Update README.md

README.md

@@ -39,6 +39,7 @@ This model is a fine-tuned KBIR model on the Inspec dataset. KBIR or Keyphrase B
 You can find more information about the architecture in this paper: https://arxiv.org/abs/2112.08547.
 
 The model is fine-tuned as a token classification problem where the text is labeled using the BIO scheme.
+
 | Label | Description                     |
 | ----- | ------------------------------- |
 | B     | At the beginning of a keyphrase |
@@ -46,13 +47,14 @@ The model is fine-tuned as a token classification problem where the text is labe
 | O     | Outside a keyphrase             |
 
 Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021).
+
 Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma, Rakesh Gosangi, Amanda Stent, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann. "Keyphrase extraction as sequence labeling using contextualized embeddings." In European Conference on Information Retrieval, pp. 328-335. Springer, Cham, 2020.
 
 ## ✋ Intended uses & limitations
 ### 🛑 Limitations
-* This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers.
+* This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.
 * Only works for English documents.
-* For a custom model, please consult the training notebook (link incoming).
+* For a custom model, please consult the training notebook for more information (link incoming).
 
 ### ❓ How to use
 ```python