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:
- 6bd079d8585fbcdbf47cb3a534f888b5aaa32b8994301f5c47573e6cf825d7da
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.