Nickmuchi

finbert-tone-finetuned-finance-topic-classification

nickmuchi

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The finbert-tone-finetuned-finance-topic-classification model is a fine-tuned version of the FinBERT model, which is a BERT-based language model pre-trained on a large corpus of financial text. This fine-tuned model is specifically designed for classifying the financial topics of tweets, achieving high accuracy on a dataset of 11,267 tweets across 20 different financial topics. Similar models in this domain include FinancialBERT-Sentiment-Analysis, which fine-tunes the FinBERT model for sentiment analysis on financial text, and distilroberta-finetuned-financial-news-sentiment-analysis, which fine-tunes a DistilRoBERTa model for the same task. Model inputs and outputs Inputs Tweets**: The model takes in financial-related tweets as input and classifies them into one of 20 different topics. Outputs Financial Topic**: The model outputs a predicted financial topic label for the input tweet, such as 'business & entrepreneurs', 'news & social concern', or 'sports'. Capabilities The finbert-tone-finetuned-finance-topic-classification model can accurately categorize financial tweets into relevant topics, with an overall F1-score of 0.91 on the evaluation dataset. This makes it a powerful tool for analyzing social media conversations and news around financial topics, allowing users to quickly identify the main themes being discussed. What can I use it for? This model could be useful for a variety of applications, such as: Monitoring social media**: Tracking conversations on Twitter and other platforms to understand which financial topics are generating the most discussion and engagement. Analyzing news coverage**: Classifying financial news articles and reports to gain insights into the dominant themes and trends in the industry. Powering financial applications**: Integrating the model into financial products and services to provide users with topic-based organization and analysis of financial information. Things to try One interesting aspect of this model is its ability to handle the unbalanced distribution of labels in the training data. By adjusting the weights to focus more on the less-sampled labels, the model is able to achieve strong performance across a wide range of financial topics. This suggests that the model could be particularly useful in real-world scenarios where financial conversations are often skewed towards certain high-profile topics. Developers could experiment with fine-tuning the model further on their own domain-specific datasets to see if they can improve the performance even more. Additionally, combining this model with other financial NLP tools, such as sentiment analysis, could provide a more holistic understanding of financial discussions online.

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