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-MeanPooling-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saiteki-kai/QA-DeBERTa-MeanPooling-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="saiteki-kai/QA-DeBERTa-MeanPooling-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DebertaResponseMeanPooling tokenizer = AutoTokenizer.from_pretrained("saiteki-kai/QA-DeBERTa-MeanPooling-binary") model = DebertaResponseMeanPooling.from_pretrained("saiteki-kai/QA-DeBERTa-MeanPooling-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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