---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v15
  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. -->

# led-risalah_data_v15

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: 1.6673
- Rouge1 Precision: 0.7043
- Rouge1 Recall: 0.1227
- Rouge1 Fmeasure: 0.2067

## 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: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 3.0403        | 1.0   | 20   | 2.4986          | 0.5024           | 0.0555        | 0.0987          |
| 2.5716        | 2.0   | 40   | 2.1700          | 0.5606           | 0.0817        | 0.1409          |
| 2.2879        | 3.0   | 60   | 2.0072          | 0.5705           | 0.0869        | 0.1492          |
| 2.0807        | 4.0   | 80   | 1.9094          | 0.6048           | 0.0899        | 0.1542          |
| 1.927         | 5.0   | 100  | 1.8184          | 0.5472           | 0.0922        | 0.1561          |
| 1.8368        | 6.0   | 120  | 1.7721          | 0.6079           | 0.1036        | 0.1751          |
| 1.7468        | 7.0   | 140  | 1.7310          | 0.639            | 0.1095        | 0.1842          |
| 1.5913        | 8.0   | 160  | 1.6907          | 0.6637           | 0.1109        | 0.1875          |
| 1.534         | 9.0   | 180  | 1.6843          | 0.6355           | 0.1102        | 0.1851          |
| 1.4835        | 10.0  | 200  | 1.6605          | 0.6596           | 0.1141        | 0.1922          |
| 1.4958        | 11.0  | 220  | 1.6403          | 0.6929           | 0.1162        | 0.1973          |
| 1.4547        | 12.0  | 240  | 1.6347          | 0.6781           | 0.1118        | 0.1892          |
| 1.3069        | 13.0  | 260  | 1.6604          | 0.6626           | 0.1101        | 0.187           |
| 1.2639        | 14.0  | 280  | 1.6712          | 0.697            | 0.1227        | 0.2061          |
| 1.3249        | 15.0  | 300  | 1.6255          | 0.6529           | 0.1135        | 0.1914          |
| 1.185         | 16.0  | 320  | 1.6484          | 0.6806           | 0.1174        | 0.1981          |
| 1.1087        | 17.0  | 340  | 1.6425          | 0.682            | 0.1195        | 0.2008          |
| 1.1125        | 18.0  | 360  | 1.6509          | 0.7122           | 0.1235        | 0.2086          |
| 1.1574        | 19.0  | 380  | 1.6740          | 0.6983           | 0.1214        | 0.2052          |
| 0.9968        | 20.0  | 400  | 1.6673          | 0.7043           | 0.1227        | 0.2067          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1