Contract review is a repetitive and tedious job because junior law firm associates always need to identify the same data points in any given contract: What is effective date of the contract? What are the renewal terms? Who are the parties involved in this contract? Machine learning models can learn to automatically extract and identify key values from contracts, thus saving hundreds of hours of manual labour.
Dataleon is a no-code AI platform which provides the best ML tools for automation and data processing. It can facilitate the process of training a model by using powerful machine learning algorithms. Before training and testing a model, it is crucial to prepare a significant volume of high-quality data. If the data isn`t clean, doesn`t make sense or has a lot of missing information, it is important to comb through the data, label and prepare it before training the model.
Creating a preset.
When just starting out with Dataleon, you will be asked to create a preset of your data. Presets is the part in which data is collected, prepared before a project creation and finally cleaned out. It may be regarded as a source of data which will be later used for labeling.
After uploading the images within one preset, you will have a possibility to check the images for duplicate or similar files. For this the platform suggests a Scan feature. Once you click Scan, the system will remove duplicate or similar files automatically.
You can also view all the details about every image which you have uploaded. Just click on the image you are interested in and view all its details. You can crop the image, if you are not satisfied with its background, and you can also delete the image.
Creating a project.
Once you have uploaded and cleaned out your data, you may proceed with a project creation. Projects part is designed for real data labeling, synthetic data generation, and data classification and detection.
If you are certain that the data is accurate, relevant and sufficient, you can use our out-of-the-box Labeling Editor to label your data.
The large amount of unlabeled data is a growing problem in machine learning. This leaves data scientists with more data than they are capable of analyzing. That’s where active learning comes in. Active learning is the subset of machine learning in which a learning algorithm can query a user interactively to label data with the desired outputs. You can apply Active learning in the Labeling Editor.
The quantity and quality of your training data determine the accuracy of the performance of your machine learning model. Therefore, lack of relevant and accurate data can be another great problem in machine learning. Hopefully, it can be easily resolved thanks to our Scene Editor, i.e. an interface used for data generation.
Creating and training a model.
After you data is collected and cleaned out, you can pass on to the Knowledge Vision section. In this part of the platform you can train your model with your data (either personal or generated in Datalabs) and visualize the result.
At Dataleon we are making it possible to train a model to process a contract. If you are in need of such a model, just let us know.