The Leading Enterprise MLOps Platform for Innovative ML Teams
A Unified Platform for Debugging, Managing and Monitoring Your NLP Models
Simplify Your NLP Debugging with Comet
- 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.
Stop Struggling to Interpret Your NLP Model Predictions
- 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.
Maintain Data Consistency Across NLP Experiments
- 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.
Resolve Class Imbalance Issues in Your NLP Models
- 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.
Empower Your Audio Projects with Advanced Audio Data Inspection
- 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.
