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--- |
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language: "en" |
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tags: |
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- financial-text-analysis |
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- esg |
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- environmental-social-corporate-governance |
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widget: |
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- text: "Rhonda has been volunteering for several years for a variety of charitable community programs. " |
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--- |
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ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. FinBERT-ESG is a FinBERT model fine-tuned on 2,000 manually annotated sentences from firms' ESG reports and annual reports. |
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**Input**: A financial text. |
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**Output**: Environmental, Social, Governance or None. |
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# How to use |
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You can use this model with Transformers pipeline for ESG classification. |
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```python |
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# tested in transformers==4.18.0 |
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from transformers import BertTokenizer, BertForSequenceClassification, pipeline |
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finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-esg',num_labels=4) |
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tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-esg') |
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nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer) |
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results = nlp('Rhonda has been volunteering for several years for a variety of charitable community programs.') |
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print(results) # [{'label': 'Social', 'score': 0.9906041026115417}] |
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``` |
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Visit [FinBERT.AI](https://finbert.ai/) for more details on the recent development of FinBERT. |
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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). |
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