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bert-base-uncased-FinedTuned

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End of training

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  1. README.md +17 -12
  2. events.out.tfevents.1717851441.c649b329e7f9.1617.0 +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: 2.7711
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  - Accuracy: 0.1762
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  ## Model description
@@ -51,23 +51,28 @@ 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: 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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- | 0.4658 | 5.5556 | 1000 | 2.8590 | 0.1762 |
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- | 0.4956 | 11.1111 | 2000 | 2.9260 | 0.1762 |
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- | 0.349 | 16.6667 | 3000 | 2.9019 | 0.1762 |
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- | 0.2575 | 22.2222 | 4000 | 2.8843 | 0.1762 |
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- | 0.2261 | 27.7778 | 5000 | 2.8242 | 0.1762 |
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- | 0.2592 | 33.3333 | 6000 | 2.8276 | 0.1762 |
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- | 0.1979 | 38.8889 | 7000 | 2.7752 | 0.1762 |
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- | 0.2125 | 44.4444 | 8000 | 2.7567 | 0.1762 |
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- | 0.157 | 50.0 | 9000 | 2.7864 | 0.1762 |
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- | 0.1751 | 55.5556 | 10000 | 2.7711 | 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: 2.6888
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  - Accuracy: 0.1762
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  ## Model description
 
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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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