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KerasHub: Pretrained Models /
KerasHub API documentation
KerasHub API documentation
KerasHub is a toolbox of modular building blocks ranging from pretrained state-of-the-art models, to low-level Transformer Encoder layers.
- Modeling API: Base classes that can be used for most high-level tasks
using pretrained models. Note that you can use the
from_preset()constructor on a base class to instantiate a model of the correct subclass. - Model Architectures: Implementations of all pretrained model architectures shipped with KerasHub.
- Tokenizers: Layer implementations of tokenization routines for text-based models.
- Preprocessing Layers: Layers for building preprocessing pipelines that handle audio, text, and image input.
- Modeling Layers: Common modeling layers used by pretrained model architectures.
- Samplers: An API for controlling generative text sampling.
- Metrics: Metrics useful for audio, text, and image workflows.
Modeling API
- Backbone
- CausalLM
- CausalLMPreprocessor
- ImageClassifier
- ImageClassifierPreprocessor
- ImageToImage
- ImageSegmenter
- Inpaint
- MaskedLM
- MaskedLMPreprocessor
- Preprocessor
- Seq2SeqLM
- Seq2SeqLMPreprocessor
- Task
- TextClassifier
- TextClassifierPreprocessor
- TextToImage
- upload_preset
Model Architectures
- Albert
- Bart
- Bert
- Bloom
- DebertaV3
- DeepLabV3
- DenseNet
- DistilBert
- Electra
- Falcon
- FNet
- Gemma
- GPT2
- Llama
- Llama3
- Mistral
- MiT
- OPT
- PaliGemma
- Phi3
- ResNet
- Roberta
- Segment Anything Model
- Stable Diffusion 3
- T5
- VGG
- ViTDet
- Whisper
- XLMRoberta
Tokenizers
- Tokenizer
- WordPieceTokenizer
- SentencePieceTokenizer
- BytePairTokenizer
- ByteTokenizer
- UnicodeCodepointTokenizer
- compute_word_piece_vocabulary function
- compute_sentence_piece_proto function
Preprocessing Layers
- AudioConverter layer
- ImageConverter layer
- StartEndPacker layer
- MultiSegmentPacker layer
- RandomSwap layer
- RandomDeletion layer
- MaskedLMMaskGenerator layer
Modeling Layers
- TransformerEncoder layer
- TransformerDecoder layer
- FNetEncoder layer
- PositionEmbedding layer
- RotaryEmbedding layer
- SinePositionEncoding layer
- ReversibleEmbedding layer
- TokenAndPositionEmbedding layer
- AlibiBias layer
- MaskedLMHead layer
- CachedMultiHeadAttention layer
Samplers
- Sampler base class
- BeamSampler
- ContrastiveSampler
- GreedySampler
- RandomSampler
- TopKSampler
- TopPSampler