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retnet-summarization_small

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retnet-summarization-small

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Files changed (5) hide show
  1. README.md +11 -9
  2. config.json +1 -0
  3. model.safetensors +1 -1
  4. runs/Nov13_00-50-05_b14393ba9044/events.out.tfevents.1699836605.b14393ba9044.648.0 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
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  ---
 
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  tags:
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  - generated_from_trainer
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  datasets:
@@ -13,9 +14,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # retnet-summarization_small
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- This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 5.1335
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  ## Model description
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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: 4
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- - eval_batch_size: 4
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 16
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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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- | 5.606 | 1.0 | 11525 | 5.1335 |
 
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  ### Framework versions
 
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  ---
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+ base_model: kaizerBox/retnet-summarization_small
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  # retnet-summarization_small
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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: 4.8029
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  ## Model description
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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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  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 40
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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: 2
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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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+ | 5.1425 | 1.0 | 4610 | 4.8581 |
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+ | 4.9561 | 2.0 | 9220 | 4.8029 |
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  ### Framework versions
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  "activation_dropout": 0.0,
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  "activation_fn": "swish",
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  "activation_dropout": 0.0,
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  "activation_fn": "swish",
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  "architectures": [
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