Create README.md

model_cards/google/roberta2roberta_L-24_discofuse/README.md

+---
+language: en
+license: apache-2.0
+datasets:
+- discofuse
+---
+
+# Roberta2Roberta_L-24_discofuse EncoderDecoder model
+
+The model was introduced in 
+[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). 
+
+The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder 
+and decoder and fine-tuned on sentencefusion on the discofuse dataset, which is linked above.
+
+Disclaimer: The model card has been written by the Hugging Face team.
+
+## How to use
+
+You can use this model for sentence fusion, *e.g.*
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_discofuse")
+
+discofuse = """As a run-blocker, Zeitler moves relatively well. Zeitler often struggles at the point of contact in space."""
+
+input_ids = tokenizer(discofuse, return_tensors="pt").input_ids
+output_ids = model.generate(input_ids)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# As a run-blocker, Zeitler moves relatively well. However, Zeitler often struggles at the point of contact in space.  
+```