Text Classification
Transformers
PyTorch
Safetensors
English
German
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use deepset/deberta-v3-base-injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/deberta-v3-base-injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepset/deberta-v3-base-injection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepset/deberta-v3-base-injection") model = AutoModelForSequenceClassification.from_pretrained("deepset/deberta-v3-base-injection") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md (#8)
Browse files- Update README.md (28f14a78e877237c634d952b8d80c7d4a1e9252e)
Co-authored-by: deepset-mnz <deepset-manz@users.noreply.huggingface.co>
README.md
CHANGED
|
@@ -20,7 +20,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 20 |
|
| 21 |
# deberta-v3-base-injection
|
| 22 |
|
| 23 |
-
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [
|
| 24 |
It achieves the following results on the evaluation set:
|
| 25 |
- Loss: 0.0673
|
| 26 |
- Accuracy: 0.9914
|
|
|
|
| 20 |
|
| 21 |
# deberta-v3-base-injection
|
| 22 |
|
| 23 |
+
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [prompt-injection](https://huggingface.co/datasets/JasperLS/prompt-injections) dataset.
|
| 24 |
It achieves the following results on the evaluation set:
|
| 25 |
- Loss: 0.0673
|
| 26 |
- Accuracy: 0.9914
|