Instructions to use ghadeermobasher/Model_imb_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghadeermobasher/Model_imb_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ghadeermobasher/Model_imb_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ghadeermobasher/Model_imb_1") model = AutoModelForTokenClassification.from_pretrained("ghadeermobasher/Model_imb_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ghadeermobasher/Model_imb_1: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/ghadeermobasher/Model_imb_1/resolve/main/training_args.bin
- Command line
-
hf download hf://ghadeermobasher/Model_imb_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ghadeermobasher/Model_imb_1/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- e1aa568dd886cfe3f7fe0d704c01f7046e2bb954c6cd14366aeb57e9b4ac1538
- Size of remote file:
- 2.8 kB
- SHA256:
- ab404a1ce9ba64f91eaa7142e3811187ed892b383b66ee64c03610963d74bfa0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.