transformers safetensors pegasus text2text-generation generated_from_trainer dataset:samsum base_model:google/pegasus-large model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/pegasus-large
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: samsum_5535_pegasus-large
  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: 0.5273
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# samsum_5535_pegasus-large

This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4847
- Rouge1: 0.5273
- Rouge2: 0.2794
- Rougel: 0.4378
- Rougelsum: 0.4376
- Gen Len: 22.6565

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.5641        | 4.34  | 500  | 0.5145          | 0.5069 | 0.2664 | 0.4229 | 0.4228    | 21.187  |
| 0.4689        | 8.69  | 1000 | 0.4847          | 0.5273 | 0.2794 | 0.4378 | 0.4376    | 22.6565 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
