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:
- 971e4de1a7a6dad7ac0564d928bf1862bfa3a917c60131f32b0601e71acbd15f
- Size of remote file:
- 16.6 MB
- SHA256:
- a9e601d4073280aaaebc48667222995da9ebb2af93301b20553b44cfe8dc67a1
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