Instructions to use ashrafnsu/DataSynthis_Job_task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ashrafnsu/DataSynthis_Job_task 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://ashrafnsu/DataSynthis_Job_task") - Notebooks
- Google Colab
- Kaggle
Download deepfm_model.keras from ashrafnsu/DataSynthis_Job_task: direct link, hf CLI and curl.
- Browser
- Download file 9.49 MB
-
https://huggingface.co/ashrafnsu/DataSynthis_Job_task/resolve/main/deepfm_model.keras
- Command line
-
hf download hf://ashrafnsu/DataSynthis_Job_task/deepfm_model.keras
-
curl -L -o deepfm_model.keras https://huggingface.co/ashrafnsu/DataSynthis_Job_task/resolve/main/deepfm_model.keras
9.49 MB
- Xet hash:
- 66aeb32e2f80cb11ae9c8a54e7e06c3c5288138d16156f27ba732579a587ca0b
- Size of remote file:
- 9.49 MB
- SHA256:
- 9780f7019f59751a6d9a662990d53cb02e5ac618ff8ec833ff5c26025bc38434
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.