bart-cnn-summarizer
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.5714 | 1 | 3.7446 |
Framework versions
- Transformers 4.46.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.20.3
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Model tree for yoeel/bart-cnn-summarizer
Base model
facebook/bart-base