---
library_name: transformers
base_model: google/pegasus-cnn_dailymail
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-finetuned-samsum
  results:
  - task:
      type: summarization
    dataset:
      name: samsum
      type: samsum
    metrics:
    - name: ROUGE-1
      type: ROUGE-1
      value: 0.44030729209257835
    - name: ROUGE-2
      type: ROUGE-2
      value: 0.21015363722992564
    - name: ROUGE-L
      type: ROUGE-L
      value: 0.3491351151672408
    - name: ROUGE-Lsum
      type: ROUGE-Lsum
      value: 0.3492708872856547
language:
- en
pipeline_tag: summarization
metrics:
- rouge
---

<!-- 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-finetuned-samsum

This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4245
- Rouge-1: 0.4403
- Rouge-2: 0.2102
- Rouge-L: 0.3491
- Rouge-Lsum: 0.3493

## Model description

This would be the 10th best-performing model on the [Papers with Code SAMsum leaderboard](https://paperswithcode.com/sota/text-summarization-on-samsum-corpus),
as of April 8, 2025.

## Intended uses & limitations

This model is designed for abstractive dialogue summarization. It can take in multi-turn conversations and generate concise summaries.

## How to Use

```Python
from transformers import pipeline

# Load summarization pipeline
model_name = "avanishd/pegasus-finetuned-samsum/"
summarizer = pipeline("summarization", model=model_name, tokenizer=model_name)

# Sample conversation
dialogue = """
John: Hey, are you free tomorrow?
Alice: I think so, why?
John: Want to catch a movie or grab lunch?
Alice: Sure, lunch sounds good. What time?
John: Let's say 1 PM at the new place downtown?
Alice: Works for me!
"""

# Generate summary
summary = summarizer(dialogue, max_length=60, min_length=15, do_sample=False)[0]['summary_text']

print("Summary:", summary)

```

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5798        | 0.9992 | 920  | 1.4245          |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1