v3.2.0

Based on transformers v4.26.1

New

New model integrations

• Add BEiT integration (@jannik-brinkmann via #428, #439)
• Add GPT-J integration (@ChiragBSavani via #426)
• Add CLIP integration (@calpt via #483)
• Add ALBERT integration (@lenglaender via #488)
• Add BertGeneration (@hSterz via #480)

Misc

• Add support for adapter configuration strings (@calpt via #465, #486)
  This enables you to easily configure adapter configs. To create a Pfeiffer adapter with reduction factor 16 you can know use pfeiffer[reduction_factor=16]. Especially for experiments using different hyperparameters or the example scripts, this can come in handy. Learn more
• Add for Stack, Parallel & BatchSplit composition to prefix tuning (@calpt via #476)
  In previous adapter-transformers versions, you could combine multiple bottleneck adapters. You could use them in parallel or stack them. Now, this is also possible for prefix-tuning adapters. Add multiple prefixes to the same model to combine the functionality of multiple adapters (Stack) or perform several tasks simultaneously (Parallel, BatchSplit) Learn more
• Enable parallel sequence generation with adapters (@calpt via #436)

Changed

• Removal of the MultiLingAdapterArguments class. Use the AdapterArguments class and setup_adapter_training method instead. Learn more.
• Upgrade of underlying transformers version to 4.26.1 (@calpt via #455, @hSterz via #503)

Fixed

• Fixes for GLUE & dependency parsing example script (@calpt via #430, #454)
• Fix access to shared parameters of compacter (e.g. during sequence generation) (@calpt via #440)
• Fix reference to adapter configs in T5EncoderModel (@calpt via #437)
• Fix DeBERTa prefix tuning with enabled relative attention (@calpt via #451)
• Fix gating for prefix tuning layers (@calpt via #471)
• Fix input to T5 adapter layers (@calpt via #479)
• Fix AdapterTrainer hyperparameter tuning (@dtuit via #482)
• Move loading best adapter to AdapterTrainer class (@MaBeHen via #487)
• Make HuggingFace Hub Mixin work with newer utilities (@Helw150 via #473)
• Only compute fusion reg loss if fusion layer is trained (@calpt via #505)