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--- |
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base_model: airesearch/wangchanberta-base-att-spm-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: test_dir |
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results: [] |
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datasets: |
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- wisesight_sentiment |
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language: |
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- th |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wangchanberta-hyperopt-sentiment-01 |
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This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the Wisesight Sentiment dataset. |
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The model is optimized for binary sentiment classification tasks, targeting two labels: positive and negative. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3595 |
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- Accuracy: 0.9103 |
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## Model description |
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This model is intended for Thai language sentiment analysis, specifically designed to classify text as either positive or negative. |
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## Intended uses & limitations |
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- The model is only trained to recognize positive and negative sentiments and may not perform well on nuanced or multi-class sentiment tasks. |
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- The model is specialized for the Thai language and is not intended for multi-language or code-switching scenarios. |
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## Training and evaluation data |
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The model is trained on the Wisesight Sentiment dataset, which is a widely-used dataset for Thai NLP tasks. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2.5692051845867925e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 7 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.55 | 250 | 0.3128 | 0.8859 | |
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| 0.3913 | 1.09 | 500 | 0.2672 | 0.8942 | |
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| 0.3913 | 1.64 | 750 | 0.2860 | 0.9025 | |
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| 0.2172 | 2.19 | 1000 | 0.4044 | 0.9060 | |
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| 0.2172 | 2.74 | 1250 | 0.3738 | 0.9076 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
