Instructions to use ghadeermobasher/Model_org with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghadeermobasher/Model_org with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ghadeermobasher/Model_org")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ghadeermobasher/Model_org") model = AutoModelForTokenClassification.from_pretrained("ghadeermobasher/Model_org", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ghadeermobasher/Model_org: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/ghadeermobasher/Model_org/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://ghadeermobasher/Model_org@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ghadeermobasher/Model_org/resolve/refs%2Fpr%2F1/training_args.bin
2.8 kB
- Xet hash:
- 3670c54977ee8bad865a0f94e6f50e8546d65e3e08006feccae6cee44cb7af6a
- Size of remote file:
- 2.8 kB
- SHA256:
- 0fb32b9d265a6f240eb214e772581246987adbd648b1f43ed023832168a846ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.