Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use JasperLS/deberta-v3-base-injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JasperLS/deberta-v3-base-injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JasperLS/deberta-v3-base-injection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JasperLS/deberta-v3-base-injection") model = AutoModelForSequenceClassification.from_pretrained("JasperLS/deberta-v3-base-injection") - Notebooks
- Google Colab
- Kaggle
update nam
Browse files
README.md
CHANGED
|
@@ -14,7 +14,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 14 |
|
| 15 |
# deberta-v3-base-injection
|
| 16 |
|
| 17 |
-
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [
|
| 18 |
It achieves the following results on the evaluation set:
|
| 19 |
- Loss: 0.0673
|
| 20 |
- Accuracy: 0.9914
|
|
|
|
| 14 |
|
| 15 |
# deberta-v3-base-injection
|
| 16 |
|
| 17 |
+
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [promp-injection](https://huggingface.co/datasets/JasperLS/prompt-injections) dataset.
|
| 18 |
It achieves the following results on the evaluation set:
|
| 19 |
- Loss: 0.0673
|
| 20 |
- Accuracy: 0.9914
|