Instructions to use hf-tiny-model-private/tiny-random-DebertaV2ForSequenceClassification 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-DebertaV2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-DebertaV2ForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4b15e9995eedb4d6d72706862eb57230cc1ead5fd908cb75c7e83bd479a7c38
- Size of remote file:
- 16.6 MB
- SHA256:
- 2a72b0e9952c95e69d56d902e001d21f94bdda8e5f5519d53cd2909072fdadd0
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