Instructions to use keras/bart_base_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bart_base_en with KerasHub:
import keras_hub # Create a Seq2SeqLM model task = keras_hub.models.Seq2SeqLM.from_preset("hf://keras/bart_base_en")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/bart_base_en") - Keras
How to use keras/bart_base_en 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/bart_base_en") - Notebooks
- Google Colab
- Kaggle
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- text-classification
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pipeline_tag: text-classification
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---
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## Model Overview
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BART encoder-decoder network.
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This class implements a Transformer-based encoder-decoder model as
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preprocessor=None,
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bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
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```
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- text-classification
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pipeline_tag: text-classification
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---
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### Model Overview
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BART encoder-decoder network.
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This class implements a Transformer-based encoder-decoder model as
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preprocessor=None,
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)
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bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
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```
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