Text Classification
Transformers
PyTorch
Safetensors
deberta-v2
Generated from Trainer
calibration
uncertainty
Instructions to use parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5") model = AutoModelForSequenceClassification.from_pretrained("parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (5d0fb7dc9d951e10400f3db5583133aa24cbbe45)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- model.safetensors +3 -0
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