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--- |
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license: mit |
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tags: |
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- kobart-hashtag |
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- generated_from_trainer |
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base_model: gogamza/kobart-summarization |
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model-index: |
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- name: modelling |
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results: [] |
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--- |
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# modelling |
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This model is a fine-tuned version of [gogamza/kobart-summarization](https://huggingface.co/gogamza/kobart-summarization) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5862 |
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## Model description |
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This model generates hash tag from input text. |
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## Training and evaluation data |
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This model was trained by the self-instruction process. |
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All data used for fine-tuning this model were generated by chatGPT 3.5. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5.6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.8136 | 1.42 | 500 | 0.6526 | |
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| 0.4651 | 2.85 | 1000 | 0.5862 | |
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| 0.2643 | 4.27 | 1500 | 0.6752 | |
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| 0.1642 | 5.7 | 2000 | 0.6840 | |
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| 0.1078 | 7.12 | 2500 | 0.7554 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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### How to Get Started with the Model |
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Use the code below to get started with the model. |
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You can adjust hyperparameters to fit on your data. |
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```python |
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from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration |
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tokenizer = PreTrainedTokenizerFast.from_pretrained("jjae/kobart-hashtag") |
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model = BartForConditionalGeneration.from_pretrained("jjae/kobart-hashtag") |
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def make_tag(text): |
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input_ids = tokenizer.encode(text, return_tensors="pt").to(device) |
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output = model.generate(input_ids = input_ids, bos_token_id = model.config.bos_token_id, |
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eos_token_id = model.config.eos_token_id, length_penalty = 3.0, max_length = 50, num_beams = 4) |
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decoded_output = tokenizer.decode(output[0], skip_special_tokens=True) |
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return decoded_output |
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``` |
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