​
## Model description
​
BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article
​
## How to use
​
PyTorch model available
​
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm")			
model = AutoModelWithLMHead.from_pretrained("yuvraj/xSumm")
​
xsumm = pipeline('summarization', model=model, tokenizer=tokenizer)
xsumm("<text to be summarized>")
​
## Limitations and bias
Trained on a small fraction of the xsumm training dataset

{"language": "en", "tags": ["summarization", "extreme summarization"]}
transformers pytorch bart text2text-generation summarization extreme summarization en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #extreme summarization #en #autotrain_compatible #endpoints_compatible #region-us 

​
## Model description
​
BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article
​
## How to use
​
PyTorch model available
​
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm")			
model = AutoModelWithLMHead.from_pretrained("yuvraj/xSumm")
​
xsumm = pipeline('summarization', model=model, tokenizer=tokenizer)
xsumm("<text to be summarized>")
​
## Limitations and bias
Trained on a small fraction of the xsumm training dataset

## Model description
​
BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article
​ ## How to use
​
PyTorch model available
​
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm")			
model = AutoModelWithLMHead.from_pretrained("yuvraj/xSumm")
​
xsumm = pipeline('summarization', model=model, tokenizer=tokenizer)
xsumm("<text to be summarized>")
​ ## Limitations and bias
Trained on a small fraction of the xsumm training dataset
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #extreme summarization #en #autotrain_compatible #endpoints_compatible #region-us 
 ## Model description
​
BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article
​ ## How to use
​
PyTorch model available
​
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm")			
model = AutoModelWithLMHead.from_pretrained("yuvraj/xSumm")
​
xsumm = pipeline('summarization', model=model, tokenizer=tokenizer)
xsumm("<text to be summarized>")
​ ## Limitations and bias
Trained on a small fraction of the xsumm training dataset
passage: TAGS
#transformers #pytorch #bart #text2text-generation #summarization #extreme summarization #en #autotrain_compatible #endpoints_compatible #region-us 
## Model description
​
BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article
​## How to use
​
PyTorch model available
​
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm")			
model = AutoModelWithLMHead.from_pretrained("yuvraj/xSumm")
​
xsumm = pipeline('summarization', model=model, tokenizer=tokenizer)
xsumm("<text to be summarized>")
​## Limitations and bias
Trained on a small fraction of the xsumm training dataset