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model_cards/microsoft/prophetnet-large-uncased-cnndm/README.md
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@ -1,3 +1,9 @@
---
language: en
datasets:
- cnn_dailymail
---
## prophetnet-large-uncased-cnndm
Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on summarization task CNN/DailyMail.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
@ -15,8 +21,11 @@ inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=100, return_tensors='pt')
# Generate Summary
summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
tokenizer.batch_decode(summary_ids.tolist())
tokenizer.batch_decode(summary_ids, skip_special_tokens=True)
# should give: 'ustc was founded in beijing by the chinese academy of sciences in 1958. [X_SEP] ustc\'s mission was to develop a high - level science and technology workforce. [X_SEP] the establishment was hailed as " a major event in the history of chinese education and science "'
```
Here, [X_SEP] is used as a special token to seperate sentences.
### Citation
```bibtex