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---
base_model: google/pegasus-x-large
tags:
- summarization
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: pegasus-x-large-finetuned-samsum1000
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 46.6996
---
<!-- 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-x-large-finetuned-samsum1000
This model is a fine-tuned version of [google/pegasus-x-large](https://huggingface.co/google/pegasus-x-large) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4802
- Rouge1: 46.6996
- Rouge2: 21.5586
- Rougel: 38.1002
- Rougelsum: 41.42
## 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: 5.6e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.7681 | 1.0 | 500 | 1.4689 | 47.1766 | 21.8869 | 38.8854 | 42.9534 |
| 1.4626 | 2.0 | 1000 | 1.4781 | 46.6978 | 20.786 | 37.764 | 41.2028 |
| 1.3591 | 3.0 | 1500 | 1.4804 | 47.1756 | 21.8821 | 38.2072 | 41.6812 |
| 1.3466 | 4.0 | 2000 | 1.4804 | 46.9411 | 21.5169 | 38.18 | 41.471 |
| 1.3464 | 5.0 | 2500 | 1.4803 | 46.8083 | 21.5333 | 38.1539 | 41.4872 |
| 1.3353 | 6.0 | 3000 | 1.4804 | 46.6675 | 21.1336 | 37.7059 | 41.0869 |
| 1.3483 | 7.0 | 3500 | 1.4803 | 46.6768 | 21.1916 | 37.7642 | 41.1696 |
| 1.3536 | 8.0 | 4000 | 1.4804 | 46.7311 | 21.5169 | 38.057 | 41.42 |
| 1.3533 | 9.0 | 4500 | 1.4802 | 46.6403 | 21.529 | 37.9922 | 41.3437 |
| 1.3469 | 10.0 | 5000 | 1.4802 | 46.6996 | 21.5586 | 38.1002 | 41.42 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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