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bert-base-uncased-FinedTuned

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End of training

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  1. README.md +13 -18
  2. events.out.tfevents.1718104401.0a87a89624da.2515.0 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the stsb_multi_mt dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.6888
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  - Accuracy: 0.1762
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  ## Model description
@@ -42,7 +42,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -51,28 +51,23 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_steps: 1000
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- - training_steps: 15000
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  - mixed_precision_training: Native AMP
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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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- | 0.1203 | 5.5556 | 1000 | 2.7894 | 0.1762 |
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- | 0.089 | 11.1111 | 2000 | 2.7816 | 0.1762 |
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- | 0.095 | 16.6667 | 3000 | 2.7732 | 0.1762 |
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- | 0.0818 | 22.2222 | 4000 | 2.7201 | 0.1762 |
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- | 0.0786 | 27.7778 | 5000 | 2.6378 | 0.1762 |
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- | 0.0816 | 33.3333 | 6000 | 2.7167 | 0.1762 |
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- | 0.0795 | 38.8889 | 7000 | 2.6429 | 0.1762 |
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- | 0.0978 | 44.4444 | 8000 | 2.6964 | 0.1762 |
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- | 0.1006 | 50.0 | 9000 | 2.7168 | 0.1762 |
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- | 0.171 | 55.5556 | 10000 | 2.7183 | 0.1762 |
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- | 0.1185 | 61.1111 | 11000 | 2.6737 | 0.1762 |
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- | 0.1648 | 66.6667 | 12000 | 2.6573 | 0.1762 |
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- | 0.1365 | 72.2222 | 13000 | 2.6944 | 0.1762 |
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- | 0.1298 | 77.7778 | 14000 | 2.6950 | 0.1762 |
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- | 0.1832 | 83.3333 | 15000 | 2.6888 | 0.1762 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the stsb_multi_mt dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.7821
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  - Accuracy: 0.1762
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  ## Model description
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-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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  - lr_scheduler_warmup_steps: 1000
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+ - training_steps: 10000
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  - mixed_precision_training: Native AMP
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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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+ | 0.2457 | 5.5556 | 1000 | 2.4913 | 0.1762 |
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+ | 0.1759 | 11.1111 | 2000 | 2.8424 | 0.1762 |
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+ | 0.1458 | 16.6667 | 3000 | 2.9765 | 0.1762 |
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+ | 0.1132 | 22.2222 | 4000 | 2.7163 | 0.1762 |
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+ | 0.1118 | 27.7778 | 5000 | 2.7272 | 0.1762 |
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+ | 0.1272 | 33.3333 | 6000 | 2.8354 | 0.1762 |
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+ | 0.1233 | 38.8889 | 7000 | 2.6948 | 0.1762 |
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+ | 0.1161 | 44.4444 | 8000 | 2.7358 | 0.1762 |
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+ | 0.0736 | 50.0 | 9000 | 2.7748 | 0.1762 |
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+ | 0.0924 | 55.5556 | 10000 | 2.7821 | 0.1762 |
 
 
 
 
 
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  ### Framework versions
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