Update README.md

README.md

@@ -12,6 +12,7 @@ ESG analysis can help investors determine a business' long-term sustainability a
 
 **finbert-esg-9-categories** classifies a text into nine fine-grained ESG topics: *Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, and Non-ESG*. This model complements [**finbert-esg**](https://huggingface.co/yiyanghkust/finbert-esg) which classifies a text into four coarse-grained ESG themes (*E, S, G or None*). 
 
+Detailed description of the nine fine-grained ESG topic definition, some examples for each topic, training sample, and the model’s performance can be found [**here**](https://www.allenhuang.org/uploads/2/6/5/5/26555246/esg_9-class_descriptions.pdf).
 
 **Input**: A text.
 
@@ -30,4 +31,9 @@ nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer)
 results = nlp('For 2002, our total net emissions were approximately 60 million metric tons of CO2 equivalents for all businesses 
                and operations we have financial interests in, based on its equity share in those businesses and operations.')
 print(results) # [{'label': 'Climate Change', 'score': 0.9955655932426453}]
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+```
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+If you use the model in your academic work, please cite the following paper: 
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+Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).