bart-large-xsum-finetuned-en-sum-2
This model is a fine-tuned version of october-sd/bart-large-xsum-finetuned-en-sum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6259
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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.99 | 126 | 1.6222 |
| No log | 1.98 | 252 | 1.6259 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.2
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Model tree for october-sd/bart-large-xsum-finetuned-en-sum-2
Base model
facebook/bart-large-xsum