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:
- 60f086b835151dca3f2126f1c38f3bf97a529bd8f78f2eb3d149445e9a4b3abe
- Size of remote file:
- 16.7 MB
- SHA256:
- 748f82bb0108c6df1f7b592f976c90e952899cc96c59dec9e738e47d7cc35fb4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.