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---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
datasets:
- indolem_sentiment
metrics:
- accuracy
- f1
model-index:
- name: scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indolem_sentiment
type: indolem_sentiment
config: indolem_sentiment_nusantara_text
split: validation
args: indolem_sentiment_nusantara_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8922305764411027
- name: F1
type: f1
value: 0.8154506437768241
---
<!-- 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. -->
# scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on the indolem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7311
- Accuracy: 0.8922
- F1: 0.8155
## 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-06
- 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.44 | 200 | 0.5133 | 0.7544 | 0.3718 |
| No log | 0.88 | 400 | 0.4239 | 0.7995 | 0.6875 |
| 0.4818 | 1.32 | 600 | 0.3889 | 0.8647 | 0.7523 |
| 0.4818 | 1.76 | 800 | 0.3263 | 0.8872 | 0.8069 |
| 0.291 | 2.2 | 1000 | 0.3933 | 0.8847 | 0.8067 |
| 0.291 | 2.64 | 1200 | 0.4703 | 0.8847 | 0.7982 |
| 0.291 | 3.08 | 1400 | 0.5284 | 0.8622 | 0.7843 |
| 0.2432 | 3.52 | 1600 | 0.4924 | 0.8897 | 0.8136 |
| 0.2432 | 3.96 | 1800 | 0.4952 | 0.9023 | 0.8219 |
| 0.1982 | 4.4 | 2000 | 0.5157 | 0.9098 | 0.8421 |
| 0.1982 | 4.84 | 2200 | 0.6454 | 0.8847 | 0.8099 |
| 0.1982 | 5.27 | 2400 | 0.5636 | 0.9048 | 0.8348 |
| 0.1441 | 5.71 | 2600 | 0.6147 | 0.8872 | 0.8193 |
| 0.1441 | 6.15 | 2800 | 0.6280 | 0.8997 | 0.8198 |
| 0.1147 | 6.59 | 3000 | 0.6505 | 0.8947 | 0.8205 |
| 0.1147 | 7.03 | 3200 | 0.6547 | 0.8972 | 0.8285 |
| 0.1147 | 7.47 | 3400 | 0.7311 | 0.8922 | 0.8155 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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