Update README

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- text-generation
+- text2text-generation
+pipeline_tag: text2text-generation
+widget:
+- text: "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man"
+  example_title: "Example1"
+- text: "Describe the following data: First Clearing | LOCATION | On NYS 52 1 Mi. Youngsville [SEP] On NYS 52 1 Mi. Youngsville | CITY_OR_TOWN | Callicoon, New York"
+  example_title: "Example2"
+---
+
+# MVP-data-to-text
+The MVP-data-to-text model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://github.com/RUCAIBox/MVP/blob/main/paper.pdf) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
+
+The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP).
+
+## Model Description
+MVP-data-to-text is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled data-to-text datasets. It is a variant (MVP+S) of our main MVP model. It follows a Transformer encoder-decoder architecture with layer-wise prompts.
+
+MVP-data-to-text is specially designed for data-to-text generation tasks, such as KG-to-text generation (WebNLG, DART), table-to-text generation (WikiBio, ToTTo) and MR-to-text generation (E2E).
+
+## Example
+```python
+>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
+
+>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
+>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-data-to-text")
+
+>>> inputs = tokenizer(
+...     "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man",
+...     return_tensors="pt",
+... )
+>>> generated_ids = model.generate(**inputs)
+>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+['Iron Man is a fictional superhero appearing in American comic books published by Marvel Comics.']
+```
+
+## Citation