Fine-tuning an embedding model is a powerful technique for optimizing retrieval augmented generation (RAG) systems in finance. By training a smaller open-source embedding model like BAAI/bge-small-en on a domain-specific dataset, the model learns more meaningful vector representations that capture the nuances and semantics of financial language. This leads to significantly improved retrieval performance compared to using generic pre-trained embeddings.

https://huggingface.co/baconnier/Finance_embedding_large_en-V1.5

Fine-tuned financial embedding models, such as Banker++ RAG, demonstrate superior accuracy on tasks like semantic search, text similarity, and clustering. They enable RAG systems to better understand complex financial jargon and retrieve the most relevant information given a query.

Integrating these specialized embeddings is straightforward using libraries like LlamaIndex or Sentence-Transformers.

As the financial industry increasingly adopts AI, fine-tuned embedding models will play a crucial role in powering domain-specific NLP applications. From analyzing market sentiment to personalizing investment recommendations, these optimized embeddings unlock new possibilities for harnessing unstructured financial data. By combining the power of open-source models with the domain expertise embedded in financial corpora, fine-tuning paves the way for more intelligent and impactful RAG systems in finance.

Banker++ is trained to act like a Senior Banker.

I’m excited to share a resource that could potentially be a valuable addition to your financial toolkit. It’s a Financial LLM Model tailored for corporate entities and financial institutions.

In today’s complex financial landscape, having access to reliable analytical tools is crucial. This model, available at link:

https://huggingface.co/spaces/baconnier/Finance

https://huggingface.co/baconnier/Finance_dolphin-2.9.1-yi-1.5-9b

It offers a framework for assessing various financial scenarios with a level of precision and insight that could benefit your decision-making processes.

However, it’s important to note that this content is strictly for educational purposes and should not be construed as financial advice. Please exercise caution when applying any information provided.

While I’m not suggesting it’s a one-size-fits-all solution or a replacement for professional financial advice, it may offer valuable insights into areas such as risk management, investment strategies, and portfolio optimization.

To illustrate, let’s consider a common question in finance:

Question: What is CDS and how does it compare to a swap?

Answer:

• CDS: Credit Default Swap (CDS) is a financial derivative contract between two parties (buyer and seller) for insurance against default or credit risk associated with a bond or loan. The protection buyer pays a premium to the protection seller in exchange for the right to receive payment if a credit event occurs. Typically, the protection seller is a financial institution, while the protection buyer can be an investor or a bank.
• Swap: A swap is an agreement between two parties to exchange cash flows, typically involving interest payments or principal at a future date. Common types of swaps include interest rate swaps, currency swaps, and commodity swaps.

CDS differs from swaps as it focuses specifically on credit risk protection, providing insurance against default events. In contrast, swaps involve the exchange of cash flows, often related to interest rates, currencies, or commodities, without directly addressing credit risk.

Feel free to explore the model and see if it aligns with your organization’s needs and objectives. Remember to approach its use with caution and consider consulting with financial experts when making significant decisions.

As we navigate the complexities of the financial world together, let’s remain humble in our pursuit of knowledge and improvement.

In this post, they implement a GPT from scratch in just 60 lines of numpy. We’ll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text.

Note:

• This post assumes familiarity with Python, NumPy, and some basic experience training neural networks.
• This implementation is missing tons of features on purpose to keep it as simple as possible while remaining complete. The goal is to provide a simple yet complete technical introduction to the GPT as an educational tool.
• The GPT architecture is just one small part of what makes LLMs what they are today.^[1].
• All the code for this blog post can be found at github.com/jaymody/picoGPT.
• Hacker news thread
• Chinese translation

Whether you’re tackling a small-scale project or large datasets, PyOD offers a range of algorithms to suit your needs.

• For time-series outlier detection, please use TODS.
• For graph outlier detection, please use PyGOD.
• Performance Comparison & Datasets: We have a 45-page, the most comprehensive anomaly detection benchmark paper. The fully open-sourced ADBench compares 30 anomaly detection algorithms on 57 benchmark datasets.
• Learn more about anomaly detection @ Anomaly Detection Resources
• PyOD on Distributed Systems: you could also run PyOD on databricks.

https://pyod.readthedocs.io/en/latest/