metadata
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: backdoored_bert-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9254587155963303
backdoored_bert-finetuned-sst2
This model is created for research study which contains backdoor inside the model. Please use it for academic research, don't use it for business scenarios.
This model is a fine-tuned version of Lujia/backdoored_bert on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.4265
- Accuracy: 0.9255
Model description
More information needed
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1821 | 1.0 | 4210 | 0.2600 | 0.9151 |
| 0.1245 | 2.0 | 8420 | 0.3431 | 0.9197 |
| 0.0934 | 3.0 | 12630 | 0.3466 | 0.9186 |
| 0.0546 | 4.0 | 16840 | 0.3703 | 0.9232 |
| 0.0329 | 5.0 | 21050 | 0.4265 | 0.9255 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2