Yiyanghkust

finbert-tone

yiyanghkust

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FinBERT is a BERT model pre-trained on a large corpus of financial communication text, including corporate reports, earnings call transcripts, and analyst reports. This model aims to enhance financial NLP research and practice. The released finbert-tone model is the FinBERT model fine-tuned on manually annotated sentences from analyst reports, achieving superior performance on the financial tone analysis task. Similar models include the FinancialBERT model, which is a BERT model pre-trained on financial texts and fine-tuned for sentiment analysis, and the DistilRoberta-finetuned-financial-news-sentiment-analysis model, a DistilRoBERTa model fine-tuned on financial news sentiment analysis. Model inputs and outputs Inputs Text data related to the financial domain, such as corporate reports, earnings call transcripts, and analyst reports. Outputs Sentiment classification labels (positive, negative, neutral) for the input text. Capabilities The finbert-tone model is capable of accurately analyzing the sentiment or tone of financial text, such as determining whether a statement about a company's financial situation is positive, negative, or neutral. What can I use it for? You can use the finbert-tone model for a variety of financial NLP tasks, such as sentiment analysis of earnings call transcripts, financial news articles, or analyst reports. This could be useful for monitoring market sentiment, identifying risks or opportunities, or automating financial research and reporting. Things to try One interesting aspect of the finbert-tone model is that it was fine-tuned on a specific corpus of financial text, which may make it more accurate for financial sentiment analysis compared to more general language models. You could experiment with using the finbert-tone model on different types of financial text to see how it performs compared to other models.

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