finbert-tone-finetuned-finance-topic-classification
Maintainer: nickmuchi
61
🎲
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🏅
finbert-tone
134
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.
Updated Invalid Date
👨🏫
distilroberta-finetuned-financial-news-sentiment-analysis
248
distilroberta-finetuned-financial-news-sentiment-analysis is a fine-tuned version of the DistilRoBERTa model, which is a distilled version of the RoBERTa-base model. It was fine-tuned by mrm8488 on the Financial PhraseBank dataset for sentiment analysis on financial news. The model achieves 98.23% accuracy on the evaluation set, with a loss of 0.1116. This model can be compared to similar financial sentiment analysis models like FinancialBERT, which was also fine-tuned on the Financial PhraseBank dataset. FinancialBERT achieved slightly lower performance, with a test set F1-score of 0.98. Model Inputs and Outputs Inputs Text data, such as financial news articles or reports Outputs Sentiment score: A number representing the sentiment of the input text, ranging from negative (-1) to positive (1) Confidence score: The model's confidence in the predicted sentiment score Capabilities The distilroberta-finetuned-financial-news-sentiment-analysis model is capable of accurately predicting the sentiment of financial text data. For example, it can analyze a news article about a company's earnings report and determine whether the tone is positive, negative, or neutral. This can be useful for tasks like monitoring market sentiment or analyzing financial news. What Can I Use It For? You can use this model for a variety of financial and business applications that require sentiment analysis of text data, such as: Monitoring news and social media for sentiment around a particular company, industry, or economic event Analyzing earnings reports, analyst notes, or other financial documents to gauge overall market sentiment Incorporating sentiment data into trading or investment strategies Improving customer service by analyzing sentiment in customer feedback or support tickets Things to Try One interesting thing to try with this model is to analyze how its sentiment predictions change over time for a particular company or industry. This could provide insights into how market sentiment is shifting and help identify potential risks or opportunities. You could also try fine-tuning the model further on a specific domain or task, such as analyzing sentiment in earnings call transcripts or SEC filings. This could potentially improve the model's performance on those specialized use cases.
Updated Invalid Date
🎲
bert-uncased-keyword-extractor
44
The bert-uncased-keyword-extractor is a fine-tuned version of the bert-base-uncased model, developed by the maintainer yanekyuk. This model achieves strong performance on the evaluation set, with a loss of 0.1247, precision of 0.8547, recall of 0.8825, accuracy of 0.9741, and an F1 score of 0.8684. Similar models include the finbert-tone-finetuned-finance-topic-classification model, which is a fine-tuned version of yiyanghkust/finbert-tone on the Twitter Financial News Topic dataset. It achieves an accuracy of 0.9106 and F1 score of 0.9106 on the evaluation set. Model inputs and outputs Inputs Text**: The bert-uncased-keyword-extractor model takes in text as its input. Outputs Keywords**: The model outputs a set of keywords extracted from the input text. Capabilities The bert-uncased-keyword-extractor model is capable of extracting relevant keywords from text. This can be useful for tasks like content summarization, topic modeling, and document classification. By identifying the most important words and phrases in a piece of text, this model can help surface the key ideas and themes. What can I use it for? The bert-uncased-keyword-extractor model could be used in a variety of applications that involve processing and understanding text data. For example, it could be integrated into a content management system to automatically generate tags and metadata for articles and blog posts. It could also be used in a search engine to improve the relevance of search results by surfacing the most important terms in a user's query. Things to try One interesting thing to try with the bert-uncased-keyword-extractor model is to experiment with different types of text data beyond the original training domain. For example, you could see how well it performs on extracting keywords from scientific papers, social media posts, or creative writing. By testing the model's capabilities on a diverse range of text, you may uncover new insights or limitations that could inform future model development.
Updated Invalid Date
🏷️
FinancialBERT-Sentiment-Analysis
56
FinancialBERT is a BERT model pre-trained on a large corpus of financial texts. The purpose is to enhance financial NLP research and practice in the financial domain, allowing financial practitioners and researchers to benefit from this model without the significant computational resources required to train it from scratch. The model was fine-tuned for Sentiment Analysis on the Financial PhraseBank dataset, and experiments show it outperforms general BERT and other financial domain-specific models. Similar models include CryptoBERT, which is pre-trained on cryptocurrency-related social media posts for sentiment analysis, and SiEBERT, a fine-tuned RoBERTa model for general English sentiment analysis. Model inputs and outputs Inputs Text**: The model takes in financial text, such as news articles or social media posts, as input. Outputs Sentiment classification**: The model outputs a sentiment classification, predicting whether the input text has a negative, neutral, or positive sentiment. Capabilities The FinancialBERT model is specifically tailored for the financial domain, allowing it to better capture the nuances and language used in financial texts compared to general language models. This makes it a powerful tool for tasks like sentiment analysis of earnings reports, market commentary, and other financial communications. What can I use it for? The FinancialBERT model can be used for a variety of financial NLP applications, such as: Sentiment analysis of financial news, reports, and social media posts to gauge market sentiment and investor sentiment. Monitoring and analyzing the tone and sentiment of financial communications to inform investment decisions or risk management. Automating the summarization and categorization of financial documents, like earnings reports or market updates. The model can be further fine-tuned on your own financial data to customize it for your specific use case. Things to try One interesting aspect of FinancialBERT is its potential to capture domain-specific language and nuances that may not be well-represented in general language models. You could experiment with using FinancialBERT in parallel with a general sentiment analysis model to see if it provides complementary insights or improved performance on financial-related texts. Another idea is to explore how FinancialBERT handles specialized financial terminology and jargon compared to more general models. You could test its performance on a variety of financial text types, from earnings reports to market commentary, to get a sense of its strengths and limitations.
Updated Invalid Date