Update README.md

README.md

@@ -15,7 +15,7 @@ license: cc-by-nc-sa-4.0
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rebel-relation-extraction-by-end-to-end/relation-extraction-on-re-tacred)](https://paperswithcode.com/sota/relation-extraction-on-re-tacred?p=rebel-relation-extraction-by-end-to-end)
 # REBEL: Relation Extraction By End-to-end Language generation
 
-This is the model card for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By End-to-end Language generation. We present a new linearization aproach and a reframing of Relation Extraction as a seq2seq task. The paper can be found [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). If you use the code, please reference this work in your paper:
+This is the model card for the Findings of EMNLP 2021 paper [REBEL: Relation Extraction By End-to-end Language generation](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). We present a new linearization aproach and a reframing of Relation Extraction as a seq2seq task. The paper can be found [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). If you use the code, please reference this work in your paper:
 
     @inproceedings{huguet-cabot-navigli-2021-rebel,
     title = "REBEL: Relation Extraction By End-to-end Language generation",
@@ -31,6 +31,8 @@ This is the model card for the Findings of EMNLP 2021 paper REBEL: Relation Extr
 
 The original repository for the paper can be found [here](https://github.com/Babelscape/rebel)
 
+Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types.
+
 ## Pipeline usage
 
 ```python