As technology continues to advance, language models have become increasingly popular in the legal industry. In particular, the FILAC language model has been used for legal clause analysis, which involves identifying the key clauses in legal documents. However, like many machine learning models, FILAC has its limitations, and improvements were necessary to achieve better results.

This is where the 1000ML team came in. By redeveloping the classification system and using unsupervised clustering techniques, they were able to significantly improve the clause extraction results from the FILAC language model. In this article, we will dive into the steps they took to achieve these results and explain how they used BERT, one of the most popular language models, in the process.

Classification is a crucial component of language models, as it helps the model understand the context and purpose of the text it is analyzing. To improve FILAC’s classification system, the 1000ML team looked at how decision summaries were generated using FILAC and identified ways to improve on the existing methods. One of the key techniques they used was unsupervised clustering, which involves grouping similar items based on their attributes.

With unsupervised clustering, the team was able to cluster different clauses and the intent behind them in a way that was more efficient and suited to their workload. The technology most used for generating these clause embeddings and representations is BERT, a language model that uses Bidirectional Encoders Representations for Transformers. BERT is highly versatile and can be used for a wide range of text-based tasks, including sequence labeling, text classification, and question-answering.

However, while BERT is a powerful language model, it is not specifically designed for legal documents or clause extraction. To address this, the 1000ML team pre-trained BERT on a dataset of legal documents to improve its understanding of legal language and clause extraction. This pre-training process involved feeding BERT legal documents so that it could better understand the specific nuances and contexts of legal language.

After pre-training BERT, the team was able to cluster the clauses using the unsupervised clustering technique. The result was a better classification system that was specifically designed for legal documents and more efficient at extracting clauses.

In conclusion, the 1000ML team was able to improve the clause extraction results from the FILAC language model by redeveloping the classification system and using unsupervised clustering techniques. By pre-training BERT on a dataset of legal documents, they were able to improve its understanding of legal language and develop a better classification system specifically tailored for legal documents. As technology continues to evolve, we can expect to see more advancements in language models and their applications in the legal industry.