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BaSum
This model is a fine-tuned version of digit82/kobart-summarization on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.5612
- eval_rouge1: 8.1463
- eval_rouge2: 1.662
- eval_rougeL: 8.0941
- eval_rougeLsum: 8.1522
- eval_gen_len: 30.0
- eval_runtime: 72.334
- eval_samples_per_second: 11.17
- eval_steps_per_second: 0.705
- epoch: 3.47
- step: 3500
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 69
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2