Instructions to use keras/all_mpnet_base_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/all_mpnet_base_v2 with KerasHub:
import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/all_mpnet_base_v2")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/all_mpnet_base_v2") - Keras
How to use keras/all_mpnet_base_v2 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/all_mpnet_base_v2") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from keras/all_mpnet_base_v2: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/keras/all_mpnet_base_v2/resolve/main/model.weights.h5
- Command line
-
hf download hf://keras/all_mpnet_base_v2/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/keras/all_mpnet_base_v2/resolve/main/model.weights.h5
436 MB
- Xet hash:
- eb9d53e499dca63c863ffeedcd7b9be63b54a3c280c061d8f1e461e228204aac
- Size of remote file:
- 436 MB
- SHA256:
- 3ed3595ab8b76bd0c0db06878fd81bd9c6045532bd944ee8fd54f2eaed69e7b0
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