---
pipeline_tag: summarization
datasets:
- samsum
language:
- en
metrics:
- rouge
library_name: transformers
widget:
- text: | 
    John: Hey! I've been thinking about getting a PlayStation 5. Do you think it is worth it? 
    Dan: Idk man. R u sure ur going to have enough free time to play it? 
    John: Yeah, that's why I'm not sure if I should buy one or not. I've been working so much lately idk if I'm gonna be able to play it as much as I'd like.
- text: | 
    Sarah: Do you think it's a good idea to invest in Bitcoin?
    Emily: I'm skeptical. The market is very volatile, and you could lose money.
    Sarah: True. But there's also a high upside, right?
- text: | 
    Madison: Hello Lawrence are you through with the article?
    Lawrence: Not yet sir.
    Lawrence: But i will be in a few.
    Madison: Okay. But make it quick.
    Madison: The piece is needed by today
    Lawrence: Sure thing
    Lawrence: I will get back to you once i am through."

model-index:
  - name: bart-finetuned-samsum
    results:
      - task:
          name: Text Summarization
          type: summarization
        dataset:
          name: SamSum
          type: samsum
        metrics:
          - name: Validation ROUGE-1
            type: rouge-1
            value: 53.8804
          - name: Validation ROUGE-2
            type: rouge-2
            value: 29.2329
          - name: Validation ROUGE-L
            type: rougeL
            value: 44.774
          - name: Validation ROUGE-L Sum
            type: rougeLsum
            value: 49.8255
          - name: Test ROUGE-1
            type: rouge-1
            value: 52.8156
          - name: Test ROUGE-2
            type: rouge-2
            value: 28.1259
          - name: Test ROUGE-L
            type: rougeL
            value: 43.7147
          - name: Test ROUGE-L Sum
            type: rougeLsum
            value: 48.5712
---

# Description

This model is a specialized adaptation of the <b>facebook/bart-large-xsum</b>, fine-tuned for enhanced performance on dialogue summarization using the <b>SamSum</b> dataset.

## Development
- Kaggle Notebook: [Text Summarization with Large Language Models](https://www.kaggle.com/code/lusfernandotorres/text-summarization-with-large-language-models)

## Usage

```python
from transformers import pipeline

model = pipeline("summarization", model="luisotorres/bart-finetuned-samsum")

conversation = '''Sarah: Do you think it's a good idea to invest in Bitcoin?
    Emily: I'm skeptical. The market is very volatile, and you could lose money.
    Sarah: True. But there's also a high upside, right?                                     
'''
model(conversation)
```

## Training Parameters
```python
evaluation_strategy = "epoch",
save_strategy = 'epoch',
load_best_model_at_end = True,
metric_for_best_model = 'eval_loss',
seed = 42,
learning_rate=2e-5,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=2,
weight_decay=0.01,
save_total_limit=2,
num_train_epochs=4,
predict_with_generate=True,
fp16=True,
report_to="none"
```

## Reference
This model is based on the original <b>BART</b> architecture, as detailed in:

Lewis et al. (2019). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. [arXiv:1910.13461](https://arxiv.org/abs/1910.13461)