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 tf_model.h5 from ydshieh/tiny-random-DebertaForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 443 kB
-
https://huggingface.co/ydshieh/tiny-random-DebertaForTokenClassification/resolve/refs%2Fpr%2F3/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-DebertaForTokenClassification@refs/pr/3/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-DebertaForTokenClassification/resolve/refs%2Fpr%2F3/tf_model.h5
443 kB
- Xet hash:
- e9279040956b0223dbd5ca545ab6da2e37e8fae1bd13b3f1cbe0216f28fc0cc9
- Size of remote file:
- 443 kB
- SHA256:
- f0bf5dbcc13034e5d4e8aefda802909e919f836dabc72cec3d03f1784b049cc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.