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ElixirConf 2023 - Toran Billups - Fine-tuning language models with Axon
Fine-tune language models with Axon for improved performance and task adaptation, learn how to leverage labeled data, and discover the benefits of Elixir's simplicity and flexibility for building machine learning models.
- Fine-tuning language models with Axon can help improve performance and adapt to specific tasks.
- Developing a diverse set of labeled data is crucial for training language models.
- Softmax normalization helps to reduce the dimensionality of the output space.
- Labeled data can be used to fine-tune pre-trained models for specific tasks.
- Axon provides an easy-to-use interface for fine-tuning language models.
- Elixir is a suitable language for building machine learning models due to its simplicity and flexibility.
- Fine-tuning requires a deep understanding of the data and the task.
- BERT and Roberta are popular pre-trained language models that can be fine-tuned for specific tasks.
- Fine-tuning can lead to improved performance and accuracy.
- Underfitting and overfitting are common issues when fine-tuning language models.
- Hyperparameters such as batch size, learning rate, and number of epochs need to be adjusted when fine-tuning.
- Increasing the batch size can help improve training efficiency.
- Fine-tuning requires a good balance between exploration and exploitation.
- Elixir’s Axon library provides a simple and easy-to-use interface for fine-tuning language models.
- Pre-processing and feature engineering are crucial steps in fine-tuning language models.
- Axon’s fine-tuning interface allows for adjustable hyperparameters and dynamic loading of models.
- Fine-tuning can lead to better model performance and adaptation to specific tasks.