Instructions to use mkbackup/final_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkbackup/final_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mkbackup/final_model")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mkbackup/final_model") model = AutoModelForSpeechSeq2Seq.from_pretrained("mkbackup/final_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b914ad6be931b5689f70e5235bee7db621dd84a167e0fe37b69259a5da1174ad
- Size of remote file:
- 967 MB
- SHA256:
- 244175db89413f3b75c767659bfd5a63dab20f627e93c473aa69dfc9d37bea5f
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