Instructions to use Jurabek/language_identifications with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use Jurabek/language_identifications with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("Jurabek/language_identifications") - Notebooks
- Google Colab
- Kaggle
Download keras_metadata.pb from Jurabek/language_identifications: direct link, hf CLI and curl.
- Browser
- Download file 8.51 kB
-
https://huggingface.co/Jurabek/language_identifications/resolve/main/keras_metadata.pb
- Command line
-
hf download hf://Jurabek/language_identifications/keras_metadata.pb
-
curl -L -o keras_metadata.pb https://huggingface.co/Jurabek/language_identifications/resolve/main/keras_metadata.pb
8.51 kB
- Xet hash:
- 67867c5f53aec8625c8dba178a2b0a2fb008f2a78c251340ed00cb67ec32883c
- Size of remote file:
- 8.51 kB
- SHA256:
- 100488d663b105f13a3cb912a8f7f6a7f9f5deb32f3b1b738fcb1185b2210605
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.