Instructions to use roscazo/CTEBMSP_bsc_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roscazo/CTEBMSP_bsc_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="roscazo/CTEBMSP_bsc_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("roscazo/CTEBMSP_bsc_test") model = AutoModelForTokenClassification.from_pretrained("roscazo/CTEBMSP_bsc_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from roscazo/CTEBMSP_bsc_test: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/roscazo/CTEBMSP_bsc_test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://roscazo/CTEBMSP_bsc_test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/roscazo/CTEBMSP_bsc_test/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- 1eb7c3c4b04665e7e83a6a4a4903cec3b14ebc64107aa7fef41eb1c24f31bb0e
- Size of remote file:
- 496 MB
- SHA256:
- d0eee4cae873c97e11cf714261e6c9234ce02052633e75d623d0dd5f4c136f5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.