​
# Summarization
​
## Model description
​
BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset
​
## How to use
​
PyTorch model available
​
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/summarizer-cnndm") 
AutoModelWithLMHead.from_pretrained("yuvraj/summarizer-cnndm")
​
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)
summarizer("<Text to be summarized>")
​
## Limitations and bias
Trained on a small dataset

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

​
# Summarization
​
## Model description
​
BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset
​
## How to use
​
PyTorch model available
​
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/summarizer-cnndm") 
AutoModelWithLMHead.from_pretrained("yuvraj/summarizer-cnndm")
​
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)
summarizer("<Text to be summarized>")
​
## Limitations and bias
Trained on a small dataset

# Summarization
​ ## Model description
​
BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset
​ ## How to use
​
PyTorch model available
​
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/summarizer-cnndm") 
AutoModelWithLMHead.from_pretrained("yuvraj/summarizer-cnndm")
​
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)
summarizer("<Text to be summarized>")
​ ## Limitations and bias
Trained on a small dataset
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #autotrain_compatible #endpoints_compatible #region-us 
 # Summarization
​ ## Model description
​
BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset
​ ## How to use
​
PyTorch model available
​
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/summarizer-cnndm") 
AutoModelWithLMHead.from_pretrained("yuvraj/summarizer-cnndm")
​
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)
summarizer("<Text to be summarized>")
​ ## Limitations and bias
Trained on a small dataset
passage: TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #autotrain_compatible #endpoints_compatible #region-us 
# Summarization
​## Model description
​
BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset
​## How to use
​
PyTorch model available
​
'''python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
​
tokenizer = AutoTokenizer.from_pretrained("yuvraj/summarizer-cnndm") 
AutoModelWithLMHead.from_pretrained("yuvraj/summarizer-cnndm")
​
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)
summarizer("<Text to be summarized>")
​## Limitations and bias
Trained on a small dataset