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---
base_model: haesun/pegasus-samsum
tags:
- generated_from_trainer
model-index:
- name: pegasus-ft
  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. -->

# pegasus-ft

This model is a fine-tuned version of [haesun/pegasus-samsum](https://huggingface.co/haesun/pegasus-samsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9779

## 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: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- 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: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1074        | 0.19  | 100  | 2.0692          |
| 1.9296        | 0.37  | 200  | 2.0481          |
| 1.9456        | 0.56  | 300  | 2.0364          |
| 1.9674        | 0.74  | 400  | 2.0205          |
| 2.0887        | 0.93  | 500  | 2.0043          |
| 2.0733        | 1.11  | 600  | 2.0023          |
| 1.9111        | 1.3   | 700  | 1.9991          |
| 1.887         | 1.48  | 800  | 1.9917          |
| 2.0347        | 1.67  | 900  | 1.9855          |
| 1.8488        | 1.85  | 1000 | 1.9838          |
| 1.7699        | 2.04  | 1100 | 1.9800          |
| 1.7386        | 2.22  | 1200 | 1.9795          |
| 1.8828        | 2.41  | 1300 | 1.9804          |
| 1.8072        | 2.59  | 1400 | 1.9808          |
| 1.898         | 2.78  | 1500 | 1.9785          |
| 1.9452        | 2.96  | 1600 | 1.9779          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2