human-centered-summarization/financial-summarization-pegasus

Summarization • Updated Apr 26 • 9.64k • 125

Note This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies.

ProsusAI/finbert

Text Classification • Updated May 23, 2023 • 1.72M • 680

Note FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. The model will give softmax outputs for three labels: positive, negative or neutral.

nbroad/ESG-BERT

Text Classification • Updated Apr 26, 2023 • 40.2k • 60

Note Sustainable Investing as a domain has a unique vocabulary that ESG-BERT is capable of understanding. ESG-BERT was further trained on unstructured text data with accuracies of 100% and 98% for Next Sentence Prediction and Masked Language Modelling tasks. Fine-tuning ESG-BERT for text classification yielded an F-1 score of 0.90. For comparison, the general BERT (BERT-base) model scored 0.79 after fine-tuning, and the sci-kit learn approach scored 0.67.

oliverwang15/FinGPT_ChatGLM2_Sentiment_Instruction_LoRA_FT

Updated Jul 11, 2023 • 27

Note https://github.com/AI4Finance-Foundation/FinGPT

Note FiNER-139 is comprised of 1.1M sentences annotated with eXtensive Business Reporting Language (XBRL) tags extracted from annual and quarterly reports of publicly-traded companies in the US. Unlike other entity extraction tasks, like named entity recognition (NER) or contract element extraction, which typically require identifying entities of a small set of common types (e.g., persons, organizations), FiNER-139 uses a much larger label set of 139 entity types.

AdaptLLM/finance-LLM

Text Generation • Updated Oct 19 • 759 • 103

Note We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering.

Note Basic reading comprehension tasks to help adapt an LLM to finance tasks. Used to create AdaptLLM/finance-LLM

yiyanghkust/finbert-tone

Text Classification • Updated Oct 17, 2022 • 1.3M • 156

Note 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.

yiyanghkust/finbert-esg-9-categories

Text Classification • Updated Oct 17, 2022 • 11.2k • 35

Note 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).