---
base_model: google/pegasus-cnn_dailymail
model-index:
- name: pegasus-samsum
  results: []
datasets:
- Samsung/samsum
language:
- en
metrics:
- rouge
pipeline_tag: summarization
library_name: transformers
---

<!-- 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-samsum

This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on 
[SAMSum](https://huggingface.co/datasets/Samsung/samsum) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3839

# Intended uses & limitations
## Intended uses:
 * Dialogue summarization (e.g., chat logs, meetings)
 * Text summarization for conversational datasets

## Limitations:
 * May struggle with very long conversations or non-dialogue text.

# 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2

## Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6026        | 0.5431 | 500  | 1.4875          |
| 1.4737        | 1.0861 | 1000 | 1.4040          |
| 1.4735        | 1.6292 | 1500 | 1.3839          |

### Test results

| rouge1 | rouge2  | rougeL | rougeLsum Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.427614       | 0.200571 | 0.340648  | 0.340738     |

## How to use
You can use this model with the transformers library for dialogue summarization. Here's an example in Python:

```python
from transformers import pipeline
import torch

device = 0 if torch.cuda.is_available() else -1
pipe = pipeline("summarization",
                model="seddiktrk/pegasus-samsum",
                device=device)

custom_dialogue = """\
Seddik: Hey, have you tried using PEGASUS for summarization?
John: Yeah, I just started experimenting with it last week!
Seddik: It's pretty powerful, especially for abstractive summaries.
John: I agree! The results are really impressive.
Seddik: I was thinking of using it for my next project. Want to collaborate?
John: Absolutely! We could make some awesome improvements together.
Seddik: Perfect, let's brainstorm ideas this weekend.
John: Sounds like a plan!
"""

# Summarize dialogue
gen_kwargs = {"length_penalty": 0.8, "num_beams":8, "max_length": 128}
print(pipe(custom_dialogue, **gen_kwargs)[0]["summary_text"])
```
Example Output
```
John started using PEG for summarization last week. Seddik is thinking of using it for his next project.
John and Seddik will brainstorm ideas this weekend.

```


			
### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1