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 metadata.json from keras/all_mpnet_base_v2: direct link, hf CLI and curl.
- Browser
- Download file 193 Bytes
-
https://huggingface.co/keras/all_mpnet_base_v2/resolve/main/metadata.json
- Command line
-
hf download hf://keras/all_mpnet_base_v2/metadata.json
-
curl -L -o metadata.json https://huggingface.co/keras/all_mpnet_base_v2/resolve/main/metadata.json
193 Bytes
| { | |
| "keras_version": "3.15.1", | |
| "keras_hub_version": "0.33.0.dev0", | |
| "parameter_count": 108894336, | |
| "date_saved": "2026-10-31@00:13:01", | |
| "tasks": [ | |
| "TextEmbedder" | |
| ] | |
| } |