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resultsara_bertscore

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End of training

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Files changed (4) hide show
  1. README.md +14 -4
  2. model.safetensors +1 -1
  3. runs/Sep03_16-08-27_423de3f4d3a8/events.out.tfevents.1725379718.423de3f4d3a8.820.5 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBART) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5748
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  ## Model description
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@@ -36,13 +36,13 @@ More information needed
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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: 32
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- - eval_batch_size: 32
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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: 500
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- - num_epochs: 3
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  - mixed_precision_training: Native AMP
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  ### Training results
@@ -56,6 +56,16 @@ The following hyperparameters were used during training:
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  | 0.6767 | 2.1313 | 2500 | 0.5775 |
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  | 0.6561 | 2.5575 | 3000 | 0.5758 |
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  | 0.6562 | 2.9838 | 3500 | 0.5748 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBART) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5783
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  ## Model description
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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: 128
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+ - eval_batch_size: 128
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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: 500
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+ - num_epochs: 8
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  - mixed_precision_training: Native AMP
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  ### Training results
 
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  | 0.6767 | 2.1313 | 2500 | 0.5775 |
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  | 0.6561 | 2.5575 | 3000 | 0.5758 |
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  | 0.6562 | 2.9838 | 3500 | 0.5748 |
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+ | 0.6457 | 3.4101 | 4000 | 0.5774 |
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+ | 0.6519 | 3.8363 | 4500 | 0.5755 |
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+ | 0.6396 | 4.2626 | 5000 | 0.5774 |
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+ | 0.626 | 4.6888 | 5500 | 0.5773 |
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+ | 0.6201 | 5.1151 | 6000 | 0.5789 |
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+ | 0.605 | 5.5413 | 6500 | 0.5776 |
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+ | 0.6044 | 5.9676 | 7000 | 0.5770 |
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+ | 0.5899 | 6.3939 | 7500 | 0.5786 |
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+ | 0.5917 | 6.8201 | 8000 | 0.5779 |
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+ | 0.5913 | 7.2464 | 8500 | 0.5783 |
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  ### Framework versions
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