Downloads · 30 days
6
12% of all-time downloads
IndoBERT-Sentiment-Analysis8v2
wildansofhal/IndoBERT-Sentiment-Analysis8v2
IndoBERT-Sentiment-Analysis8v2 is a text classification model from wildansofhal. 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
52
Public
Parameters
124M
498 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors498 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Aug 11, 2025
- Updated
- Aug 11, 2025
- SHA
- c415ec5c
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. -->
IndoBERT-Sentiment-Analysis8v2
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4324
- Accuracy: 0.9077
- F1 Score: 0.9073
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: 1e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|---|---|---|---|---|---|
| 0.6356 | 0.1096 | 50 | 0.6418 | 0.65 | 0.6481 |
| 0.6133 | 0.2193 | 100 | 0.5997 | 0.6782 | 0.6750 |
| 0.5781 | 0.3289 | 150 | 0.5178 | 0.7449 | 0.7445 |
| 0.5433 | 0.4386 | 200 | 0.4351 | 0.8051 | 0.8051 |
| 0.4154 | 0.5482 | 250 | 0.4331 | 0.8026 | 0.8019 |
| 0.467 | 0.6579 | 300 | 0.3819 | 0.8462 | 0.8459 |
| 0.3623 | 0.7675 | 350 | 0.4463 | 0.8410 | 0.8397 |
| 0.3316 | 0.8772 | 400 | 0.4174 | 0.8551 | 0.8548 |
| 0.3407 | 0.9868 | 450 | 0.5784 | 0.8141 | 0.8101 |
| 0.2882 | 1.0965 | 500 | 0.4091 | 0.8769 | 0.8768 |
| 0.2379 | 1.2061 | 550 | 0.5138 | 0.8603 | 0.8590 |
| 0.2828 | 1.3158 | 600 | 0.5102 | 0.8744 | 0.8730 |
| 0.2148 | 1.4254 | 650 | 0.4847 | 0.8833 | 0.8824 |
| 0.262 | 1.5351 | 700 | 0.4366 | 0.8987 | 0.8981 |
| 0.3484 | 1.6447 | 750 | 0.3786 | 0.9090 | 0.9086 |
| 0.1367 | 1.7544 | 800 | 0.4582 | 0.8949 | 0.8942 |
| 0.2344 | 1.8640 | 850 | 0.4343 | 0.9064 | 0.9060 |
| 0.2519 | 1.9737 | 900 | 0.4315 | 0.9077 | 0.9073 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Aug 11, 2025
- Updated
- Aug 11, 2025