Instructions to use microsoft/deberta-v3-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-v3-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/deberta-v3-small")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/deberta-v3-small", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -19,6 +19,7 @@ For more details of our V3 model, please check appendix A11 in our paper.
|
|
| 19 |
#### Fine-tuning on NLU tasks
|
| 20 |
|
| 21 |
We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
|
|
|
|
| 22 |
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
|
| 23 |
|-------------------|-----------|-----------|--------|
|
| 24 |
| RoBERTa-base | 91.5/84.6 | 83.7/80.5 | 87.6 |
|
|
|
|
| 19 |
#### Fine-tuning on NLU tasks
|
| 20 |
|
| 21 |
We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
|
| 22 |
+
|
| 23 |
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
|
| 24 |
|-------------------|-----------|-----------|--------|
|
| 25 |
| RoBERTa-base | 91.5/84.6 | 83.7/80.5 | 87.6 |
|