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---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-seq_bn-1
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. -->
# sentiment-seq_bn-1
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3078
- Accuracy: 0.8797
- Precision: 0.8572
- Recall: 0.8499
- F1: 0.8534
## 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: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5593 | 1.0 | 122 | 0.5129 | 0.7318 | 0.6697 | 0.6453 | 0.6532 |
| 0.481 | 2.0 | 244 | 0.4831 | 0.7343 | 0.6993 | 0.7295 | 0.7054 |
| 0.4234 | 3.0 | 366 | 0.3974 | 0.8221 | 0.7926 | 0.7616 | 0.7740 |
| 0.3701 | 4.0 | 488 | 0.3780 | 0.8396 | 0.8128 | 0.7890 | 0.7992 |
| 0.3499 | 5.0 | 610 | 0.3612 | 0.8471 | 0.8135 | 0.8268 | 0.8195 |
| 0.3165 | 6.0 | 732 | 0.3760 | 0.8271 | 0.7953 | 0.8377 | 0.8072 |
| 0.2968 | 7.0 | 854 | 0.3342 | 0.8697 | 0.8438 | 0.8403 | 0.8420 |
| 0.2812 | 8.0 | 976 | 0.3311 | 0.8672 | 0.8463 | 0.8260 | 0.8351 |
| 0.2682 | 9.0 | 1098 | 0.3269 | 0.8722 | 0.8463 | 0.8446 | 0.8454 |
| 0.2596 | 10.0 | 1220 | 0.3145 | 0.8797 | 0.8560 | 0.8524 | 0.8541 |
| 0.2464 | 11.0 | 1342 | 0.3138 | 0.8697 | 0.8503 | 0.8278 | 0.8377 |
| 0.2415 | 12.0 | 1464 | 0.3126 | 0.8847 | 0.8697 | 0.8459 | 0.8565 |
| 0.2354 | 13.0 | 1586 | 0.3136 | 0.8822 | 0.8694 | 0.8392 | 0.8521 |
| 0.2303 | 14.0 | 1708 | 0.3172 | 0.8747 | 0.8463 | 0.8563 | 0.8510 |
| 0.2172 | 15.0 | 1830 | 0.3120 | 0.8822 | 0.8656 | 0.8442 | 0.8537 |
| 0.2159 | 16.0 | 1952 | 0.3116 | 0.8622 | 0.8319 | 0.8400 | 0.8357 |
| 0.2192 | 17.0 | 2074 | 0.3123 | 0.8847 | 0.8717 | 0.8434 | 0.8557 |
| 0.2124 | 18.0 | 2196 | 0.3150 | 0.8647 | 0.8340 | 0.8467 | 0.8399 |
| 0.2077 | 19.0 | 2318 | 0.3084 | 0.8797 | 0.8585 | 0.8474 | 0.8526 |
| 0.205 | 20.0 | 2440 | 0.3078 | 0.8797 | 0.8572 | 0.8499 | 0.8534 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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