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VP_ViSoBERT_syl_ViWikiFC

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End of training

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  1. README.md +48 -38
  2. model.safetensors +1 -1
  3. runs/Jun06_19-04-41_e3ad251fbc84/events.out.tfevents.1717700682.e3ad251fbc84.34.0 +2 -2
README.md CHANGED
@@ -1,5 +1,5 @@
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  ---
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- base_model: uitnlp/visobert
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -14,10 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # VP_ViSoBERT_syl_ViWikiFC
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- This model is a fine-tuned version of [uitnlp/visobert](https://huggingface.co/uitnlp/visobert) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9243
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- - Accuracy: 0.6364
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  ## Model description
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@@ -37,48 +37,58 @@ More information needed
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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: 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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- | 1.1299 | 0.1 | 100 | 1.1182 | 0.3411 |
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- | 1.0816 | 0.19 | 200 | 1.0678 | 0.3976 |
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- | 1.0181 | 0.29 | 300 | 1.0163 | 0.4823 |
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- | 1.0121 | 0.38 | 400 | 0.9956 | 0.5072 |
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- | 0.9617 | 0.48 | 500 | 0.9718 | 0.5048 |
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- | 0.9297 | 0.57 | 600 | 0.9665 | 0.5239 |
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- | 0.9332 | 0.67 | 700 | 0.9252 | 0.5646 |
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- | 0.9057 | 0.76 | 800 | 0.9667 | 0.5421 |
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- | 0.8756 | 0.86 | 900 | 0.8884 | 0.5871 |
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- | 0.879 | 0.96 | 1000 | 0.8907 | 0.5718 |
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- | 0.8249 | 1.05 | 1100 | 0.8793 | 0.5981 |
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- | 0.7177 | 1.15 | 1200 | 0.8951 | 0.5957 |
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- | 0.7145 | 1.24 | 1300 | 0.9523 | 0.6062 |
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- | 0.7469 | 1.34 | 1400 | 0.9001 | 0.5986 |
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- | 0.7358 | 1.43 | 1500 | 0.8865 | 0.6081 |
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- | 0.7112 | 1.53 | 1600 | 0.9099 | 0.6057 |
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- | 0.7299 | 1.62 | 1700 | 0.8496 | 0.6144 |
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- | 0.6949 | 1.72 | 1800 | 0.8580 | 0.6124 |
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- | 0.6988 | 1.81 | 1900 | 0.8840 | 0.6215 |
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- | 0.6524 | 1.91 | 2000 | 0.8753 | 0.6134 |
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- | 0.6914 | 2.01 | 2100 | 0.8729 | 0.6330 |
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- | 0.5427 | 2.1 | 2200 | 0.9494 | 0.6431 |
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- | 0.5628 | 2.2 | 2300 | 0.9531 | 0.6120 |
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- | 0.5607 | 2.29 | 2400 | 0.9050 | 0.6340 |
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- | 0.5396 | 2.39 | 2500 | 0.9149 | 0.6335 |
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- | 0.5178 | 2.48 | 2600 | 0.9848 | 0.6124 |
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- | 0.5322 | 2.58 | 2700 | 0.9198 | 0.6330 |
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- | 0.5406 | 2.67 | 2800 | 0.9206 | 0.6364 |
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- | 0.5183 | 2.77 | 2900 | 0.9150 | 0.6392 |
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- | 0.5369 | 2.87 | 3000 | 0.9200 | 0.6340 |
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- | 0.5105 | 2.96 | 3100 | 0.9243 | 0.6364 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  ---
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+ base_model: tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  # VP_ViSoBERT_syl_ViWikiFC
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+ This model is a fine-tuned version of [tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC](https://huggingface.co/tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1555
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+ - Accuracy: 0.6445
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  ## Model description
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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: 8
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+ - eval_batch_size: 8
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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: 2
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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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+ | 0.6994 | 0.05 | 100 | 0.9688 | 0.6158 |
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+ | 0.6904 | 0.1 | 200 | 0.9753 | 0.6014 |
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+ | 0.7969 | 0.14 | 300 | 0.9446 | 0.5871 |
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+ | 0.6801 | 0.19 | 400 | 0.9912 | 0.6057 |
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+ | 0.7089 | 0.24 | 500 | 0.9617 | 0.5861 |
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+ | 0.6627 | 0.29 | 600 | 1.0585 | 0.5689 |
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+ | 0.6792 | 0.33 | 700 | 1.0064 | 0.6230 |
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+ | 0.6702 | 0.38 | 800 | 1.0593 | 0.5818 |
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+ | 0.6252 | 0.43 | 900 | 0.9621 | 0.5967 |
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+ | 0.6262 | 0.48 | 1000 | 1.0152 | 0.5957 |
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+ | 0.6515 | 0.53 | 1100 | 0.9539 | 0.6225 |
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+ | 0.6596 | 0.57 | 1200 | 0.9188 | 0.6067 |
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+ | 0.6458 | 0.62 | 1300 | 0.9318 | 0.6201 |
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+ | 0.6087 | 0.67 | 1400 | 0.9532 | 0.6172 |
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+ | 0.6282 | 0.72 | 1500 | 1.0107 | 0.6244 |
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+ | 0.6266 | 0.76 | 1600 | 1.0199 | 0.6096 |
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+ | 0.6165 | 0.81 | 1700 | 1.0973 | 0.6096 |
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+ | 0.5869 | 0.86 | 1800 | 0.9177 | 0.6325 |
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+ | 0.596 | 0.91 | 1900 | 0.8821 | 0.6364 |
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+ | 0.6073 | 0.96 | 2000 | 0.9350 | 0.6306 |
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+ | 0.5921 | 1.0 | 2100 | 0.9606 | 0.6282 |
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+ | 0.4551 | 1.05 | 2200 | 1.0386 | 0.6373 |
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+ | 0.3922 | 1.1 | 2300 | 1.1936 | 0.6368 |
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+ | 0.39 | 1.15 | 2400 | 1.1922 | 0.6316 |
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+ | 0.442 | 1.19 | 2500 | 1.1599 | 0.6220 |
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+ | 0.4092 | 1.24 | 2600 | 1.3106 | 0.6196 |
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+ | 0.4582 | 1.29 | 2700 | 1.1817 | 0.6316 |
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+ | 0.4356 | 1.34 | 2800 | 1.1257 | 0.6316 |
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+ | 0.4145 | 1.39 | 2900 | 1.1899 | 0.6354 |
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+ | 0.4379 | 1.43 | 3000 | 1.1385 | 0.6388 |
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+ | 0.4222 | 1.48 | 3100 | 1.1844 | 0.6249 |
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+ | 0.3758 | 1.53 | 3200 | 1.2444 | 0.6311 |
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+ | 0.4114 | 1.58 | 3300 | 1.1908 | 0.6349 |
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+ | 0.4449 | 1.62 | 3400 | 1.1483 | 0.6273 |
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+ | 0.4046 | 1.67 | 3500 | 1.1977 | 0.6306 |
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+ | 0.4274 | 1.72 | 3600 | 1.1520 | 0.6450 |
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+ | 0.3785 | 1.77 | 3700 | 1.1665 | 0.6330 |
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+ | 0.3854 | 1.82 | 3800 | 1.1680 | 0.6474 |
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+ | 0.3562 | 1.86 | 3900 | 1.1616 | 0.6459 |
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+ | 0.3938 | 1.91 | 4000 | 1.1823 | 0.6397 |
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+ | 0.5083 | 1.96 | 4100 | 1.1555 | 0.6445 |
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  ### Framework versions
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