Update README.md

README.md

@@ -6,4 +6,25 @@ tags:
 - environmental-social-corporate-governance
 widget:
 - text: "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. "
----
+---
+
+ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. **finbERT-esg-9-categories** is a FinBERT model fine-tuned on about 14,000 manually annotated sentences from firms' ESG reports and annual reports. 
+
+**finbert-esg-9-categories** classifies a financial text into 9 fine-grained ESG classes: *Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, and Non-ESG*. It complements [**finbert-esg**](https://huggingface.co/yiyanghkust/finbert-esg) which classifies a text into 4 coarse-grained ESG categories (*E, S, G or None*). 
+
+
+**Input**: A financial text.
+
+**Output**: Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, or Non-ESG.
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification, pipeline
+
+finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-esg-9-categories',num_labels=9)
+tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-esg-9-categories')
+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}]
+```