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--- |
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language: en |
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license: apache-2.0 |
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datasets: |
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- gigaword |
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tags: |
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- summarization |
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--- |
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# Roberta2Roberta_L-24_gigaword EncoderDecoder model |
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The model was introduced in |
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[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_gigaword/1). |
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The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder |
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and decoder and fine-tuned on headline generation using the Gigaword dataset, which is linked above. |
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Disclaimer: The model card has been written by the Hugging Face team. |
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## How to use |
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You can use this model for extreme summarization, *e.g.* |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_gigaword") |
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model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_gigaword") |
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article = """australian shares closed down #.# percent monday |
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following a weak lead from the united states and |
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lower commodity prices , dealers said .""" |
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input_ids = tokenizer(article, return_tensors="pt").input_ids |
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output_ids = model.generate(input_ids)[0] |
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print(tokenizer.decode(output_ids, skip_special_tokens=True)) |
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# should output |
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# australian shares close down #.# percent. |
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``` |
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