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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 552f1a3c9e9038903c33a35eebd3d016d60629a3a5579529da73a4555867dc5d
- Size of remote file:
- 16.7 MB
- SHA256:
- a250c4766cb24fd895fe323c43a1e5c23816597cf036b9bf8f48fb1dcc7a433b
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