Instructions to use zakria/NLP_Project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zakria/NLP_Project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="zakria/NLP_Project")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("zakria/NLP_Project") model = AutoModelForCTC.from_pretrained("zakria/NLP_Project", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from zakria/NLP_Project: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/zakria/NLP_Project/resolve/main/training_args.bin
- Command line
-
hf download hf://zakria/NLP_Project/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/zakria/NLP_Project/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 7d05ea445fe293c7faf79eb0e8f0174ef312f2b93929c595ca6f329e3eb723e6
- Size of remote file:
- 2.99 kB
- SHA256:
- 035f33cee82adb40a2bb937b7027837e1392f3c83446f3ee617372cefb318378
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