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
Commit ·
17f8611
1
Parent(s): 2b6e24b
Update README.md
Browse files
README.md
CHANGED
|
@@ -8,6 +8,11 @@ base_model: microsoft/deberta-v3-base
|
|
| 8 |
model-index:
|
| 9 |
- name: deberta-v3-base-injection
|
| 10 |
results: []
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 11 |
---
|
| 12 |
|
| 13 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
@@ -59,4 +64,4 @@ The following hyperparameters were used during training:
|
|
| 59 |
- Transformers 4.29.1
|
| 60 |
- Pytorch 2.0.0+cu118
|
| 61 |
- Datasets 2.12.0
|
| 62 |
-
- Tokenizers 0.13.3
|
|
|
|
| 8 |
model-index:
|
| 9 |
- name: deberta-v3-base-injection
|
| 10 |
results: []
|
| 11 |
+
datasets:
|
| 12 |
+
- deepset/prompt-injections
|
| 13 |
+
language:
|
| 14 |
+
- en
|
| 15 |
+
- de
|
| 16 |
---
|
| 17 |
|
| 18 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 64 |
- Transformers 4.29.1
|
| 65 |
- Pytorch 2.0.0+cu118
|
| 66 |
- Datasets 2.12.0
|
| 67 |
+
- Tokenizers 0.13.3
|