---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v11
  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_v11

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: 1.6843
- Rouge1 Precision: 0.7035
- Rouge1 Recall: 0.1205
- Rouge1 Fmeasure: 0.2038

## 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: 5e-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
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 2.6071        | 0.9714 | 17   | 1.8938          | 0.6021           | 0.1074        | 0.1803          |
| 1.745         | 2.0    | 35   | 1.7661          | 0.7095           | 0.1174        | 0.1994          |
| 1.5717        | 2.9714 | 52   | 1.7251          | 0.6704           | 0.1176        | 0.1968          |
| 1.4921        | 4.0    | 70   | 1.6772          | 0.7014           | 0.1175        | 0.1986          |
| 1.3932        | 4.9714 | 87   | 1.6745          | 0.7008           | 0.1187        | 0.2011          |
| 1.3002        | 6.0    | 105  | 1.6869          | 0.6913           | 0.1196        | 0.2012          |
| 1.2784        | 6.9714 | 122  | 1.6857          | 0.7114           | 0.1246        | 0.2097          |
| 1.1779        | 7.7714 | 136  | 1.6843          | 0.7035           | 0.1205        | 0.2038          |


### Framework versions

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