v4.26.0: Generation configs, image processors, backbones and plenty of new models!
GenerationConfig
The generate method has multiple arguments whose defaults were lying in the model config. We have now decoupled these in a separate generation config, which makes it easier to store different sets of parameters for a given model, with different generation strategies. While we will keep supporting generate arguments in the model configuration for the foreseeable future, it is now recommended to use a generation config. You can learn more about its uses here and its documentation here.
Generate: use GenerationConfig as the basis for .generate() parametrization by @gante in #20388
Generate: TF uses GenerationConfig as the basis for .generate() parametrization by @gante in #20994
Generate: FLAX uses GenerationConfig as the basis for .generate() parametrization by @gante in #21007
ImageProcessor
In the vision integration, all feature extractor classes have been deprecated to be renamed to ImageProcessor. The old feature extractors will be fully removed in version 5 of Transformers and new vision models will only implement the ImageProcessor class, so be sure to switch your code to this new name sooner rather than later!
Add deprecation warning when image FE instantiated by @amyeroberts in #20427
AltCLIP is a variant of CLIP obtained by switching the text encoder with a pretrained multilingual text encoder (XLM-Roberta). It has very close performances with CLIP on almost all tasks, and extends the original CLIP’s capabilities to multilingual understanding.
BLIP is a model that is able to perform various multi-modal tasks including visual question answering, image-text retrieval (image-text matching) and image captioning.
BioGPT is a domain-specific generative pre-trained Transformer language model for biomedical text generation and mining. BioGPT follows the Transformer language model backbone, and is pre-trained on 15M PubMed abstracts from scratch.
BiT is a simple recipe for scaling up pre-training of ResNet-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
EfficientFormer proposes a dimension-consistent pure transformer that can be run on mobile devices for dense prediction tasks like image classification, object detection and semantic segmentation.
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Bumps transformers from 4.19.2 to 4.26.0.
Release notes
Sourced from transformers's releases.
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Commits
820c46a
Hotifx remove tuple for git config image processor. (#21278)a280cdd
Fix MaskFormerImageProcessor.post_process_instance_segmentation (#21256)baf0df1
Release: v4.26.0fd5cdae
Models docstring (#21225)9e86c4e
Supported pipeline tasks update (#21268)d8415ba
[Whisper] fix all issues with unk token (#21250)c18b4fb
Add class properties with warnings (#21195)b80b221
[ci-daily] Fix pipeline tests (#21257)275ad9d
Add: TensorFlow example for semantic segmentation task guide (#21223)2218dac
Notebook examples grouping and update (#21265)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
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