File size: 2,657 Bytes
3e53327 |
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 |
---
library_name: transformers
license: mit
base_model: w11wo/indonesian-roberta-base-sentiment-classifier
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results_final
results: []
---
<!-- 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. -->
# results_final
This model is a fine-tuned version of [w11wo/indonesian-roberta-base-sentiment-classifier](https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3507
- Accuracy: 0.9056
- F1 Macro: 0.9055
- F1 Weighted: 0.9055
- Precision Macro: 0.9057
- Recall Macro: 0.9056
- Precision Weighted: 0.9057
- Recall Weighted: 0.9056
## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Recall Macro | Precision Weighted | Recall Weighted |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|:------------------:|:---------------:|
| 0.4024 | 1.8519 | 500 | 0.2951 | 0.9 | 0.9002 | 0.9002 | 0.9011 | 0.9000 | 0.9011 | 0.9 |
| 0.3394 | 3.7037 | 1000 | 0.3384 | 0.8944 | 0.8946 | 0.8946 | 0.8960 | 0.8944 | 0.8960 | 0.8944 |
| 0.2395 | 5.5556 | 1500 | 0.3507 | 0.9056 | 0.9055 | 0.9055 | 0.9057 | 0.9056 | 0.9057 | 0.9056 |
| 0.2222 | 7.4074 | 2000 | 0.3621 | 0.9 | 0.9000 | 0.9000 | 0.9003 | 0.9 | 0.9003 | 0.9 |
| 0.1813 | 9.2593 | 2500 | 0.3718 | 0.8963 | 0.8963 | 0.8963 | 0.8963 | 0.8963 | 0.8963 | 0.8963 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|