Machine learning is advancing at an unprecedented pace. There are some incredible, game-changing technologies out there and they are moving us ever closer to a world previously only seen in sci-fi movies.
This rapid advancement is driven by improvements in computer processing, with AI now able to process immense quantities of data. But where does this data come from and what are the best sources of training data for language models?
Top 6 sources of training data
The Google Dataset Search engine
Google is an expert in both data and user experience. It knows where people shop, how they search, how they browse, and what they’re looking for. It’s no surprise then that Google also has some of the best datasets for machine learning.
Amazon datasets
Amazon datasets are hosted via Amazon Web Services. They include a wide selection of usage examples to show you how it can be used, thus making the data and information more accessible.
Kaspian, specialised data delivery platform
As businesses increasingly rely on language models for conversational AI, content generation, and information retrieval, the quality of the data used to train these models is becoming more critical. Kaspian, an Identrics product, offers a solution to this problem by providing high-quality training data that meets the highest standards.
Kaspian’s data streams are trusted by many of the world’s leading companies and are built from archives dating back to 2013. The data is domain-specific, meaning it can be used to build language models that are adapted to particular industries and domains. Kaspian’s data serves as an excellent foundation for developing both multilingual and language-specific models.
By using Kaspian’s data, companies can guarantee the accuracy and effectiveness of their language models. Kaspian’s data is easily accessible and can be filtered based on different criteria, such as topic, industry, search string, named entity recognition, sentiment analysis, and hate speech detection. By using Kaspian’s data, companies can build a solid foundation for their language models and ensure their accuracy and ethical soundness.
Kaggle
Kaggle data spans a wide variety of topics and can provide key insights into everything from social media use to space, medicine, and technology.
Kaggle boasts over 13 million members around the world and currently has access to more than 50,000 public datasets and 400,000 public notebooks.
Microsoft datasets
Microsoft Research Open Data is a vast collection of free datasets from Microsoft’s extensive research. It covers language processing, computer vision, and much more. There are handy dataset categories to inspire you and a search tool to help you find something specific.
Government datasets
Need data that is specific to a country or region? Then look for datasets published by that country’s government. These are fairly easy to find and they usually have a wealth of high-quality datasets available.
For instance, the European Commission keeps over 1.6 million European datasets across 36 countries. It’s a vast collection that covers many different topics. And that’s not all, as there are also over 1,300 news pieces relating to open data, as well as data stories and educational materials.
As for the United States, it stores everything on their Data.gov website. At the time of writing, there are 250,685 datasets available, and these come from all levels of government regiments – city, county, state, and federal. You can search by specific organisations and there is even a link that highlights the most viewed sets.
The UK operates something similar that you can use to search for published data across central and local authorities, as well as public bodies.
How much data do I need?
Again, it depends on the research, but more is usually better. The more data that a program has, the more nuances it can detect in those data, and the greater its understanding will be.
Qualified data is crucial for large language models building, as it not only enhances their accuracy and performance but also ensures that they are built upon an ethical foundation. This helps in minimizing biases, maintaining fairness, and ensuring that the AI-driven solutions derived from these models are trustworthy and adhere to the highest ethical standards.
In conclusion
One of the most exciting things about AI and machine learning is that things will only get better. As amazing as AI seems right now, future generations will look back at the 2020s in much the same way that we look at the 1990s and the birth of the internet—it was the genesis, but it was also very rudimentary.
That exciting future is driven by data, and the better the sources are, the sooner those sci-fi-style technologies will be realised.
If you need high-quality training data, contact us immediately to ask for more details or to request a demo of our data.
