julien-c
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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/google/roberta2roberta_L-24_discofuse/README.md

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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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+ - discofuse
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+ ---
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+
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+ # Roberta2Roberta_L-24_discofuse EncoderDecoder model
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+
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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_discofuse/1).
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+
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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 sentencefusion on the discofuse dataset, which is linked above.
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+
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+ Disclaimer: The model card has been written by the Hugging Face team.
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+
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+ ## How to use
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+
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+ You can use this model for sentence fusion, *e.g.*
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+
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+ IMPORTANT: The model was not trained on the `"` (double quotation mark) character -> so the before tokenizing the text, it is advised to replace all `"` (double quotation marks) with a single `` ` `` (single back tick).
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
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+ model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_discofuse")
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+
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+ discofuse = """As a run-blocker, Zeitler moves relatively well. Zeitler often struggles at the point of contact in space."""
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+
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+ input_ids = tokenizer(discofuse, 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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+ # As a run-blocker, Zeitler moves relatively well. However, Zeitler often struggles at the point of contact in space.
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+ ```