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---
license: mit
tags:
- generated_from_trainer
datasets:
- aihub_paper_summarization
metrics:
- rouge
model-index:
- name: kobart-base-v2-finetuned-paper
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: aihub_paper_summarization
type: aihub_paper_summarization
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 6.2883
---
<!-- 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. -->
# kobart-base-v2-finetuned-paper
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the aihub_paper_summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2966
- Rouge1: 6.2883
- Rouge2: 1.7038
- Rougel: 6.2556
- Rougelsum: 6.2618
- Gen Len: 20.0
## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.2215 | 1.0 | 8831 | 1.3293 | 6.2425 | 1.7317 | 6.2246 | 6.2247 | 20.0 |
| 1.122 | 2.0 | 17662 | 1.3056 | 6.2298 | 1.7005 | 6.2042 | 6.2109 | 20.0 |
| 1.0914 | 3.0 | 26493 | 1.2966 | 6.2883 | 1.7038 | 6.2556 | 6.2618 | 20.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
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