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FYP_qa_final

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  1. README.md +11 -6
  2. model.safetensors +1 -1
  3. runs/Apr07_14-04-10_b3b10cc61d93/events.out.tfevents.1712498651.b3b10cc61d93.8066.0 +2 -2
README.md CHANGED
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [kxx-kkk/FYP_sq2_mrqa_adqa_synqa_quoref](https://huggingface.co/kxx-kkk/FYP_sq2_mrqa_adqa_synqa_quoref) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.7986
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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: 1e-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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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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- | 2.7022 | 0.8 | 500 | 3.2057 |
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- | 2.5208 | 1.59 | 1000 | 2.9619 |
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- | 2.4266 | 2.39 | 1500 | 2.7986 |
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [kxx-kkk/FYP_sq2_mrqa_adqa_synqa_quoref](https://huggingface.co/kxx-kkk/FYP_sq2_mrqa_adqa_synqa_quoref) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.3814
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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: 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: 4
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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 |
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  |:-------------:|:-----:|:----:|:---------------:|
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+ | 2.7153 | 0.48 | 300 | 2.8860 |
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+ | 2.5698 | 0.96 | 600 | 2.6071 |
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+ | 2.4486 | 1.44 | 900 | 2.5039 |
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+ | 2.3583 | 1.92 | 1200 | 2.4549 |
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+ | 2.332 | 2.4 | 1500 | 2.4043 |
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+ | 2.264 | 2.88 | 1800 | 2.4150 |
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+ | 2.232 | 3.35 | 2100 | 2.3507 |
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+ | 2.201 | 3.83 | 2400 | 2.3814 |
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  ### Framework versions
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