Instructions to use hf-tiny-model-private/tiny-random-DebertaForSequenceClassification 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-DebertaForSequenceClassification 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-DebertaForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-DebertaForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f986361c59446356a3e9a66f79578fa9d2f4900cd3c367dbeb0062dcdd44b150
- Size of remote file:
- 367 kB
- SHA256:
- fde6b4e2b2216a9e85ace4a1afd452f1438955e3a729cd524c3e16bf9b4b583d
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