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---
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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: laptop_sentence_classfication_wangChanBERTa
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results: []
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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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# laptop_sentence_classfication_wangChanBERTa
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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 None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2752
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- Accuracy: 0.9
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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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: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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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: 20
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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 | 1.0 | 25 | 0.6130 | 0.7077 |
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| No log | 2.0 | 50 | 0.4832 | 0.7769 |
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| No log | 3.0 | 75 | 0.4457 | 0.8154 |
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| No log | 4.0 | 100 | 0.4696 | 0.7692 |
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| No log | 5.0 | 125 | 0.4378 | 0.8077 |
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| No log | 6.0 | 150 | 0.4698 | 0.8077 |
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| No log | 7.0 | 175 | 0.3654 | 0.8615 |
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| No log | 8.0 | 200 | 0.3795 | 0.8615 |
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| No log | 9.0 | 225 | 0.4212 | 0.8692 |
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| No log | 10.0 | 250 | 0.4153 | 0.8538 |
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| No log | 11.0 | 275 | 0.3723 | 0.8692 |
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| No log | 12.0 | 300 | 0.3590 | 0.8538 |
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| No log | 13.0 | 325 | 0.2553 | 0.9077 |
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| No log | 14.0 | 350 | 0.2713 | 0.9 |
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| No log | 15.0 | 375 | 0.2699 | 0.9077 |
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| No log | 16.0 | 400 | 0.2563 | 0.9154 |
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| No log | 17.0 | 425 | 0.2536 | 0.9231 |
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| No log | 18.0 | 450 | 0.2529 | 0.9154 |
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| No log | 19.0 | 475 | 0.2743 | 0.9 |
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| 0.3025 | 20.0 | 500 | 0.2752 | 0.9 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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