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---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: exp2-led-risalah_data_v7-fix
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/silmiaulia/huggingface/runs/2a3srq9p)
# exp2-led-risalah_data_v7-fix
This model is a fine-tuned version of [silmi224/finetune-led-35000](https://huggingface.co/silmi224/finetune-led-35000) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6801
- Rouge1: 20.0364
- Rouge2: 9.57
- Rougel: 13.9743
- Rougelsum: 14.0563
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.8706 | 1.0 | 10 | 3.3282 | 9.2634 | 1.825 | 6.2857 | 6.6749 |
| 3.5173 | 2.0 | 20 | 2.8713 | 9.381 | 1.5365 | 6.5965 | 6.6722 |
| 3.0587 | 3.0 | 30 | 2.5101 | 12.3761 | 3.5034 | 8.6155 | 8.7913 |
| 2.7254 | 4.0 | 40 | 2.2919 | 14.8916 | 4.9071 | 10.0 | 9.9487 |
| 2.504 | 5.0 | 50 | 2.1490 | 14.5316 | 4.9407 | 9.6973 | 9.5973 |
| 2.3306 | 6.0 | 60 | 2.0516 | 15.6234 | 5.419 | 10.6929 | 10.671 |
| 2.1991 | 7.0 | 70 | 1.9705 | 16.9222 | 6.1531 | 10.3785 | 10.4171 |
| 2.0922 | 8.0 | 80 | 1.9114 | 15.9531 | 6.007 | 10.2455 | 10.2734 |
| 2.0108 | 9.0 | 90 | 1.8601 | 16.3146 | 6.2786 | 10.632 | 10.6027 |
| 1.9243 | 10.0 | 100 | 1.8352 | 18.1771 | 6.6919 | 11.1811 | 11.2366 |
| 1.8675 | 11.0 | 110 | 1.7865 | 17.2554 | 7.4135 | 10.5322 | 10.5689 |
| 1.8066 | 12.0 | 120 | 1.7520 | 15.8483 | 7.1825 | 10.7059 | 10.7344 |
| 1.7476 | 13.0 | 130 | 1.7341 | 16.0049 | 6.6876 | 10.9744 | 10.9918 |
| 1.6911 | 14.0 | 140 | 1.7126 | 17.6921 | 8.9076 | 12.8474 | 12.8966 |
| 1.6388 | 15.0 | 150 | 1.6960 | 19.7192 | 9.1168 | 13.3649 | 13.3949 |
| 1.5902 | 16.0 | 160 | 1.6783 | 20.7583 | 9.7459 | 14.1533 | 14.1794 |
| 1.5433 | 17.0 | 170 | 1.6476 | 19.4203 | 9.4624 | 13.3403 | 13.401 |
| 1.4992 | 18.0 | 180 | 1.6450 | 18.74 | 8.8791 | 13.3925 | 13.3709 |
| 1.4614 | 19.0 | 190 | 1.6335 | 19.476 | 9.0282 | 13.5223 | 13.4966 |
| 1.4216 | 20.0 | 200 | 1.6246 | 17.6435 | 7.9777 | 13.1255 | 13.1599 |
| 1.3842 | 21.0 | 210 | 1.6102 | 18.6282 | 8.511 | 12.8825 | 12.7954 |
| 1.3479 | 22.0 | 220 | 1.6200 | 18.066 | 8.4414 | 12.467 | 12.4232 |
| 1.3087 | 23.0 | 230 | 1.6350 | 17.8312 | 8.6603 | 12.522 | 12.511 |
| 1.2752 | 24.0 | 240 | 1.6186 | 18.5374 | 9.7206 | 13.0955 | 13.0266 |
| 1.2434 | 25.0 | 250 | 1.6219 | 18.232 | 7.9904 | 12.7029 | 12.6916 |
| 1.2046 | 26.0 | 260 | 1.6393 | 17.4585 | 7.2075 | 12.5202 | 12.4766 |
| 1.1716 | 27.0 | 270 | 1.6139 | 19.6477 | 9.9919 | 14.3408 | 14.346 |
| 1.1388 | 28.0 | 280 | 1.6416 | 19.7279 | 8.8207 | 13.6708 | 13.7072 |
| 1.1083 | 29.0 | 290 | 1.6485 | 19.1252 | 9.2133 | 13.6003 | 13.6412 |
| 1.0745 | 30.0 | 300 | 1.6801 | 20.0364 | 9.57 | 13.9743 | 14.0563 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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