metadata
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: 0.899749373433584
- name: F1
type: f1
value: 0.8181818181818181
scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased
This model is a fine-tuned version of indolem/indobert-base-uncased on the indolem_sentiment dataset. It achieves the following results on the evaluation set:
- Loss: 0.7320
- Accuracy: 0.8997
- F1: 0.8182
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 | 0.5218 | 0.7343 | 0.2838 |
No log | 0.88 | 400 | 0.4318 | 0.8070 | 0.7138 |
0.4843 | 1.32 | 600 | 0.4092 | 0.8521 | 0.7281 |
0.4843 | 1.76 | 800 | 0.3515 | 0.8772 | 0.7803 |
0.2912 | 2.2 | 1000 | 0.4582 | 0.8697 | 0.7833 |
0.2912 | 2.64 | 1200 | 0.5148 | 0.8747 | 0.7881 |
0.2912 | 3.08 | 1400 | 0.5736 | 0.8672 | 0.7837 |
0.2526 | 3.52 | 1600 | 0.5119 | 0.8797 | 0.7983 |
0.2526 | 3.96 | 1800 | 0.5242 | 0.8997 | 0.8095 |
0.1974 | 4.4 | 2000 | 0.5311 | 0.8997 | 0.8182 |
0.1974 | 4.84 | 2200 | 0.6478 | 0.8797 | 0.7983 |
0.1974 | 5.27 | 2400 | 0.6219 | 0.8822 | 0.8000 |
0.1526 | 5.71 | 2600 | 0.6591 | 0.8872 | 0.8178 |
0.1526 | 6.15 | 2800 | 0.6483 | 0.8947 | 0.8056 |
0.1159 | 6.59 | 3000 | 0.7075 | 0.8847 | 0.8099 |
0.1159 | 7.03 | 3200 | 0.7157 | 0.8872 | 0.8 |
0.1159 | 7.47 | 3400 | 0.7320 | 0.8997 | 0.8182 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3