## Usage
```python
from transformers import pipeline

summarizer_pipe = pipeline("summarization", model="yashugupta786/bart_large_xsum_samsum_conv_summarizer")
conversation_data = '''Hannah: Hey, do you have Betty's number?
Amanda: Lemme check
Amanda: Sorry, can't find it.
Amanda: Ask Larry
Amanda: He called her last time we were at the park together
Hannah: I don't know him well
Amanda: Don't be shy, he's very nice
Hannah: If you say so..
Hannah: I'd rather you texted him
Amanda: Just text him 🙂
Hannah: Urgh.. Alright
Hannah: Bye
Amanda: Bye bye                                       
'''
summarizer_pipe(conversation_data)
```

## Results
| key | value |
| --- | ----- |
| eval_rouge1 | 54.3921 |
| eval_rouge2 | 29.8078 |
| eval_rougeL | 45.1543 |
| eval_rougeLsum | 49.942 |
| test_rouge1 | 53.3059 |
| test_rouge2 | 28.355 |
| test_rougeL | 44.0953 |
| test_rougeLsum | 48.9246 |

All the metric Rouge1,2,L are computed using precison  and recall then computed the F measure for these 
Rouge recall= no of overlaping words/total no of referenced humman annotated words
Rouge precision= no of overlaping words/total no of candidate machine predicted words
{"language": ["en"], "library_name": "transformers", "tags": ["summarization", "conversational", "seq2seq", "bart large"], "datasets": ["samsum"], "metrics": ["rouge"], "pipeline_tag": "summarization", "widget": [{"text": "Hannah: Hey, do you have Betty's number?\nAmanda: Lemme check\nAmanda: Sorry, can't find it.\nAmanda: Ask Larry\nAmanda: He called her last time we were at the park together\nHannah: I don't know him well\nAmanda: Don't be shy, he's very nice\nHannah: If you say so..\nHannah: I'd rather you texted him\nAmanda: Just text him \ud83d\ude42\nHannah: Urgh.. Alright\nHannah: Bye\nAmanda: Bye bye\n"}], "model-index": [{"name": "bart-large-xsum-samsum-conversational_summarizer", "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": 54.3921, "name": "Validation ROUGE-1"}, {"type": "rouge-2", "value": 29.8078, "name": "Validation ROUGE-2"}, {"type": "rouge-l", "value": 45.1543, "name": "Validation ROUGE-L"}, {"type": "rouge-1", "value": 53.3059, "name": "Test ROUGE-1"}, {"type": "rouge-2", "value": 28.355, "name": "Test ROUGE-2"}, {"type": "rouge-l", "value": 44.0953, "name": "Test ROUGE-L"}]}]}]}
transformers pytorch bart text2text-generation summarization conversational seq2seq bart large en dataset:samsum model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #conversational #seq2seq #bart large #en #dataset-samsum #model-index #autotrain_compatible #endpoints_compatible #region-us 

Usage
-----


Results
-------



All the metric Rouge1,2,L are computed using precison and recall then computed the F measure for these
Rouge recall= no of overlaping words/total no of referenced humman annotated words
Rouge precision= no of overlaping words/total no of candidate machine predicted words



TAGS
#transformers #pytorch #bart #text2text-generation #summarization #conversational #seq2seq #bart large #en #dataset-samsum #model-index #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bart #text2text-generation #summarization #conversational #seq2seq #bart large #en #dataset-samsum #model-index #autotrain_compatible #endpoints_compatible #region-us 
