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 pytorch_model.bin from zakria/NLP_Project: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/zakria/NLP_Project/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zakria/NLP_Project/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zakria/NLP_Project/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- ba73c6babf55bc54a491dfe863aa0ff2aec6ac930ebaa4ef6c7aa7f111174f88
- Size of remote file:
- 378 MB
- SHA256:
- 3ff9d6e08935cc69b19b583479225444796f0a1db5754107a78360f48f45d166
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