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retnet-summarization-small

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  1. README.md +9 -7
  2. model.safetensors +1 -1
  3. runs/Nov16_19-21-08_0302d3c57c64/events.out.tfevents.1700162469.0302d3c57c64.293.0 +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 [kaizerBox/retnet-summarization_small](https://huggingface.co/kaizerBox/retnet-summarization_small) on the xsum dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 4.1604
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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: 0.0006
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  - train_batch_size: 10
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  - eval_batch_size: 10
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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: cosine
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  - lr_scheduler_warmup_steps: 10
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- - num_epochs: 1
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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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- | 4.3639 | 1.0 | 4610 | 4.1604 |
 
 
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  ### Framework versions
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  - Transformers 4.35.2
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  - Pytorch 2.1.0+cu118
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- - Datasets 2.14.7
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  - Tokenizers 0.15.0
 
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  This model is a fine-tuned version of [kaizerBox/retnet-summarization_small](https://huggingface.co/kaizerBox/retnet-summarization_small) on the xsum dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 4.1548
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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: 0.0001
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  - train_batch_size: 10
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  - eval_batch_size: 10
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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: cosine
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  - lr_scheduler_warmup_steps: 10
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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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+ | 4.3508 | 1.0 | 4610 | 4.1581 |
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+ | 4.3471 | 2.0 | 9220 | 4.1551 |
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+ | 4.3446 | 3.0 | 13830 | 4.1548 |
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  ### Framework versions
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  - Transformers 4.35.2
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  - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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  - Tokenizers 0.15.0
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