## `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 |

{"language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "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\n"}], "model-index": [{"name": "bart-base-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": "rouge-1", "value": 46.6619, "name": "Validation ROUGE-1"}, {"type": "rouge-2", "value": 23.3285, "name": "Validation ROUGE-2"}, {"type": "rouge-l", "value": 39.4811, "name": "Validation ROUGE-L"}, {"type": "rouge-1", "value": 44.9932, "name": "Test ROUGE-1"}, {"type": "rouge-2", "value": 21.7286, "name": "Test ROUGE-2"}, {"type": "rouge-l", "value": 38.1921, "name": "Test ROUGE-L"}]}, {"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "samsum", "type": "samsum", "config": "samsum", "split": "test"}, "metrics": [{"type": "rouge", "value": 45.0148, "name": "ROUGE-1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWNlYWIyNzI4MDg5YTcxNzE2NDg3MTBkZGMzMGFmNjVhNDhiMjdiM2YxODdiMDRhZWYyYTdlY2ZkOTZlMThkNyIsInZlcnNpb24iOjF9.hUpQMm2qHUkBPstp7nldJFNy-9B75Z6zunEQCstfGSxIUYXdIlI9u-o0Y9DHIBr4ZLx_CvBtvR2e0shcFFbUBg"}, {"type": "rouge", "value": 21.6861, "name": "ROUGE-2", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTAwNjdmM2MwMTcxYjNjMTA4ODk4ZDRhODQ1M2UwN2U2ZjM0MDAyZTJhMTRmMTg0ZThiYThiYTJiN2FiYTk1ZiIsInZlcnNpb24iOjF9._QzKtHvIc_oi1VO-Maxofu-LKINnu9NuAwHmLKka_KwEwrTUZkL74zLa-r4ojKNWpRLRicu02L8W_AQafYoZCw"}, {"type": "rouge", "value": 38.1728, "name": "ROUGE-L", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGU0OTEzZTFhMGExOTkzYTI3NzljYjg2YzAxNDM4YzBhM2NjNjI4NWMxYjUwYmFjYzc5YTcxMGVmMTI3YThmMiIsInZlcnNpb24iOjF9.2JgzUAzdOOxUlt8HOWYa8mQuqyRBdyn-LqPiZI-h72zT8mrEO3sIEmmBOvmW40Gf5rvlErYtq87BgxzNwwYUAA"}, {"type": "rouge", "value": 41.2794, "name": "ROUGE-LSUM", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjI3ODg4YWQ5MjgwZmZkYTMzMGRjMGI2OWU2MDQ0ZDI3MThkZmYzN2U0OGMwMWJlMjhlMTc5YzgwMDBiM2JiZSIsInZlcnNpb24iOjF9.EnYKG7MuM-lNLkKOrlsb6mB94HqOg9sDBG1mCOni8hi7kM0rveSgSDVLk5Z6Adp-cfdRlho8zK-15TJTHJRxAw"}, {"type": "loss", "value": 1.597476601600647, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTBmYjJmZDhiYmJiMTcxODM5M2ZmMTBkZTcwYzM2NDFiMDJjNjJhOGMyNGQ3MGI1Y2UxZTBhNTBiMjFjZGZiNyIsInZlcnNpb24iOjF9.UdOhxHcBJGRM-kz46st_vVQR_-KWr9EtsaQnLvj7YjCzE6JqHA2LPXnDogpUQX96PISJj32XoK7jlj-2z-CGBQ"}, {"type": "gen_len", "value": 17.6606, "name": "gen_len", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWNlM2IyY2EzZGNiOWE0ZGMxZmJmZjhmMDI2YzE1YTQ3NmM3OGQ1NjY2ODllYjI5MDllODNhMjNmMWMyMDAyMiIsInZlcnNpb24iOjF9.sewPQx2WKY8IOBgr0XZkmzOzgwsvJko2iK0noBHpgbyWp41akxWHiaxmvipTOLcx7rbIroXQEr_UgE_LMv46Dw"}]}]}]}
transformers pytorch safetensors bart text2text-generation seq2seq summarization en dataset:samsum license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

'bart-base-samsum'
------------------


This model was obtained by fine-tuning 'facebook/bart-base' on Samsum dataset.


Usage
-----


Training procedure
------------------


* Colab notebook: URL


Results
-------




TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
