metadata
license: mit
base_model: gogamza/kobart-summarization
tags:
- kobart-hashtag
- generated_from_trainer
model-index:
- name: modelling
results: []
modelling
This model is a fine-tuned version of gogamza/kobart-summarization on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5862
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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.8136 | 1.42 | 500 | 0.6526 |
| 0.4651 | 2.85 | 1000 | 0.5862 |
| 0.2643 | 4.27 | 1500 | 0.6752 |
| 0.1642 | 5.7 | 2000 | 0.6840 |
| 0.1078 | 7.12 | 2500 | 0.7554 |
Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0