Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use vempaliakhil/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vempaliakhil/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vempaliakhil/outputs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vempaliakhil/outputs") model = AutoModelForSequenceClassification.from_pretrained("vempaliakhil/outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4cd66dc51b82529fc0f452f85634e7420427b8000e5ffc7236e8a74b86bdea1
- Size of remote file:
- 4.92 kB
- SHA256:
- d2875c03fe66ed518e35891c390e8844529f35a0e95da151e21348322fa3f35d
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