Instructions to use hf-tiny-model-private/tiny-random-DebertaV2ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-DebertaV2ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-DebertaV2ForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ae80351adb7b90da479e9971ef550e8430df1251d208a971ca0edacc4156d45
- Size of remote file:
- 16.6 MB
- SHA256:
- 2f7c0ff81a0aba9bb1b672bef2fb0f721aaecedd3df544e5e28cfa90cfe0c3a3
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