Instructions to use hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a29177e2e055a2fca3b498359fd9c649daf3189470b0d947b29272b1b2363baa
- Size of remote file:
- 16.8 MB
- SHA256:
- 3e6829c4e8b0cefb68c357b95f00fce4ddc3f01bd6bdc76c152f1235d829aefa
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