Instructions to use keras/parseq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/parseq with KerasHub:
import keras_hub # Load CausalLM model (optional: use half precision for inference) causal_lm = keras_hub.models.CausalLM.from_preset("hf://keras/parseq", dtype="bfloat16") causal_lm.compile(sampler="greedy") # (optional) specify a sampler # Generate text causal_lm.generate("Keras: deep learning for", max_length=64)import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/parseq") - Keras
How to use keras/parseq with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/parseq") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: keras-hub
pipeline_tag: text-generation
Model Overview
Permuted autoregressive sequence (PARSeq) model for Scene Text Recognition (STR)
This model is designed for Scene Text Recognition (STR), which involves reading text from images. You can load and use the pre-trained PARSeq model with the following Python code snippet. The model takes an image as input and outputs the recognized text.
Links
- PARSeq Quickstart Notebook
- PARSeq API Documentation
- PARSeq Model Card
- KerasHub Beginner Guide
- KerasHub Model Publishing Guide
Installation
Keras and KerasHub can be installed with:
pip install -U -q keras-hub
pip install -U -q keras>=3
Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.
Presets
The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name | Parameters | Description |
|---|---|---|
parseq |
23.8M | 23 million parameter base model. |