finbert
Maintainer: ProsusAI
539
👁️
| 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
FinBERT is a pre-trained natural language processing (NLP) model developed by Prosus AI to analyze the sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and fine-tuning it for financial sentiment classification. The model was trained on the Financial PhraseBank dataset by Malo et al. (2014).
Similar models like FinancialBERT-Sentiment-Analysis and CryptoBERT have also been developed for financial and cryptocurrency-related text analysis, respectively. These models leverage domain-specific data to enhance performance for their respective financial applications.
Model inputs and outputs
Inputs
- Financial text, such as news articles, reports, and social media posts
Outputs
- Softmax outputs for three sentiment labels: positive, negative, or neutral
Capabilities
The FinBERT model is capable of accurately classifying the sentiment of financial text, including identifying positive, negative, and neutral sentiments. This can be useful for tasks such as:
- Analyzing investor sentiment towards a company or industry
- Monitoring public perception of financial news and events
- Automating the process of sentiment analysis in financial applications
What can I use it for?
FinBERT can be used in a variety of financial applications, such as:
- Sentiment analysis of financial news and reports to gauge market sentiment
- Monitoring social media posts and discussions related to financial topics
- Incorporating sentiment analysis into investment decision-making processes
- Automating the analysis of customer feedback and reviews for financial products and services
Things to try
Some interesting things to try with FinBERT include:
- Evaluating the model's performance on your own financial text data and fine-tuning it for your specific use case
- Exploring how the model's sentiment predictions align with market movements or financial outcomes
- Combining
FinBERT's sentiment analysis with other financial data sources to create more comprehensive investment strategies - Investigating how the model's performance compares to human-labeled sentiment analysis in the financial domain
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
🏷️
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
🏅
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
🗣️
cryptobert
86
CryptoBERT is a pre-trained natural language processing (NLP) model designed to analyze the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai/bertweet-base language model on a corpus of over 3.2M unique cryptocurrency-related social media posts. This model can be useful for monitoring market sentiment and identifying potential trends or investment opportunities in the cryptocurrency space. Similar models include twitter-XLM-roBERTa-base-sentiment for general sentiment analysis on Twitter data, and BTLM-3B-8k-base for large-scale language modeling. However, CryptoBERT is specifically tailored for the cryptocurrency domain, making it potentially more accurate for tasks like cryptocurrency sentiment analysis. Model inputs and outputs Inputs Text**: The model takes in text, such as social media posts or messages, related to cryptocurrencies. Outputs Sentiment classification**: The model outputs a sentiment classification of the input text, with labels "Bearish", "Neutral", or "Bullish". Classification scores**: Along with the sentiment label, the model also outputs the probability scores for each sentiment class. Capabilities CryptoBERT can be used to analyze the sentiment of cryptocurrency-related text, which can be useful for monitoring market trends, identifying potential investment opportunities, or understanding public perception of specific cryptocurrencies. The model was trained on a large corpus of cryptocurrency-related social media posts, giving it a strong understanding of the language and sentiment in this domain. What can I use it for? You can use CryptoBERT for a variety of applications related to cryptocurrency market analysis and sentiment tracking. For example, you could use it to: Monitor social media sentiment around specific cryptocurrencies or the broader cryptocurrency market. Identify potential investment opportunities by detecting shifts in market sentiment. Analyze the sentiment of news articles, blog posts, or other cryptocurrency-related content. Incorporate sentiment data into trading strategies or investment decision-making processes. The model's maintainer has also provided a classification example, which you can use as a starting point for integrating the model into your own applications. Things to try One interesting thing to try with CryptoBERT is to compare its sentiment predictions with actual cryptocurrency market movements. You could track the model's sentiment output over time and see how well it correlates with changes in cryptocurrency prices or trading volume. This could help you understand the model's strengths and limitations in predicting market sentiment and identify potential areas for improvement. Another idea is to experiment with fine-tuning the model on additional cryptocurrency-related data, such as company announcements, developer forums, or industry reports. This could further enhance the model's understanding of the language and nuances of the cryptocurrency space, potentially improving its sentiment analysis capabilities.
Updated Invalid Date
🏷️
bert-base-multilingual-uncased-sentiment
258
The bert-base-multilingual-uncased-sentiment model is a BERT-based model that has been fine-tuned for sentiment analysis on product reviews across six languages: English, Dutch, German, French, Spanish, and Italian. This model can predict the sentiment of a review as a number of stars (between 1 and 5). It was developed by NLP Town, a provider of custom language models for various tasks and languages. Similar models include the twitter-XLM-roBERTa-base-sentiment model, which is a multilingual XLM-roBERTa model fine-tuned for sentiment analysis on tweets, and the sentiment-roberta-large-english model, which is a fine-tuned RoBERTa-large model for sentiment analysis in English. Model inputs and outputs Inputs Text**: The model takes product review text as input, which can be in any of the six supported languages (English, Dutch, German, French, Spanish, Italian). Outputs Sentiment score**: The model outputs a sentiment score, which is an integer between 1 and 5 representing the number of stars the model predicts for the input review. Capabilities The bert-base-multilingual-uncased-sentiment model is capable of accurately predicting the sentiment of product reviews across multiple languages. For example, it can correctly identify a positive review like "This product is amazing!" as a 5-star review, or a negative review like "This product is terrible" as a 1-star review. What can I use it for? You can use this model for sentiment analysis on product reviews in any of the six supported languages. This could be useful for e-commerce companies, review platforms, or anyone interested in analyzing customer sentiment. The model could be used to automatically aggregate and analyze reviews, detect trends, or surface particularly positive or negative feedback. Things to try One interesting thing to try with this model is to experiment with reviews that contain a mix of languages. Since the model is multilingual, it may be able to correctly identify the sentiment even when the review contains words or phrases in multiple languages. You could also try fine-tuning the model further on a specific domain or language to see if you can improve the accuracy for your particular use case.
Updated Invalid Date