Level Up Your LLMs, NLP or CV Models

Visualize image data across experiments and over time for tasks such as object detection, segmentation, and reconstruction etc. Easily compare input samples over the course of your computer vision training runs, evaluate and compare the quality of outputs from object detection, segmentation and generative models.

Build the best-performing LLMs with Comet’s powerful exploration and debugging tools. Dive into the Prompt Playground, where Prompt Engineers can iterate rapidly with various Prompt Templates, gauging their impact on different contexts. Keep tabs on your progress with the comprehensive Prompt History feature, enabling you to understand and debug ML product behavior based on LLMs. And with our Prompt Usage Tracker, gain granular insights into your usage at a project and experiment level, ensuring efficient utilization of paid APIs.

Logging text data during the training process can aid in error tracking and debugging. For instance, if your model isn’t performing as expected, you can use the logged text to identify problematic data samples or analyze patterns that the model might be misinterpreting.

NLP models can often feel like black boxes, where it’s hard to understand why they’re making the predictions they are. Visualizing embeddings can provide a more intuitive way to understand what the model has learned and how different words are being understood and related to each other. High-dimensional data can be difficult to work with and interpret. Techniques like t-SNE or PCA, which are often used in embedding visualizations, reduce the dimensionality of this data, making it more manageable and easier to understand.

By aggregating and displaying data across experiments, the Data Panel enables you to check for consistency and maintain a uniform view of your data, which is critical when comparing models trained under different settings or at different times.

Interactive Confusion matrices can help you quickly identify if your model is struggling with class imbalance in your data. This is especially important in NLP, where certain categories might be underrepresented. By analyzing the misclassifications, you can identify which classes your model is confusing with one another. This can guide you to engineer more discriminative features or choose a model that can better separate these classes.