metadata
license: mit
base_model: roberta-base
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: 0.9286666512489319
- name: Precision
type: precision
value: 0.9286666512489319
- name: Recall
type: recall
value: 0.9286666512489319
- name: F1
type: f1
value: 0.9286666512489319
results
This model is a fine-tuned version of roberta-base on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2691
- Accuracy: 0.9287
- Precision: 0.9287
- Recall: 0.9287
- F1: 0.9287
- Auroc: 0.9772
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 | Validation Loss | Accuracy | Precision | Recall | F1 | Auroc |
|---|---|---|---|---|---|---|---|---|
| 0.1764 | 0.46 | 500 | 0.2698 | 0.9044 | 0.9044 | 0.9044 | 0.9044 | 0.9738 |
| 0.3348 | 0.91 | 1000 | 0.2755 | 0.9117 | 0.9117 | 0.9117 | 0.9117 | 0.9686 |
| 0.1478 | 1.37 | 1500 | 0.3275 | 0.9109 | 0.9109 | 0.9109 | 0.9109 | 0.9771 |
| 0.2051 | 1.83 | 2000 | 0.2575 | 0.9309 | 0.9309 | 0.9309 | 0.9309 | 0.9793 |
| 0.1435 | 2.29 | 2500 | 0.3140 | 0.9245 | 0.9245 | 0.9245 | 0.9245 | 0.9783 |
| 0.1425 | 2.74 | 3000 | 0.2691 | 0.9287 | 0.9287 | 0.9287 | 0.9287 | 0.9772 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1