Instructions to use snigdha05/DataSynthis_ML_JobTask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use snigdha05/DataSynthis_ML_JobTask 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://snigdha05/DataSynthis_ML_JobTask") - Notebooks
- Google Colab
- Kaggle
Download lstm_model.keras from snigdha05/DataSynthis_ML_JobTask: direct link, hf CLI and curl.
- Browser
- Download file 200 kB
-
https://huggingface.co/snigdha05/DataSynthis_ML_JobTask/resolve/main/lstm_model.keras
- Command line
-
hf download hf://snigdha05/DataSynthis_ML_JobTask/lstm_model.keras
-
curl -L -o lstm_model.keras https://huggingface.co/snigdha05/DataSynthis_ML_JobTask/resolve/main/lstm_model.keras
200 kB
- Xet hash:
- 4bdf6d610a7ff7ec6ac8738fbe138295ff1ebdb441be92e96b9d11a92bfa9607
- Size of remote file:
- 200 kB
- SHA256:
- beeea7d99a0d48580bdf23f422c4ea144fab316ac0509d15bbb9aa1ae43fb815
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