Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use ankitkupadhyay/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ankitkupadhyay/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ankitkupadhyay/outputs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ankitkupadhyay/outputs") model = AutoModelForSequenceClassification.from_pretrained("ankitkupadhyay/outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26e2e8ca06c9496dabc6caa465bfd4762cb34e2591b17e21c1d92e2f0e76d07c
- Size of remote file:
- 3.18 kB
- SHA256:
- eae408b7bb33253ae23797f0534f39b856e63dbdc398f2813d74b74652345068
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