Overview

FinBERT is a domain-specific adaptation of the BERT architecture, pre-trained on a massive corpus of financial communications. Unlike general-purpose language models, FinBERT is optimized for the nuances of financial terminology and sentiment, where words like 'bullish' or 'volatility' carry specific weights that standard models often miss. For developers, this means significantly higher accuracy in sentiment analysis for earnings reports, financial news, and analyst calls without needing to build a custom classifier from scratch. It integrates seamlessly into existing Hugging Face pipelines, making it a plug-and-play solution for building quantitative trading signals, risk monitoring dashboards, or automated financial summaries.

Highlights

Pre-trained on large-scale financial text corpora
High-precision sentiment analysis for financial markets
Seamless integration via Hugging Face Transformers
Apache-2.0 license for flexible commercial deployment
Outperforms general BERT on domain-specific tasks

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("ProsusAI/finbert")
tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download ProsusAI/finbert

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download ProsusAI/finbert config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('ProsusAI/finbert')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/ProsusAI/finbert

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/ProsusAI/finbert

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('ProsusAI/finbert')
tokenizer = AutoTokenizer.from_pretrained('ProsusAI/finbert')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model ProsusAI/finbert

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model ProsusAI/finbert README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('ProsusAI/finbert')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/ProsusAI/finbert.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/ProsusAI/finbert.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'ProsusAI/finbert')

Full Documentation

来源: HuggingFace

---
language: "en"
tags:

financial-sentiment-analysis

sentiment-analysis

widget:

text: "Stocks rallied and the British pound gained."

---

FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper FinBERT: Financial Sentiment Analysis with Pre-trained Language Models and our related blog post on Medium.

The model will give softmax outputs for three labels: positive, negative or neutral.

---

About Prosus

Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities. For more information, please visit www.prosus.com.

Contact information

Please contact Dogu Araci dogu.araci[at]prosus[dot]com and Zulkuf Genc zulkuf.genc[at]prosus[dot]com about any FinBERT related issues and questions.