metadata
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: >
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
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 ROGUE-1
type: rogue-1
value: 46.6619
- name: Validation ROGUE-2
type: rogue-2
value: 23.3285
- name: Validation ROGUE-L
type: rogue-l
value: 39.4811
- name: Test ROGUE-1
type: rogue-1
value: 44.9932
- name: Test ROGUE-2
type: rogue-2
value: 21.7286
- name: Test ROGUE-L
type: rogue-l
value: 38.1921
bart-base-samsum
This model was obtained by fine-tuning facebook/bart-base on Samsum dataset.
Usage
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 |