---
language:
- en
- pt
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: |
    Bruno: Ei, você tem o número da Betty?
    Kleber: Desculpe, não encontrei.
    Bruno: Pesquise.
    Kleber: De acordo com minha pesquisa o número é 35 9 9898-6142
    Bruno: Obrigado.
model-index:
- name: bart-large-xsum-samsum
  results:
  - task:
      name: Abstractive Text Summarization
      type: abstractive-text-summarization
    dataset:
      name: >-
        SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive
        Summarization
      type: samsum
    metrics:
    - name: Validation ROUGE-1
      type: rouge-1
      value: 54.3921
    - name: Validation ROUGE-2
      type: rouge-2
      value: 29.8078
    - name: Validation ROUGE-L
      type: rouge-l
      value: 45.1543
    - name: Test ROUGE-1
      type: rouge-1
      value: 53.3059
    - name: Test ROUGE-2
      type: rouge-2
      value: 28.355
    - name: Test ROUGE-L
      type: rouge-l
      value: 44.0953
metrics:
- accuracy
---
## `bart-large-xsum-samsum`
This model was obtained by fine-tuning `facebook/bart-large-xsum` on [Samsum](https://huggingface.co/datasets/samsum) dataset.
## Usage
```python
from transformers import pipeline

summarizer = pipeline("summarization", model="lidiya/bart-large-xsum-samsum")
conversation = '''Hannah: Hey, do you have Betty's number?
Amanda: Lemme check
Amanda: Sorry, can't find it.
Amanda: Ask Larry
Amanda: He called her last time we were at the park together
Hannah: I don't know him well
Amanda: Don't be shy, he's very nice
Hannah: If you say so..
Hannah: I'd rather you texted him
Amanda: Just text him 🙂
Hannah: Urgh.. Alright
Hannah: Bye
Amanda: Bye bye                                       
'''
summarizer(conversation)
```
## Training procedure
- Colab notebook: https://colab.research.google.com/drive/1dul0Sg-TTMy9xZCJzmDRajXbyzDwtYx6?usp=sharing
## Results
| key | value |
| --- | ----- |
| eval_rouge1 | 54.3921 |
| eval_rouge2 | 29.8078 |
| eval_rougeL | 45.1543 |
| eval_rougeLsum | 49.942 |
| test_rouge1 | 53.3059 |
| test_rouge2 | 28.355 |
| test_rougeL | 44.0953 |
| test_rougeLsum | 48.9246 |