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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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# Roberta2Roberta_L-24_discofuse 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_discofuse/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 sentencefusion on the discofuse 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 sentence fusion, *e.g.* |
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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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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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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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discofuse = """As a run-blocker, Zeitler moves relatively well. Zeitler often struggles at the point of contact in space.""" |
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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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``` |
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