Instructions to use roscazo/DISO_bsc_test16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roscazo/DISO_bsc_test16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="roscazo/DISO_bsc_test16")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("roscazo/DISO_bsc_test16") model = AutoModelForTokenClassification.from_pretrained("roscazo/DISO_bsc_test16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from roscazo/DISO_bsc_test16: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/roscazo/DISO_bsc_test16/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://roscazo/DISO_bsc_test16/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/roscazo/DISO_bsc_test16/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- ff2b142218ced4ffc98c8135946fdaca12cfa4ded7c9d1958a53a6b09fe87e63
- Size of remote file:
- 496 MB
- SHA256:
- c5ee7a5176bcacbe62d9d79540f28b5849a7cf90afe4a7379d2496e2bf750b09
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.