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finbert-esg

yiyanghkust

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. Visit FinBERT.AI for more details on the recent development of FinBERT. Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).

Text analysis

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finbert-esg-9-categories

yiyanghkust

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 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** 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**.

Text analysis

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finbert-fls

yiyanghkust

Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Management Discussion and Analysis section of annual reports of Russell 3000 firms.

Text analysis

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finbert-pretrain

yiyanghkust

`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. - Corporate Reports 10-K & 10-Q: 2.5B tokens - Earnings Call Transcripts: 1.3B tokens - Analyst Reports: 1.1B tokens Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).

Fill-mask

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finbert-tone

yiyanghkust

`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. - Corporate Reports 10-K & 10-Q: 2.5B tokens - Earnings Call Transcripts: 1.3B tokens - Analyst Reports: 1.1B tokens More technical details on `FinBERT`: Click Link This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.

Text generationOpen weights

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finbert-tone-chinese

yiyanghkust

This is a fine-tuned version of FinBERT, based on bert-base-chinese, on a private dataset (around ~8k analyst report sentences) for sentiment analysis. * Test Accuracy = 0.88 * Test Macro F1 = 0.87 * **Labels**: 0 -> Neutral; 1 -> Positive; 2 -> Negative

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Explore AI models 6

finbert-esg

finbert-esg-9-categories

finbert-fls

finbert-pretrain

finbert-tone

finbert-tone-chinese

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