## `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
    {
    "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,
    "sagemaker_container_log_level": 20,
    "sagemaker_job_name": "huggingface-pytorch-training-2021-09-08-06-40-19-182",
    "sagemaker_program": "run_summarization.py",
    "sagemaker_region": "us-west-2",
    "sagemaker_submit_directory": "s3://sagemaker-us-west-2-847380964353/huggingface-pytorch-training-2021-09-08-06-40-19-182/source/sourcedir.tar.gz",
    "seed": 7
}
## Usage
    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 | 42.059 |
| eval_rouge2 | 21.5509 |
| eval_rougeL | 32.4083 |
| eval_rougeLsum | 39.0015 |
| test_rouge1 | 40.8656 |
| test_rouge2 | 20.3517 |
| test_rougeL | 31.2268 |
| test_rougeLsum | 37.9301 |

{"language": "en", "license": "apache-2.0", "tags": ["sagemaker", "bart", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Jeff: Can I train a \ud83e\udd17 Transformers model on Amazon SageMaker? \nPhilipp: 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": "bart-large-cnn-samsum", "results": [{"task": {"type": "abstractive-text-summarization", "name": "Abstractive Text Summarization"}, "dataset": {"name": "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization", "type": "samsum"}, "metrics": [{"type": "rogue-1", "value": 42.621, "name": "Validation ROGUE-1"}, {"type": "rogue-2", "value": 21.9825, "name": "Validation ROGUE-2"}, {"type": "rogue-l", "value": 33.034, "name": "Validation ROGUE-L"}, {"type": "rogue-1", "value": 41.3174, "name": "Test ROGUE-1"}, {"type": "rogue-2", "value": 20.8716, "name": "Test ROGUE-2"}, {"type": "rogue-l", "value": 32.1337, "name": "Test ROGUE-L"}]}, {"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "samsum", "type": "samsum", "config": "samsum", "split": "test"}, "metrics": [{"type": "rouge", "value": 40.8911, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 20.3551, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 31.2696, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 37.9313, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 1.4995627403259277, "name": "loss", "verified": true}, {"type": "gen_len", "value": 60.2247, "name": "gen_len", "verified": true}]}]}]}
transformers pytorch bart text2text-generation sagemaker summarization en dataset:samsum license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

'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
* Example Notebooks
* Amazon SageMaker documentation for Hugging Face
* Python SDK SageMaker documentation for Hugging Face
* Deep Learning Container


Hyperparameters
---------------



```
{
"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,
"sagemaker_container_log_level": 20,
"sagemaker_job_name": "huggingface-pytorch-training-2021-09-08-06-40-19-182",
"sagemaker_program": "run_summarization.py",
"sagemaker_region": "us-west-2",
"sagemaker_submit_directory": "s3://sagemaker-us-west-2-847380964353/huggingface-pytorch-training-2021-09-08-06-40-19-182/source/URL",
"seed": 7

```

}


Usage
-----



```
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. URL                                           
'''
nlp(conversation)

```

Results
-------




TAGS
#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
