Text Classification
Transformers
PyTorch
TensorBoard
English
deberta-v2
Generated from Trainer
deberta-v3
Eval Results (legacy)
Instructions to use mrm8488/deberta-v3-small-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/deberta-v3-small-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrm8488/deberta-v3-small-finetuned-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/deberta-v3-small-finetuned-mrpc") model = AutoModelForSequenceClassification.from_pretrained("mrm8488/deberta-v3-small-finetuned-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e89a6248d5c5dc36146a298a9df05e1c99504a7aac6ce527550331d1e687214d
- Size of remote file:
- 568 MB
- SHA256:
- 50c960e91d9c4b5c3d6c12708cdd685b02f8a23b9a69022c7b2b4fc77ea52940
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