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%2F1/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-DebertaForTokenClassification@refs/pr/1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-DebertaForTokenClassification/resolve/refs%2Fpr%2F1/tf_model.h5
443 kB
- Xet hash:
- ece752ef3f7b96f8cb18eefac6e84a549ae99c9e91872b7e670fae9909011503
- Size of remote file:
- 443 kB
- SHA256:
- 139bd7fbec74b6001fa505d6176ce4a9a096d5cf32db14b9d1b68bc7b4c564ce
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