bart-base-samsum / README.md
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philschmid
HF staff
Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#2)
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---
language: en
license: apache-2.0
tags:
- sagemaker
- bart
- summarization
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\
\ "
model-index:
- name: philschmid/bart-base-samsum
results:
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 45.3438
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2JhY2U3M2ViYTVhNTAzM2M3NjhjMzBjYTk0N2I2MzlmN2Q0N2M1YzFlNGU1ZWVlMGI1YjYzMzZhYjNmMDk1MCIsInZlcnNpb24iOjF9.tLr7VUXSYDd9LaMtVIV8dheZRxX7pf1kyn9Kd4MQY8L_pj13_CeWenqOauVsHzRAZ5Jt5RuHjYFBWbV2TNjvDQ
- type: rouge
value: 21.6953
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmExODAyMTcwNjU5MjM0MzkzNjZlMGY5YzMyMjNiZjM5OWQ5NzFhODIyMWJiYjUwZGY4ZGM0MzE5OTJiYzEyMSIsInZlcnNpb24iOjF9.qR_Cge1A4NfJL_do4W7Y1kHxU0L98Ds6tbZy-4e-FVNW4aG5zRBxgOX8ieB93N2E19gtzqGE6BdpQfVcZAgXBQ
- type: rouge
value: 38.1365
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTA5ZTgyNDYxNzgzN2FhNTBlN2NjNzE0MDgyMzZkMTNjMGUyMDk3N2EzOThhMGFhZTQyYzZhZjQ5NjlkOTVlYyIsInZlcnNpb24iOjF9.dKns4BLmyWGUWweYSLYFttHIoWw57z1GKnvatMjkyVvcgwd_iF9imZ7QnJjjLAkc-AUMwwoxoOjEVF8FNf8JBA
- type: rouge
value: 41.5913
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmJiMzY3ODEwY2Q0YzNjM2QwMjI2MGRmOTEyYjQ3ZmNhZThmYWUxNDJkZDY1NTg3NGQzOGI0YmZlYjI2MDNlZSIsInZlcnNpb24iOjF9.pBrKwWa1mjacdhXSXMUQ0nv1wbcwscW_9uVFkicF2PbJ-JQjzUbL10Jy-b_yBOiJeY5I9ApJySgUH5JMq3_pBg
- type: loss
value: 1.5832244157791138
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWZhNGZjNjJiODIyNDU0NjZjMGExOWE1NWJhMmFiOGY5MDNiZWY0MjExYzA3Njg1OTJhNjEyZjI2MTg0N2I5YiIsInZlcnNpb24iOjF9.T6xwQM5yZ8eD8upqo5zjcUxcX0mqY9wx7f8j0zN9txAe39hURHY-8ibLYJvWckepTvpdUA6is4AC9RUWia24AA
- type: gen_len
value: 17.9927
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzU4ZGI1ZjJlMjg0NTBkYzlkOWQzMWUzZDZkODZkZjVhNTAyMTI4YTA2MWExM2U2YTQwM2YxMDQ2ODE0Yjc0NSIsInZlcnNpb24iOjF9.mDGhriDLXIJq_yb3Yqj6MBJSCxXXrRN1LfHsGkV8i1oOpkLiSLic7D8fSFMdTZTkl2XmzQfkVU2Wv298YyQEBg
---
## `bart-base-samsum`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
You can find the notebook [here]() and the referring blog post [here]().
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_train": true,
"fp16": true,
"learning_rate": 5e-05,
"model_name_or_path": "facebook/bart-base",
"num_train_epochs": 3,
"output_dir": "/opt/ml/model",
"per_device_eval_batch_size": 8,
"per_device_train_batch_size": 8,
"seed": 7
}
```
## Train results
| key | value |
| --- | ----- |
| epoch | 3 |
| init_mem_cpu_alloc_delta | 180190 |
| init_mem_cpu_peaked_delta | 18282 |
| init_mem_gpu_alloc_delta | 558658048 |
| init_mem_gpu_peaked_delta | 0 |
| train_mem_cpu_alloc_delta | 6658519 |
| train_mem_cpu_peaked_delta | 642937 |
| train_mem_gpu_alloc_delta | 2267624448 |
| train_mem_gpu_peaked_delta | 10355728896 |
| train_runtime | 98.4931 |
| train_samples | 14732 |
| train_samples_per_second | 3.533 |
## Eval results
| key | value |
| --- | ----- |
| epoch | 3 |
| eval_loss | 1.5356481075286865 |
| eval_mem_cpu_alloc_delta | 659047 |
| eval_mem_cpu_peaked_delta | 18254 |
| eval_mem_gpu_alloc_delta | 0 |
| eval_mem_gpu_peaked_delta | 300285440 |
| eval_runtime | 0.3116 |
| eval_samples | 818 |
| eval_samples_per_second | 2625.337 |
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/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
'''
nlp(conversation)
```