Yiyanghkust
Rank:Average Model Cost: $0.0000
Number of Runs: 1,279,906
Models by this creator
finbert-tone
finbert-tone
FinBERT-Tone is a text classification model that is specifically designed for financial sentiment analysis. It is based on the FinBERT model, which has been pre-trained on a large corpus of financial news articles and fine-tuned using supervised learning on labeled financial sentiment data. FinBERT-Tone can determine the sentiment of financial text, classifying it as positive, negative, or neutral. This model is useful for understanding and analyzing the sentiment of financial news, social media posts, and other financial documents.
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1.2M
Huggingface
finbert-esg
finbert-esg
finbert-esg is a text classification model that predicts the environmental, social, and governance (ESG) rating of a company based on its financial filings and reports. The model uses a deep neural network trained on a large dataset of company reports to classify the ESG rating of a given text. It can be used by analysts and investors to quickly assess the sustainability and ethical practices of a company.
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108.1K
Huggingface
finbert-fls
finbert-fls
FinBERT-FLS is a text classification model designed for financial news sentiment analysis. It is trained on a large dataset of financial news articles and can predict sentiment labels for text inputs, indicating whether the sentiment expressed in the text is positive or negative. This model can be used to automate the process of sentiment analysis for financial news, which can be helpful for traders, investors, and financial analysts in making informed decisions.
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8.3K
Huggingface
finbert-pretrain
finbert-pretrain
FinBERT is a masked language model trained on the finance domain. It has been pre-trained on a large corpus of financial text data and can be fine-tuned for specific downstream tasks in the finance domain, such as sentiment analysis or financial document classification. The model is specifically designed to understand financial concepts and terminology, and can be used to generate predictions and insights for a range of financial applications.
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6.3K
Huggingface
finbert-esg-9-categories
finbert-esg-9-categories
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. Input: A text. Output: Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, or Non-ESG. How to use You can use this model with Transformers pipeline for fine-grained ESG 9 categories classification. 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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3.1K
Huggingface