Update README.md

README.md

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 # Model Card for mdeberta-v3-base-triplet-critic-xnli
 
 <!-- Provide a quick summary of what the model is/does. [Optional] -->
-This is the Triplit Critic model presented in the ACL 2023 paper RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset. 
+This is the Triplit Critic model presented in the ACL 2023 paper [RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset](https://arxiv.org/abs/2306.09802). If you use the model, please reference this work in your paper:
+
+    @inproceedings{huguet-cabot-et-al-2023-redfm-dataset,
+        title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset",
+        author = "Huguet Cabot, Pere-Llu{\'\i}s  and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and
+          Navigli, Roberto",
+        booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023",
+        month = jul,
+        year = "2023",
+        address = "Toronto, Canada",
+        publisher = "Association for Computational Linguistics",
+        url = "https://arxiv.org/abs/2306.09802",
+    }
+
+The Triplit Critic is based on mdeberta-v3-base and it was trained as a multitask system to filter triplets as well as on the XNLI dataset. The model weights contain the two classification heads, however loading it using the huggingface library will only load those for Triplet filtering (ie. a binary classification head), if one wants to use it for XNLI it needs a custom script. While it is defined and trained as a classification system, we use the positive score (ie. Label_1) as the confidence score for a triplet. For SRED<sup>FM</sup> the confidence score thresshold was set at 0.75.
 
-It is based on mdeberta-v3-base and it was trained as a multitask system to filter triplets as well as on the XNLI dataset. The model weights contain the two classification heads, however loading it using the huggingface library will only load those for Triplet filtering (ie. a binary classification head), if one wants to use it for XNLI it needs a custom script. While it is defined and trained as a classification system, we use the positive score (ie. Label_1) as the confidence score for a triplet. For SRED<sup>FM</sup> the confidence score thresshold was set at 0.75.