The Leading Enterprise MLOps Platform for Innovative ML Teams
A Unified Platform for Debugging, Managing and Monitoring Your NLP Models
- Leverage the Text Tab to record text data throughout the training phase.
- This capability facilitates efficient error tracking and debugging.
- Should your model’s performance falter, the logged text can be utilized to spot troublesome data samples or decipher patterns that the model could be misconstruing.
- 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.
- Techniques like t-SNE or PCA used in embedding visualizations reduce the dimensionality of data, making it more manageable and easier to understand.
- Utilize the Data Panel to compile and showcase data from various experiments.
- Ensure uniformity and coherence in your data perspective.
- Make comparisons between models trained under diverse conditions or at different temporal intervals.
- Interactive Confusion matrices serve as a useful tool to promptly detect issues of class imbalance that your model may be facing.
- Through the analysis of misclassified instances, you can pinpoint classes that your model is wrongly associating with each other.
- This valuable insight can steer you towards crafting more distinguishing features, or selecting a model better equipped to segregate these classes.
- The Audio tab offers a combined audible and visual examination of the audio data incorporated in your training process.
- This tool proves especially beneficial for tasks related to speech recognition, music analysis, or any other audio-centric undertakings.
- With the ability to both listen to and view your audio data, you gain a deeper comprehension of your data’s attributes, potentially contributing to an enhancement in model performance.
NLP Integrations
Spacy
Spark NLP
HuggingFace
Scikit Learn
Keras
Pytorch
Tensorflow
Comet Resources
How to Build a Text Classification Model Using HuggingFace Transformers and Comet
Learn how to build your text classification model using Transformers (which includes a state-of-the-art pre-trained model) and how to utilize Comet to keep track of your model’s experiments.
