Text Classification
Transformers
PyTorch
TensorBoard
deberta
Generated from Trainer
Eval Results (legacy)
Instructions to use Tomor0720/deberta-large-finetuned-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tomor0720/deberta-large-finetuned-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tomor0720/deberta-large-finetuned-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tomor0720/deberta-large-finetuned-sst2") model = AutoModelForSequenceClassification.from_pretrained("Tomor0720/deberta-large-finetuned-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfb5d5243020bba8daf968a553277cb39aba4c1650efeff4b4dea78f4491f0f7
- Size of remote file:
- 1.62 GB
- SHA256:
- 6d746623265d72cebc659256ee29f2c6ab131fddbd7892a98ba78c1ef4e8a528
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.