Text Classification
Transformers
Safetensors
deberta-v2
single_label_classification
question-answering
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use saiteki-kai/QA-DeBERTa-IGPooling-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saiteki-kai/QA-DeBERTa-IGPooling-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="saiteki-kai/QA-DeBERTa-IGPooling-binary")# Load model directly from transformers import AutoTokenizer, DebertaIntegratedGradientsWeightedPooling tokenizer = AutoTokenizer.from_pretrained("saiteki-kai/QA-DeBERTa-IGPooling-binary") model = DebertaIntegratedGradientsWeightedPooling.from_pretrained("saiteki-kai/QA-DeBERTa-IGPooling-binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82f0f49362b796a680d78c93cb98bf1a6a7ee9fc1ec5ea048859ad6cb5bfe384
- Size of remote file:
- 5.91 kB
- SHA256:
- 49b84f4e42e5847145007a9ca5c93ae98ba880b32eb66e1fc3152e1d9dc9cd97
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