There has been a recent explosion in AI models that can read and write language. These large language models come pre-trained on massive datasets and can be accessed by a simple API call.

The most interesting part is, that these models are capable of performing a variety of AI and ML tasks, with very little training data. Classification, entity extraction, and topic modeling have a wide variety of use cases. And in many cases the bigger the model, the better the performance.

So when you’re building AI into your products, the natural question is do you build and train your own models, or do you use a pre-trained one? Let’s find out.

The Complex Task of Developing Your Own Model

Whether you’re building a simple classification model, or a complex content generation app, there are several steps involved in building and training these models, and they bring hidden costs.

Data Collection

The first step is collecting data to train your model. The more complex the task you’re trying to solve with AI, the more data you need. Large language models that can generate coherent content require gigabytes of training data. Even simpler classification tasks require thousands of training examples to perform well, and more often than not you won’t find this data in a neat little file on Kaggle.

Data Preparation

Research shows that data scientists spend nearly half their time cleaning their data. This includes removing bad data, fixing incorrect data, maybe even filtering and moderating data if you’re dealing with language and to ensure there’s no scope for harmful content.

Working with text to create large language models is extra complex due to the messy nature of language. Many extra challenges, such as sarcasm, ambiguity, and irony, complicate the training process. This means data collection and preparation are crucial for language models as you must account for all the intricacies of the English language.

Hosting and Infrastructure

The larger your dataset and model, the more power you need to train and host it. Many large language models require hundreds of GPUs, and when you factor in other equipment, hosting costs, and so on, you’re looking at a bill in the millions, if not tens of millions of dollars.

On top of that, you’ll need to regularly maintain your model - continuous re-training and updating of features, feeding in new data, and ongoing hosting costs add up significantly over time.

Personnel

These processes are only a few you must complete building a model from the beginning. Building custom models entails gathering pertinent training data, feature extraction, framework development, interface creation, and so on. To construct an adequate model, you may also require data engineers, data scientists, platform engineers, and business domain specialists.

Creating a custom model from scratch can become a drawn-out procedure that diverts attention from more important work. So, you can see how developing, and training models can cost a lot of time and money.

The Power of Pre-Trained

Pre-trained models can reduce the cost and effort required for deep learning because you do not have to spend time and money to gather and clean the data, not to mention the infrastructure and knowledge needed to train the models correctly.

What Are Pre-Trained Models?

A pre-trained model is a machine-learning model that third-party developers have developed, trained, and made available. Data scientists use massive datasets to train their models, and companies typically use them to address challenges requiring massive amounts of data.

In the artificial intelligence (AI) world, there’s no such thing as a perfect model. No model exists that is 100 percent accurate all the time. So, building a model is always a trade-off between effort and accuracy. Pre-trained models are often as precise as or even more accurate than self-built models because professionals specializing in building models build them.

Why Do Pre-trained Language Models Make Sense?

Studies show that pre-trained large language models perform as well as specifically trained models for custom tasks. Meaning that all of the efforts we described earlier to create your own model are moot, as you would do just as well, if not better, by picking a pre-trained model off the shelf.

Additionally, many pre-trained models like Cohere come with an easy-to-use API. With just a few lines of code, you have access to all the benefits of a large pre-trained model without the hidden costs described earlier. Our Python, Node, and Go SDKs allow you to build language AI into any stack, while we take care of everything else - hosting the model, updating it, and even ensuring safety.

Our models are also frequently trained using well-calibrated parameters, producing excellent accuracy, as you can see from the metrics.

And, despite being pre-trained, you still have a lot of control over the model outputs. By engineering your prompts and tweaking different parameters, you can significantly impact the outputs. If you have custom data you want to use in your own models, you can simply fine-tune a pre-trained model. Cohere allows you to upload your own training data and when that’s combined with the power of a Large Language Model, the results are incredible.

Real-World NLP Examples That Use Pre-Trained Models

It's not just small startups that benefit from pre-trained models. Many large companies prefer to use Large Language Models instead of dedicating resources to building in-house.

Named Entity Recognition

Named entity recognition (NER) instructs the model to determine the kind of word or phrase that appears in the input text. For instance, if a sentence says that John was born on 24th December 1990, the model detects that “John” is a name, “24th Dec“ is a date, and “1990” is a year.

This approach is used by various enterprise companies, including Netflix, Hulu, and Disney+, to help guide their content suggestions.

Text Classification

Classifying text automatically is extremely important for companies that deal with customer communications. Social media and gaming companies, for example, need to moderate user-generated content at scale and at speed. By classifying content into toxic or harmful speech, they can automate the entire process.

Instead of having to collect thousands of examples of toxic content to train your own model, you can simply use Cohere’s fine-tuned toxicity model to do this in minutes.

Summarization

Generating summaries might sound like a trivial task but doing it at scale is not easy. Many financial companies and law firms find summaries of long reports to be quite useful. Without their own internal ML teams, it makes sense for them to use pre-trained models.

Conclusion

Pre-trained models save time, resources, and money compared to building and training your own model. And they are often as effective and more efficient than custom models.

The best part is, that you can get started right away using our easy-to-use APIs, instead of waiting months to build and train your own model. Get started with Cohere for free, and test drive our pre-trained Large Language Models.