v1.0.0

03.07.2025 - Version 1.0.0

Feature

• Adding an OutputManager class that can be customized by adding observers for easier integration with external
  tools such as MLFlow, WanB and tensorboard (the latter is already supported)
• Adding the autoeval module to biotrainer that enables evaluating protein language models on downstream tasks.
  Currently, a curated subset of the FLIP datasets is supported.
• Adding an improved CLI, including train, predict, convert (deprecated files) and autoeval commands
• Adding an InputValidator and InputValidationStep that validates the given input_file. Can be
  deactivated by setting validate_input to False in the config file.
• Adding LoRA finetuning via finetuning_config. Implementation is currently in beta state
  (some modes like auto_resume and ppi are not supported), but finetuning can already be applied for all protocols.
• Adding a random_embedder to calculate random embeddings as a baseline for predefined embedders.

Maintenance

• Replaced biopython dependency with custom read, write and filter functions
• Refactored the large trainer class into a pipeline with distinct steps for better readability, maintainability and
  customization
• Enforced bootstrapping for sanity checks
• Refactoring embedding_service to allow embedding computation as generator function. Embeddings are now directly
  stored in the h5 file after computation. Experiments show that this is about as efficient as the old batching approach,
  while allowing for better code readability.
• Adding PyPi release
• Adding official macOS support

Breaking

• Refactoring file input to a single input_file.
  sequence_file, labels_file and mask_file are no longer supported.
• Naming changes in the output file, documented in this issue: #137
• embedder_name and embeddings_file are no longer mutually exclusive. If an embeddings_file is provided,
  it will be used instead of calculating the embeddings
• Embeddings are now stored by hash in the result h5 file. The behaviour can be turned off for special use-cases
  in the compute_embeddings function by setting the store_by_hash flag to False. In that case, the original
  sequence id is now used (over a running integer) as h5 index. The sequence id also always saved in the original_id
  attribute of the h5 dataset.
• Ending support for Python 3.10, adding support for Python 3.12
• Migrating build and dependency system from poetry to uv for better performance