Hugging Face's logo

philschmid
/
bart-large-cnn-samsum

Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results
Inference Endpoints
Model card Files Community
19
bart-large-cnn-samsum
File size: 3,234 Bytes 
79540ca
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
 

---
language: en
tags:
- sagemaker
- bart
- 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-large-cnn-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: 42.621
       - name: Validation ROGUE-2
         type: rogue-2
         value: 21.9825
       - name: Validation ROGUE-L
         type: rogue-l
         value: 33.034
       - name: Test ROGUE-1
         type: rogue-1
         value: 41.3174
       - name: Test ROGUE-2
         type: rogue-2
         value: 20.8716
       - name: Test ROGUE-L
         type: rogue-l
         value: 32.1337
---

## `bart-large-cnn-samsum`

This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.

For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
- [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
- [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
- [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)

## Hyperparameters
```json
{
    "dataset_name": "samsum",
    "do_eval": true,
    "do_predict": true,
    "do_train": true,
    "fp16": true,
    "learning_rate": 5e-05,
    "model_name_or_path": "facebook/bart-large-cnn",
    "num_train_epochs": 3,
    "output_dir": "/opt/ml/model",
    "per_device_eval_batch_size": 4,
    "per_device_train_batch_size": 4,
    "predict_with_generate": true,
    "seed": 7
}
```

## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-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                                           
'''
nlp(conversation)
```

## Results

| key | value |
| --- | ----- |
| eval_rouge1 | 42.621 |
| eval_rouge2 | 21.9825 |
| eval_rougeL | 33.034 |
| eval_rougeLsum | 39.6783 |
| test_rouge1 | 41.3174 |
| test_rouge2 | 20.8716 |
| test_rougeL | 32.1337 |
| test_rougeLsum | 38.4149 |