metadata
license: mit
base_model: gpt2
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9330666661262512
- name: Precision
type: precision
value: 0.9330666661262512
- name: Recall
type: recall
value: 0.9330666661262512
- name: F1
type: f1
value: 0.9330666661262512
results
This model is a fine-tuned version of gpt2 on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2797
- Accuracy: 0.9331
- Precision: 0.9331
- Recall: 0.9331
- F1: 0.9331
- Auroc: 0.9810
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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 | Accuracy | Auroc | F1 | Validation Loss | Precision | Recall |
|---|---|---|---|---|---|---|---|---|
| 0.1436 | 0.46 | 500 | 0.8935 | 0.9751 | 0.8935 | 0.2923 | 0.8935 | 0.8935 |
| 0.1621 | 0.91 | 1000 | 0.9261 | 0.9789 | 0.9261 | 0.1984 | 0.9261 | 0.9261 |
| 0.2196 | 1.37 | 1500 | 0.9289 | 0.9810 | 0.9289 | 0.2082 | 0.9289 | 0.9289 |
| 0.1457 | 1.83 | 2000 | 0.9325 | 0.9816 | 0.9325 | 0.2282 | 0.9325 | 0.9325 |
| 0.1103 | 2.29 | 2500 | 0.9305 | 0.9806 | 0.9305 | 0.3201 | 0.9305 | 0.9305 |
| 0.0679 | 2.74 | 3000 | 0.2797 | 0.9331 | 0.9331 | 0.9331 | 0.9331 | 0.9810 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1