---
base_model: google/pegasus-large
tags:
- generated_from_trainer
model-index:
- name: pegasus-large
  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-large

This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6537

## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.7374        | 0.0   | 500   | 3.7869          |
| 0.6947        | 0.01  | 1000  | 0.7657          |
| 0.7536        | 0.01  | 1500  | 0.7410          |
| 0.6689        | 0.01  | 2000  | 0.7213          |
| 0.662         | 0.02  | 2500  | 0.7120          |
| 0.7537        | 0.02  | 3000  | 0.7015          |
| 0.79          | 0.02  | 3500  | 0.6990          |
| 0.6765        | 0.03  | 4000  | 0.6920          |
| 0.7353        | 0.03  | 4500  | 0.6881          |
| 0.633         | 0.03  | 5000  | 0.6858          |
| 0.5015        | 0.04  | 5500  | 0.6828          |
| 0.8227        | 0.04  | 6000  | 0.6806          |
| 0.832         | 0.05  | 6500  | 0.6790          |
| 0.587         | 0.05  | 7000  | 0.6743          |
| 0.6121        | 0.05  | 7500  | 0.6726          |
| 0.621         | 0.06  | 8000  | 0.6713          |
| 0.5112        | 0.06  | 8500  | 0.6714          |
| 0.6596        | 0.06  | 9000  | 0.6677          |
| 0.7421        | 0.07  | 9500  | 0.6682          |
| 0.5891        | 0.07  | 10000 | 0.6655          |
| 0.596         | 0.07  | 10500 | 0.6660          |
| 0.7527        | 0.08  | 11000 | 0.6639          |
| 0.8404        | 0.08  | 11500 | 0.6620          |
| 0.6896        | 0.08  | 12000 | 0.6624          |
| 0.7312        | 0.09  | 12500 | 0.6598          |
| 0.7061        | 0.09  | 13000 | 0.6591          |
| 0.5983        | 0.09  | 13500 | 0.6594          |
| 0.659         | 0.1   | 14000 | 0.6587          |
| 0.8656        | 0.1   | 14500 | 0.6568          |
| 0.7991        | 0.1   | 15000 | 0.6571          |
| 0.6637        | 0.11  | 15500 | 0.6571          |
| 0.5115        | 0.11  | 16000 | 0.6558          |
| 0.6464        | 0.11  | 16500 | 0.6566          |
| 0.6673        | 0.12  | 17000 | 0.6550          |
| 0.6477        | 0.12  | 17500 | 0.6544          |
| 0.8145        | 0.13  | 18000 | 0.6542          |
| 0.6216        | 0.13  | 18500 | 0.6537          |
| 0.943         | 0.13  | 19000 | 0.6539          |
| 0.788         | 0.14  | 19500 | 0.6538          |
| 0.5921        | 0.14  | 20000 | 0.6537          |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1