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
Download pytorch_model.bin from parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- be5afe62a18fc62492b5a7fe47b74daced2f27e63308a14d64b4bd59ffb2a2b9
- Size of remote file:
- 738 MB
- SHA256:
- 69ec254a03ab565362c440b4f653083a3beefdd41f8bd2692d601460482f1f7d
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