File size: 1,946 Bytes
1f6823f ed22e22 1f6823f ed22e22 1f6823f ed22e22 1f6823f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
license: mit
base_model: roberta-base-openai-detector
tags:
- generated_from_trainer
datasets:
- au_tex_tification
metrics:
- accuracy
model-index:
- name: roberta-base-openai-detector-autextification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: au_tex_tification
type: au_tex_tification
config: detection_en
split: train
args: detection_en
metrics:
- name: Accuracy
type: accuracy
value: 0.6
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-openai-detector-autextification
This model is a fine-tuned version of [roberta-base-openai-detector](https://huggingface.co/roberta-base-openai-detector) on the au_tex_tification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7155
- Accuracy: 0.6
- Roc Auc: 0.6354
## 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: 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 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 1.0975 | 1.0 | 10 | 0.7345 | 0.65 | 0.5417 |
| 0.4022 | 2.0 | 20 | 0.6266 | 0.65 | 0.6667 |
| 0.1635 | 3.0 | 30 | 0.7155 | 0.6 | 0.6354 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|