| | --- |
| | language: "en" |
| | tags: |
| | - financial-text-analysis |
| | - esg |
| | - environmental-social-corporate-governance |
| | widget: |
| | - text: "Rhonda has been volunteering for several years for a variety of charitable community programs. " |
| | --- |
| | |
| | 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. |
| |
|
| | **Input**: A financial text. |
| |
|
| | **Output**: Environmental, Social, Governance or None. |
| |
|
| | # How to use |
| | You can use this model with Transformers pipeline for ESG classification. |
| | ```python |
| | # tested in transformers==4.18.0 |
| | from transformers import BertTokenizer, BertForSequenceClassification, pipeline |
| | |
| | finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-esg',num_labels=4) |
| | tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-esg') |
| | nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer) |
| | results = nlp('Rhonda has been volunteering for several years for a variety of charitable community programs.') |
| | print(results) # [{'label': 'Social', 'score': 0.9906041026115417}] |
| | |
| | ``` |
| |
|
| | Visit [FinBERT.AI](https://finbert.ai/) for more details on the recent development of FinBERT. |
| |
|
| | If you use the model in your academic work, please cite the following paper: |
| |
|
| | 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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