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 pytorch_model.bin from Tomor0720/deberta-large-finetuned-qqp: direct link, hf CLI and curl.
- Browser
- Download file 1.62 GB
-
https://huggingface.co/Tomor0720/deberta-large-finetuned-qqp/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Tomor0720/deberta-large-finetuned-qqp/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tomor0720/deberta-large-finetuned-qqp/resolve/main/pytorch_model.bin
1.62 GB
- Xet hash:
- 5bc9caae900c1b50b6e719290d363e8e0f1df6b8ff089e4a1a2fc197d018de66
- Size of remote file:
- 1.62 GB
- SHA256:
- af48e9f04535b9cf404886842f656570396be93b86b97b4c3c2b641b21332f63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.