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# gelectra-base-injection-pt_v1

DEPRECATED - PLEASE USE NEWER GELECTRA OR DEBERTA VERSION

This model is a fine-tuned version of [deepset/gelectra-base](https://huggingface.co/deepset/gelectra-base) on a closed prompt injection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0163
- Accuracy: 1.0

## Model description

The model classifies prompts as injections or legitimate questions.

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 45   | 0.2042          | 0.9211   |
| No log        | 2.0   | 90   | 0.0247          | 1.0      |
| No log        | 3.0   | 135  | 0.0163          | 1.0      |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "deepset/gelectra-base", "model-index": [{"name": "gelectra-base-injection-pt_v1", "results": []}]}
transformers pytorch tensorboard safetensors electra text-classification generated_from_trainer base_model:deepset/gelectra-base license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #electra #text-classification #generated_from_trainer #base_model-deepset/gelectra-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

gelectra-base-injection-pt\_v1
==============================


DEPRECATED - PLEASE USE NEWER GELECTRA OR DEBERTA VERSION


This model is a fine-tuned version of deepset/gelectra-base on a closed prompt injection dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0163
* Accuracy: 1.0


Model description
-----------------


The model classifies prompts as injections or legitimate questions.


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #electra #text-classification #generated_from_trainer #base_model-deepset/gelectra-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #electra #text-classification #generated_from_trainer #base_model-deepset/gelectra-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3