pypi transformers 4.26.0
v4.26.0: Generation configs, image processors, backbones and plenty of new models!
on Python PyPI

latest releases: 4.46.2, 4.46.1, 4.46.0...
22 months ago

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!

New models

AltCLIP

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

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

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

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

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.

GIT

GIT is a decoder-only Transformer that leverages CLIP’s vision encoder to condition the model on vision inputs besides text. The model obtains state-of-the-art results on image captioning and visual question answering benchmarks.

GPT-sw3

GPT-Sw3 is a collection of large decoder-only pretrained transformer language models that were developed by AI Sweden in collaboration with RISE and the WASP WARA for Media and Language. GPT-Sw3 has been trained on a dataset containing 320B tokens in Swedish, Norwegian, Danish, Icelandic, English, and programming code. The model was pretrained using a causal language modeling (CLM) objective utilizing the NeMo Megatron GPT implementation.

Graphormer

Graphormer is a Graph Transformer model, modified to allow computations on graphs instead of text sequences by generating embeddings and features of interest during preprocessign and collation, then using a modified attention.

Mask2Former

Mask2Former is a unified framework for panoptic, instance and semantic segmentation and features significant performance and efficiency improvements over MaskFormer.

OneFormer

OneFormer is a universal image segmentation framework that can be trained on a single panoptic dataset to perform semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference.

Roberta prelayernorm

The RoBERTa-PreLayerNorm model is identical to RoBERTa but uses the --encoder-normalize-before flag in fairseq.

Swin2SR

Swin2R improves the SwinIR model by incorporating Swin Transformer v2 layers which mitigates issues such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data.

TimeSformer

TimeSformer is the first video transformer. It inspired many transformer based video understanding and classification papers.

UPerNet

UPerNet is a general framework to effectively segment a wide range of concepts from images, leveraging any vision backbone like ConvNeXt or Swin.

Vit Hybrid

ViT hybrid is a slight variant of the plain Vision Transformer, by leveraging a convolutional backbone (specifically, BiT) whose features are used as initial “tokens” for the Transformer. It’s the first architecture that attains similar results to familiar convolutional architectures.

Backbones

Breaking a bit the one model per file policy, we introduce backbones (mainly for vision models) which can then be re-used in more complex models like DETR, MaskFormer, Mask2Former etc.

Bugfixes and improvements

Significant community contributions

The following contributors have made significant changes to the library over the last release:

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