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/main/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-DebertaForTokenClassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-DebertaForTokenClassification/resolve/main/pytorch_model.bin
361 kB
- Xet hash:
- 61d74ab98e1b94ae7e5b5f5cf99e335279defc127d48923902cf7be0e4db9652
- Size of remote file:
- 361 kB
- SHA256:
- f50e09dabfe313de1be45b838df9a3b3fd2049c2dd825ec9e361209ce4ad7a82
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