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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-ia3
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-ia3
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: 0.4042
- Accuracy: 0.8145
- Precision: 0.7763
- Recall: 0.7763
- F1: 0.7763
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5636 | 1.0 | 122 | 0.5070 | 0.7243 | 0.6575 | 0.6274 | 0.6354 |
| 0.5128 | 2.0 | 244 | 0.5015 | 0.7343 | 0.6911 | 0.7120 | 0.6976 |
| 0.4941 | 3.0 | 366 | 0.4709 | 0.7469 | 0.6955 | 0.6984 | 0.6969 |
| 0.4702 | 4.0 | 488 | 0.4496 | 0.7744 | 0.7275 | 0.7154 | 0.7207 |
| 0.4704 | 5.0 | 610 | 0.4521 | 0.7719 | 0.7270 | 0.7386 | 0.7320 |
| 0.4616 | 6.0 | 732 | 0.4490 | 0.7644 | 0.7175 | 0.7258 | 0.7213 |
| 0.4543 | 7.0 | 854 | 0.4381 | 0.7820 | 0.7389 | 0.7532 | 0.7449 |
| 0.4532 | 8.0 | 976 | 0.4197 | 0.8070 | 0.7744 | 0.7385 | 0.7519 |
| 0.4517 | 9.0 | 1098 | 0.4195 | 0.7970 | 0.7551 | 0.7539 | 0.7545 |
| 0.4438 | 10.0 | 1220 | 0.4102 | 0.8170 | 0.8013 | 0.7330 | 0.7540 |
| 0.4389 | 11.0 | 1342 | 0.4112 | 0.8271 | 0.7933 | 0.7826 | 0.7876 |
| 0.4428 | 12.0 | 1464 | 0.4179 | 0.7970 | 0.7555 | 0.7664 | 0.7604 |
| 0.4421 | 13.0 | 1586 | 0.4030 | 0.8321 | 0.8110 | 0.7662 | 0.7828 |
| 0.4403 | 14.0 | 1708 | 0.4037 | 0.8321 | 0.8014 | 0.7837 | 0.7915 |
| 0.4392 | 15.0 | 1830 | 0.4077 | 0.8221 | 0.7852 | 0.7866 | 0.7859 |
| 0.4329 | 16.0 | 1952 | 0.4062 | 0.8195 | 0.7820 | 0.7848 | 0.7834 |
| 0.4338 | 17.0 | 2074 | 0.4058 | 0.8145 | 0.7761 | 0.7788 | 0.7774 |
| 0.4407 | 18.0 | 2196 | 0.4042 | 0.8145 | 0.7763 | 0.7763 | 0.7763 |
| 0.4329 | 19.0 | 2318 | 0.4033 | 0.8195 | 0.7827 | 0.7798 | 0.7812 |
| 0.4292 | 20.0 | 2440 | 0.4042 | 0.8145 | 0.7763 | 0.7763 | 0.7763 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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