- BART is a model for document summarization
- Derived from the same transformer as BERT
- Unlike BERT, it has an encoder-decoder structure
- This is because it is intended for sentence generation
This page shows the steps to run a tutorial on BART.
Procedure
- install transformers
Run ``sh pip install transformers
Run summary
2. Run the summary
from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
# Generate Summary
summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
```
On 2021/01/18, the output was MyMy friends.
Interesting.
## Where I got stuck.
Error when the version of pytorch is different from the one specified in transformers.
pip install -U torch
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