Instructions to use zakria/Project_NLP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zakria/Project_NLP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="zakria/Project_NLP")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("zakria/Project_NLP") model = AutoModelForCTC.from_pretrained("zakria/Project_NLP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from zakria/Project_NLP: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/zakria/Project_NLP/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://zakria/Project_NLP@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/zakria/Project_NLP/resolve/refs%2Fpr%2F1/training_args.bin
2.99 kB
- Xet hash:
- 058bb09eef54374e2d3862d282b1293536c5efd4284834343a4794123361629f
- Size of remote file:
- 2.99 kB
- SHA256:
- 12295603a122f4c0d1f12e06bd6130a3ac46ea7398e25768041ae2542207fb3d
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