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@@ -12,6 +12,7 @@ ESG analysis can help investors determine a business' long-term sustainability a
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  **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*).
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  **Input**: A text.
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@@ -30,4 +31,9 @@ nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer)
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  results = nlp('For 2002, our total net emissions were approximately 60 million metric tons of CO2 equivalents for all businesses
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  and operations we have financial interests in, based on its equity share in those businesses and operations.')
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  print(results) # [{'label': 'Climate Change', 'score': 0.9955655932426453}]
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- ```
 
 
 
 
 
 
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  **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*).
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+ 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).
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  **Input**: A text.
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  results = nlp('For 2002, our total net emissions were approximately 60 million metric tons of CO2 equivalents for all businesses
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  and operations we have financial interests in, based on its equity share in those businesses and operations.')
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  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).