pytorch bart text2text-generation en dataset:samsum transformers seq2seq summarization license:apache-2.0 model-index autotrain_compatible
---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
    Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
    \ ok.\nJeff: and how can I get started? \nJeff: where can I find documentation?\
    \ \nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face\n"
model-index:
- name: bart-base-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: 46.6619
    - name: Validation ROUGE-2
      type: rouge-2
      value: 23.3285
    - name: Validation ROUGE-L
      type: rouge-l
      value: 39.4811
    - name: Test ROUGE-1
      type: rouge-1
      value: 44.9932
    - name: Test ROUGE-2
      type: rouge-2
      value: 21.7286
    - name: Test ROUGE-L
      type: rouge-l
      value: 38.1921
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: test
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 45.0148
      verified: true
    - name: ROUGE-2
      type: rouge
      value: 21.6861
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 38.1728
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 41.2794
      verified: true
    - name: loss
      type: loss
      value: 1.597476601600647
      verified: true
    - name: gen_len
      type: gen_len
      value: 17.6606
      verified: true
---
## `bart-base-samsum`
This model was obtained by fine-tuning `facebook/bart-base` on Samsum dataset.

## Usage
```python
from transformers import pipeline

summarizer = pipeline("summarization", model="lidiya/bart-base-samsum")
conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? 
Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
Jeff: ok.
Jeff: and how can I get started? 
Jeff: where can I find documentation? 
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face                                           
'''
summarizer(conversation)
```

## Training procedure
- Colab notebook: https://colab.research.google.com/drive/1RInRjLLso9E2HG_xjA6j8JO3zXzSCBRF?usp=sharing

## Results
| key | value |
| --- | ----- |
| eval_rouge1 | 46.6619 |
| eval_rouge2 | 23.3285 |
| eval_rougeL | 39.4811 |
| eval_rougeLsum | 43.0482 |
| test_rouge1 | 44.9932 |
| test_rouge2 | 21.7286 |
| test_rougeL | 38.1921 |
| test_rougeLsum | 41.2672 |
