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 training_args.bin from parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5/resolve/main/training_args.bin
- Command line
-
hf download hf://parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/parameterlab/apricot_binary_trivia_qa_deberta-v3-base_for_vicuna-7b-v1.5/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- 13ac1366a224f663c6695a305e7022df08deeb384e3a2cde21a086a381350182
- Size of remote file:
- 4.22 kB
- SHA256:
- 7d3bdb8c62b81e13cee503021f0efdec7518a7e796af35f148771bfbe93ba294
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