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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: fine-tune-wangchanberta-stock-thai |
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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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# fine-tune-wangchanberta-stock-thai |
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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: 1.0974 |
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- Accuracy: 0.4010 |
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- Precision: 0.3527 |
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- Recall: 0.4010 |
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- F1: 0.2439 |
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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: 32 |
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- eval_batch_size: 64 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.0643 | 1.0 | 26 | 1.1045 | 0.3564 | 0.2361 | 0.3564 | 0.2750 | |
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| 1.08 | 2.0 | 52 | 1.0902 | 0.3960 | 0.1568 | 0.3960 | 0.2247 | |
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| 1.074 | 3.0 | 78 | 1.1016 | 0.3960 | 0.1568 | 0.3960 | 0.2247 | |
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| 1.0613 | 4.0 | 104 | 1.0929 | 0.3960 | 0.1568 | 0.3960 | 0.2247 | |
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| 1.0564 | 5.0 | 130 | 1.0990 | 0.3960 | 0.1568 | 0.3960 | 0.2247 | |
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| 1.053 | 6.0 | 156 | 1.1003 | 0.3960 | 0.1568 | 0.3960 | 0.2247 | |
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| 1.0498 | 7.0 | 182 | 1.0968 | 0.3911 | 0.1557 | 0.3911 | 0.2227 | |
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| 1.0444 | 8.0 | 208 | 1.0946 | 0.3911 | 0.1557 | 0.3911 | 0.2227 | |
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| 1.0418 | 9.0 | 234 | 1.0990 | 0.3960 | 0.1568 | 0.3960 | 0.2247 | |
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| 1.0385 | 10.0 | 260 | 1.0982 | 0.3960 | 0.3025 | 0.3960 | 0.2331 | |
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| 1.0352 | 11.0 | 286 | 1.0980 | 0.3911 | 0.1557 | 0.3911 | 0.2227 | |
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| 1.0401 | 12.0 | 312 | 1.1001 | 0.3911 | 0.1557 | 0.3911 | 0.2227 | |
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| 1.0395 | 13.0 | 338 | 1.0970 | 0.4010 | 0.3519 | 0.4010 | 0.2431 | |
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| 1.032 | 14.0 | 364 | 1.0971 | 0.4010 | 0.3519 | 0.4010 | 0.2431 | |
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| 1.0351 | 15.0 | 390 | 1.0977 | 0.4010 | 0.3527 | 0.4010 | 0.2439 | |
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| 1.0262 | 16.0 | 416 | 1.0970 | 0.4010 | 0.3527 | 0.4010 | 0.2439 | |
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| 1.0385 | 17.0 | 442 | 1.0970 | 0.4010 | 0.3527 | 0.4010 | 0.2439 | |
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| 1.031 | 18.0 | 468 | 1.0970 | 0.4010 | 0.3527 | 0.4010 | 0.2439 | |
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| 1.0313 | 19.0 | 494 | 1.0969 | 0.4010 | 0.3527 | 0.4010 | 0.2439 | |
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| 1.0429 | 20.0 | 520 | 1.0974 | 0.4010 | 0.3527 | 0.4010 | 0.2439 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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