transformers tensorboard safetensors pegasus_x text2text-generation summarization generated_from_trainer dataset:samsum base_model:google/pegasus-x-large base_model:finetune:google/pegasus-x-large model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/pegasus-x-large
datasets:
- samsum
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: pegasus-x-large-finetuned-samsum1000
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: validation
      args: samsum
    metrics:
    - type: rouge
      value: 46.6996
      name: Rouge1
---

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# 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
