Instructions to use ydshieh/tiny-random-DebertaForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-DebertaForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ydshieh/tiny-random-DebertaForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-DebertaForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("ydshieh/tiny-random-DebertaForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ydshieh/tiny-random-DebertaForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 361 kB
-
https://huggingface.co/ydshieh/tiny-random-DebertaForTokenClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-DebertaForTokenClassification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-DebertaForTokenClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
361 kB
- Xet hash:
- 1bf241c234c3a61a7fb8d48ac01fea982d1e34f0086d7a6a50c874a3230204ae
- Size of remote file:
- 361 kB
- SHA256:
- 0185ecc99c84a8f5ab51c3a4d5b17fec55a43a81f96b14943915d8783a11de58
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.