varies depending on the use case and the organization using it, these models are typically inaccurate.

In this article, we will discuss the differences between off-the-shelf and customizable ML models, as well as their applications and benefits.

Customizable NLP Models

Off-the-shelf solutions are intended to perform the most common operations with the least amount of configuration.

They are the most effective when there isn’t enough time or data to train an NLP model.

Off-the-shelf models are frequently trained on general-purpose data sources, so they are not typically tailored to a specific use cases, which frequently necessitates understanding of specialized terms.





– Results can be obtained with minimal machine learning expertise.

– It Allows users to create prototypes and proofs of concept quickly.

– It Reduces development time by utilizing existing knowledge.

– They are available for all stages of ML workloads.

– They relieve the strains and issues associated with preprocessing efforts.

– They prioritize configuration over implementation.


By using off-the-shelf models, users can concentrate on how the various parameters will fit together rather than allocating resources to determine what should be done.
However, while it is simple for anyone to build and deploy this type of machine learning model, it is only suitable for prototyping since the exported insights may have low accuracy. Examples of off-the-shelf ML models:

Customizable NLP Models


BERT is an abbreviation for Bidirectional Encoder Representations from Transformers.

It is intended to pre-train deep bidirectional representations in all layers by conditioning on both left and right context.

BERT can be fine-tuned with just one additional output layer to produce cutting-edge models for a wide range of tasks, including question answering and language inference, without requiring significant task-specific architecture modifications.


XLNet, which is commonly used for NLP tasks like reading comprehension, text classification, sentiment analysis, and so on. XLNet is a generalized autoregressive pretraining method that combines the best of autoencoding and autoregressive language modeling.


GPT-3 is the third generation of the Generative Pre-trained Transformer is a machine learning neural network model trained on internet data to generate any type of text.

GPT-3 requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text and achieves promising results, even outperforming fine-tuned models on occasion.


The Pathways Language Model (PaLM) achieves cutting-edge few-shot performance across most tasks, with breakthrough capabilities demonstrated in language understanding, language generation, reasoning, and code-related tasks.

Custom ML models

Custom-made solutions built from the ground up are critical in complex situations such as fine-tuning a specific ML stage to the specifics of a problem or developing a specific feature set that necessitates custom feature engineering.

It is also highly recommended when the goal is the solution’s scalability and sustainability.

NLP Custom models can be trained and taught to understand the underlying meaning and relationships specific to the use case or industry domain in question.
They can be built by data scientists or by subject matter experts using code-free tooling.
The SME’s role is to define the information that is important in a model.
The next step is uploading the documents for training, annotating them, and then defining the entities or relations between them for extraction.

The training process may be long because the model usually requires numerous improvements to ensure its effectiveness.

Advantages :

– Comprehension of what the data actually represents for a specific organization

– Can be built frequently without the help of data scientists or developers, using “no code” development tools.

– Conversant with the industry’s technical terms.

– Adaptable to specific data sets

– Provides more accurate and relevant results.

– Customizable models can provide users with the highest level of accuracy while reducing noise, allowing businesses to save money, time, and resources on data classification and extraction.


Use cases in the pharmaceutical industry

  • Personalized Treatment

Personalized medicine refers to more effective treatment based on individual health data and predictive analytics.

The domain is heavily reliant on customizable ML models, which allow physicians to choose from a smaller set of diagnoses, resulting in better disease assessment than off shelf models.




  • Drug Discovery

The use of customizable machine learning models in early-stage drug discovery has the potential for a variety of applications, ranging from initial drug compound screening to predicting success rates based on biological factors.






Use cases in the Finance Industry

  • Machine learning for customer experience in financial services

Customizing ML models helps the finance industry improve customer experience, services, and budgets by replacing routine manual work and off-the-shelf ML models.

  • Financial Document Analysis

Fintech companies can gain insights from their existing documentation resources by training customizable NLP models to sift through hundreds of documents, extracting and consolidating the most relevant, insightful data.




Use cases in the legal industry

  • Reducing Errors

When compared to off-the-shelf models and lawyers or paralegals, customizable NLP models are required to reduce errors and save time in data-heavy tasks.


  • Probating Wills

With a manual process, probating wills can take several years because much of the time is spent looking through documents to find relevant information.

Using an off-the-shelf model can reduce time away from work, but it may produce inaccurate generalized results.

However, a customized NLP model can ensure the highest possible accuracy in minutes.

100% custom vs. 100% off-the-shelf model

Customizable NLP Models

Choosing between a custom model and an off-the-shelf model isn’t always a binary decision because it’s never about going completely custom or not.

Different business problems necessitate different solutions, which can be composed of numerous individual components and services that must work together to form an accurate solution.

As a result, ML models are built using a combination of the two, with off-the-shelf services used to extract general insights and custom models used to work on accuracy and specifications.


But we can’t deny that if you’re willing to invest the time and money to pay for efficient and scalable solutions, then 100% custom-built NLP models are extremely rewarding.

Because, at the end of the day, the customization approach is designed to serve specific business interests while producing excellent results.

If you want to reduce the risks of inaccuracy and misinterpretation, UBIAI’s customizable NLP solutions can help you maximize your ROI.

Start implementing tailored solutions to extract the best insights from your data and stay ahead of your competitors.