Instructions to use keras/modern_bert_large_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/modern_bert_large_en with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/modern_bert_large_en", num_classes=2, ) # Fine-tune text_classifier.fit(x=["Thilling adventure!", "Total snoozefest."], y=[1, 0]) # Classify text text_classifier.predict(["Not my cup of tea."])import keras_hub # Create a MaskedLM model task = keras_hub.models.MaskedLM.from_preset("hf://keras/modern_bert_large_en")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/modern_bert_large_en") - Keras
How to use keras/modern_bert_large_en with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://keras/modern_bert_large_en") - Notebooks
- Google Colab
- Kaggle
Download task.weights.h5 from keras/modern_bert_large_en: direct link, hf CLI and curl.
- Browser
- Download file 4.42 MB
-
https://huggingface.co/keras/modern_bert_large_en/resolve/main/task.weights.h5
- Command line
-
hf download hf://keras/modern_bert_large_en/task.weights.h5
-
curl -L -o task.weights.h5 https://huggingface.co/keras/modern_bert_large_en/resolve/main/task.weights.h5
4.42 MB
- Xet hash:
- 2e4c385df51d0a62259d74d3d9aa051d995c59dc3d966e67be5e7a4c73458925
- Size of remote file:
- 4.42 MB
- SHA256:
- 0b16acbcfddc8b1fe96e1f15f382e1e47a132d7249e153259c78cbd47cb02df6
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