Hugging Face is widely known across the AI industry for its capabilities in natural language processing (NLP). Hugging Face has a large, open-source library containing pre-trained models including large language models (LLMs). LLMs such as BERT, RoBERTa, and GPT are particularly known for their cutting-edge natural language processing capabilities.
evoML has now integrated Hugging Face into its automated ML model code development and optimisation process, expanding the NLP functionalities of the platform. In this article, we take a quick look at the benefits of this integration and discuss some key areas where NLP can be applied in the finance industry.
🤗 Hugging face
Hugging Face has a large, open-source library containing pre-trained models including models for natural language processing (NLP) tasks. These models can be further fine-tuned for specific machine learning tasks.
💻 evoML
evoML is an AI optimisation platform that enables businesses to quickly generate production-ready AI model code from raw data in minutes not months, as well as accelerating existing AI model code for the targeted hardware, while reducing deployment costs.
evoML+Hugging Face: What’s so special?
With Hugging Face, evoML now offers cutting-edge NLP capabilities to businesses that want to build their own machine-learning models.
With this integration, our customers can now directly access Hugging Face pre-trained large language models (LLM) through evoML without leaving the platform. Users can then fine-tune these models on evoML with their custom datasets to improve performance further. Models developed using evoML can be easily optimised for faster speed and targeted hardware using evoML’s signature optimisation functionalities. Furthermore, customers can have full ownership of the model code without worrying about data privacy and security vulnerabilities.
Benefits of Building Custom Generative Models with evoML over Public APIs
- Data privacy. With evoML, your business data is not sent to external servers, ensuring complete data privacy.
- Flexibility. evoML offers a wide range of open-source models to choose from, and you can also upload your own models, providing greater flexibility and customisation.
- Model independence. You are not dependent on a third-party API service and have complete control over the model’s development, optimisation and maintenance.
How does it work?
Developing and fine-tuning NLP models on evoML is easy and straightforward, users simply upload their text data, customise options for the machine learning task, and direct evoML to carry out the model-building process.
Further, evoML allows users to:
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Choose which pre-trained language models to fine-tune on their own proprietary dataset.
2. Tune model parameters (such as Learning Rates, Epochs, Max Length) in parallel using our search algorithm to get even better model performance.
3. Run models in parallel and compare them on selected optimisation metrics.
To understand this process in depth, see our explainer video here:
In addition to using evoML to develop models from scratch, users are also able to upload their own models (custom-built or from other platforms) to evoML and have them fine-tuned.
Key applications in finance
NLP has many applications across a variety of industries. We look at four key applications of NLP in finance below.
- Sentiment analysis: Sentiment analysis uses NLP to analyse the tone of text data. Texts can include financial news, customer reviews, or social media content. Sentiment analysis involves identifying and extracting subjective information, including opinions, attitudes, and emotions. With the help of sentiment analysis, financial investors and traders can gain valuable insights into market sentiment. This knowledge can be leveraged to gauge the overall mood of the market and understand how it might affect the performance of specific stocks or companies.
evoML and its Hugging Face-based NLP capabilities enable businesses to develop machine learning models to process larger amounts of text data for sentiment analysis, drawing more useful insights on which to base financial decisions. - Named entity recognition: Named entity recognition (NER) uses NLP to identify and extract named entities from text data, including company names, stock symbols, financial terms, and other important information. With Hugging Face and its LLM capabilities now embedded in evoML, businesses can quickly identify and extract key information and trends from large volumes of text data to gain a deeper understanding of market trends.
- Customer support: Technologies such as NLP-powered chatbots enhance customer support tasks of financial services. They can be used to answer customer queries that would otherwise require human input. Especially by using chatbots to answer more generic questions, financial firms can direct valuable human resources to address more critical concerns. evoML allows customers to draw from language models that are trained on large datasets, thereby improving the robustness of chatbots.
- Forecasting: NLP and text data can be used to better forecast future trends in the financial sector, such as price movements. Using evoML and its NLP tools, financial analysts are able to analyse text data such as financial reports, social media content, and news, to develop a more holistic view of financial markets, allowing for more accurate forecasts of financial indicators.
In any of the above use cases, users can simply upload their text data to evoML (real data or synthetically generated/augmented data) to develop custom machine learning models to support the selected task.
