Instructions to use roscazo/DISO_SINAI_test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roscazo/DISO_SINAI_test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="roscazo/DISO_SINAI_test1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("roscazo/DISO_SINAI_test1") model = AutoModelForTokenClassification.from_pretrained("roscazo/DISO_SINAI_test1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from roscazo/DISO_SINAI_test1: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/roscazo/DISO_SINAI_test1/resolve/main/training_args.bin
- Command line
-
hf download hf://roscazo/DISO_SINAI_test1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/roscazo/DISO_SINAI_test1/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 08a5695d61b492225d01155936cd6e79685a0313c82ea68ea9450862d28869cd
- Size of remote file:
- 3.39 kB
- SHA256:
- 8278e4aa80f1462381830dc4fa2ed220eebfb58c3718b0c433b0e13a5d71c34b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.