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wangchanberta-hyperopt-sentiment-01
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the Wisesight Sentiment dataset. The model is optimized for binary sentiment classification tasks, targeting two labels: positive and negative.
It achieves the following results on the evaluation set:
- Loss: 0.3595
- Accuracy: 0.9103
Model description
This model is intended for Thai language sentiment analysis, specifically designed to classify text as either positive or negative.
Intended uses & limitations
- The model is only trained to recognize positive and negative sentiments and may not perform well on nuanced or multi-class sentiment tasks.
- The model is specialized for the Thai language and is not intended for multi-language or code-switching scenarios.
Training and evaluation data
The model is trained on the Wisesight Sentiment dataset, which is a widely-used dataset for Thai NLP tasks.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5692051845867925e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 7
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.55 | 250 | 0.3128 | 0.8859 |
| 0.3913 | 1.09 | 500 | 0.2672 | 0.8942 |
| 0.3913 | 1.64 | 750 | 0.2860 | 0.9025 |
| 0.2172 | 2.19 | 1000 | 0.4044 | 0.9060 |
| 0.2172 | 2.74 | 1250 | 0.3738 | 0.9076 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
