---
license: mit
tags:
- kobart-hashtag
- generated_from_trainer
base_model: gogamza/kobart-summarization
model-index:
- name: modelling
  results: []
---



# modelling

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: 0.5862

## Model description

This model generates hash tag from input text.  


## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8136        | 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


### 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.

```python
from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration
tokenizer = PreTrainedTokenizerFast.from_pretrained("jjae/kobart-hashtag")
model = BartForConditionalGeneration.from_pretrained("jjae/kobart-hashtag")

def make_tag(text):
  input_ids = tokenizer.encode(text, return_tensors="pt").to(device)
  output = model.generate(input_ids = input_ids, bos_token_id = model.config.bos_token_id,
                          eos_token_id = model.config.eos_token_id, length_penalty = 3.0, max_length = 50, num_beams = 4)
  decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
  return decoded_output
```