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fine-tune-wangchanberta-stock-thai

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fine-tune-wangchanberta-SABINA-split-headline1-test

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  1. README.md +31 -12
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -4,6 +4,9 @@ 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: fine-tune-wangchanberta-stock-thai
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  results: []
@@ -16,8 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.6753
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- - Accuracy: 0.5998
 
 
 
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  ## Model description
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@@ -42,19 +48,32 @@ The following hyperparameters were used during training:
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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: 7
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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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- | 0.715 | 1.0 | 553 | 0.6922 | 0.5140 |
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- | 0.6929 | 2.0 | 1106 | 0.6738 | 0.6019 |
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- | 0.6898 | 3.0 | 1659 | 0.6753 | 0.6010 |
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- | 0.6902 | 4.0 | 2212 | 0.6782 | 0.6010 |
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- | 0.6879 | 5.0 | 2765 | 0.6734 | 0.6010 |
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- | 0.6878 | 6.0 | 3318 | 0.6777 | 0.6003 |
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- | 0.6876 | 7.0 | 3871 | 0.6753 | 0.5998 |
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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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  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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  - 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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