metadata
license: mit
tags:
- kobart-summarization-diary
- generated_from_trainer
base_model: gogamza/kobart-summarization
model-index:
- name: summary
results: []
summary
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: 0.4011
Model description
This model summarizes the diary.
Training and evaluation data
This model was trained by the self-instruction process. All data used for fine-tuning this model were generated by chatGPT 3.5.
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 |
|---|---|---|---|
| 1.4804 | 1.47 | 500 | 0.4035 |
| 0.2475 | 2.93 | 1000 | 0.4011 |
| 0.1249 | 4.4 | 1500 | 0.4591 |
| 0.072 | 5.87 | 2000 | 0.4671 |
| 0.039 | 7.33 | 2500 | 0.5022 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
How to Get Started with the Model
Use the code below to get started with the model. You can adjust hyperparameters to fit on your data.
def diary_summary(text):
input_ids = tokenizer.encode(text, return_tensors = 'pt').to(device)
summary_text_ids = model.generate(input_ids = input_ids, bos_token_id = model.config.bos_token_id, eos_token_id = model.config.eos_token_id,
length_penalty = 2.0, max_length = 150, num_beams = 2)
return tokenizer.decode(summary_text_ids[0], skip_special_tokens = True)