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 pytorch_model.bin from roscazo/DISO_SINAI_test1: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/roscazo/DISO_SINAI_test1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://roscazo/DISO_SINAI_test1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/roscazo/DISO_SINAI_test1/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- 3403e33da39ac83842aa35a24aa373f72a0ad9ca0c8561b53d1cbc5be24c62a2
- Size of remote file:
- 496 MB
- SHA256:
- 36af1506c00a0bb21a8dc5a7baba68ab871ea934b31b652c986c297e294d617f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.