---
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 facebook/bart-large-xsum, fine-tuned for enhanced performance on dialogue summarization using the SamSum 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 BART 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)