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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7968557ec7bb5c87a7d37553bf4dab53c358b95dd5ec12e3590fcabea8cf506f
- Size of remote file:
- 5.91 kB
- SHA256:
- 3ba7d56b35ec63adf673fde5fcc479fdb8eae589bffb8ebf33a3e52e55630038
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