Instructions to use hf-internal-testing/tiny-random-DebertaV2ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DebertaV2ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-DebertaV2ForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e043f67f1f963a3f28092c15f97004a2b37077ac4e09770ed09c9ab2441bd4e
- Size of remote file:
- 16.8 MB
- SHA256:
- a65c6b0d009efd14dfb93dbf3f7d2ab3fe0ee7c0aebac0ff07526407e2da73f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.