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 pytorch_model.bin from zakria/Project_NLP: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/zakria/Project_NLP/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://zakria/Project_NLP@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zakria/Project_NLP/resolve/refs%2Fpr%2F1/pytorch_model.bin
378 MB
- Xet hash:
- 026a913969a1a267f307a5f1eb7666d7076213540b5e43b6211c9c8c1022d868
- Size of remote file:
- 378 MB
- SHA256:
- 6f58da5f2138e9d02509f45e83d26389dceda9ef927011b27c53c1668c7bcd49
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