finetuning-sentiment-analysis-en-id
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1654
- Accuracy: 0.9527
- F1: 0.9646
- Precision: 0.9641
- Recall: 0.9652
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: 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: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.4566 | 1.0 | 1602 | 0.3666 | 0.8473 | 0.8909 | 0.8530 | 0.9323 |
0.3458 | 2.0 | 3204 | 0.2193 | 0.9238 | 0.9432 | 0.9410 | 0.9454 |
0.2362 | 3.0 | 4806 | 0.1654 | 0.9527 | 0.9646 | 0.9641 | 0.9652 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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