Downloads · 30 days
8
19% of all-time downloads
22best_berita_roberta_model_fold_4
choiruzzia/22best_berita_roberta_model_fold_4
22best_berita_roberta_model_fold_4 is a text classification model from choiruzzia. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
43
Public
Parameters
125M
499 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors499 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jul 17, 2024
- Updated
- Jul 17, 2024
- SHA
- a2ea948f
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>
22best_berita_roberta_model_fold_4
This model is a fine-tuned version of ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9316
- Accuracy: 0.8578
- Precision: 0.8563
- Recall: 0.8589
- F1: 0.8565
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 106 | 0.6362 | 0.7630 | 0.7721 | 0.7544 | 0.7523 |
| No log | 2.0 | 212 | 0.6104 | 0.7915 | 0.8111 | 0.7792 | 0.7784 |
| No log | 3.0 | 318 | 0.9225 | 0.8389 | 0.8441 | 0.8394 | 0.8349 |
| No log | 4.0 | 424 | 0.7776 | 0.8531 | 0.8505 | 0.8526 | 0.8513 |
| 0.4372 | 5.0 | 530 | 0.9357 | 0.8531 | 0.8545 | 0.8499 | 0.8482 |
| 0.4372 | 6.0 | 636 | 1.2975 | 0.8341 | 0.8573 | 0.8336 | 0.8289 |
| 0.4372 | 7.0 | 742 | 0.9316 | 0.8578 | 0.8563 | 0.8589 | 0.8565 |
| 0.4372 | 8.0 | 848 | 1.2425 | 0.8199 | 0.8267 | 0.8221 | 0.8159 |
| 0.4372 | 9.0 | 954 | 1.2742 | 0.8389 | 0.8549 | 0.8352 | 0.8324 |
| 0.0124 | 10.0 | 1060 | 1.2426 | 0.8341 | 0.8476 | 0.8311 | 0.8278 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jul 17, 2024
- Updated
- Jul 17, 2024