|
--- |
|
license: mit |
|
tags: |
|
- kobart-summarization-diary |
|
- generated_from_trainer |
|
base_model: gogamza/kobart-summarization |
|
model-index: |
|
- name: summary |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# summary |
|
|
|
This model is a fine-tuned version of [gogamza/kobart-summarization](https://huggingface.co/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) |
|
''' |
