FinBERT

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finBERT is an open-source NLP model for financial sentiment analysis. It is built by fine-tuning the BERT language model on a large financial corpus.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is finBERT?

finBERT is a specialized Natural Language Processing (NLP) model designed for financial sentiment analysis. It enhances the standard BERT language model by further training it on a large financial corpus, making it highly effective for understanding the sentiment expressed in financial texts. This tool is particularly useful for analyzing documents like earnings call transcripts, financial news, and reports to extract sentiment. It provides capabilities for training the model on specific datasets, such as the Financial PhraseBank, and offers a script to quickly get sentiment predictions from text files, including softmax probabilities for positive, negative, and neutral labels, and a calculated sentiment score. Developed as an intern research project by the Prosus AI team, finBERT is available on Hugging Face and is open-source under an Apache-2.0 license.

Best used for

Ideal for data scientists who need to analyze sentiment in financial documents, train specialized NLP models for finance, and predict sentiment from various financial texts. Especially valuable for researchers and developers working with large financial datasets to extract nuanced emotional tones.

Common actions

analyze financial sentiment

train NLP models

predict text sentiment

open-sourceautomated workflowdeepfakeworkflowscollaborationface swappinglow-code/no-codegithub copilot"AI Agents"

Capabilities

Key features

Financial sentiment analysis
BERT-based NLP model
Custom model training
Sentiment prediction script
Hugging Face integration

Target Audience

data scientist

Integrations

hugging-face

Pricing & Plans

Open Source

Free

FAQs

What kind of financial texts can finBERT analyze?

finBERT is designed to analyze a wide range of financial texts, including financial news articles, company reports, earnings call transcripts, and other documents where financial sentiment is expressed. Its training on a specialized financial corpus makes it particularly adept at this task.

Can I train finBERT on my own financial datasets?

Yes, finBERT allows for custom training on your own financial datasets. The project provides guidance on how to prepare your data, specifically mentioning the use of the Financial PhraseBank, and outlines the training parameters within its provided notebooks.

How do I get sentiment predictions using finBERT?

You can get sentiment predictions by using the provided `predict.py` script. Simply input a text file, and the script will output a CSV file containing the sentences, softmax probabilities for positive, negative, and neutral labels, and a calculated sentiment score.

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Subcategories trending in Finance

Trading & Investments Financial Modeling Risk & Fraud Detection Personal Finance Crypto & Blockchain Fintech

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Coding & Development › Open Source & Models Data & Analytics › Predictive Analytics

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