Text Classification
Transformers
PyTorch
TensorBoard
deberta
Generated from Trainer
Eval Results (legacy)
Instructions to use Tomor0720/deberta-large-finetuned-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tomor0720/deberta-large-finetuned-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tomor0720/deberta-large-finetuned-qqp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tomor0720/deberta-large-finetuned-qqp") model = AutoModelForSequenceClassification.from_pretrained("Tomor0720/deberta-large-finetuned-qqp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Tomor0720/deberta-large-finetuned-qqp: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/Tomor0720/deberta-large-finetuned-qqp/resolve/main/training_args.bin
- Command line
-
hf download hf://Tomor0720/deberta-large-finetuned-qqp/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Tomor0720/deberta-large-finetuned-qqp/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- d25236984438939c213ef0446465e974a813e63bc6d67b621ff6cacaf003c11b
- Size of remote file:
- 3.45 kB
- SHA256:
- b5a4548766be04efa409a8d015e0e2c1e1a32b49b11a9c72b9dc6af312b163de
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