🧠
finbert model by ProsusAI
⭐ 35
🧠
Model

finbert

by ProsusAI ID: hf-model--prosusai--finbert
Downloads 2.9M
FNI Rank 35
Percentile Top 0%
Activity
β†’ 0.0%

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 us...

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Audited 35 FNI Score
Tiny - Params
- Context
Hot 2.9M Downloads
Dense BERTFORSEQUENCECLASSIFICATION Architecture
Model Information Summary
Entity Passport
Registry ID hf-model--prosusai--finbert
Provider huggingface
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Academic & Research Attribution

BibTeX 
@misc{hf_model__prosusai__finbert,
  author = {ProsusAI},
  title = {finbert Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/ProsusAI/finbert}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
ProsusAI. (2026). finbert [Model]. Free2AITools. https://huggingface.co/ProsusAI/finbert

πŸ”¬Technical Deep Dive

Full Specifications [+]

⚑ Quick Commands

πŸ€— HF Download
huggingface-cli download prosusai/finbert
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AI Nexus Index

Methodology β†’ πŸ“˜ What is FNI?
35.0
Top 0% Overall Impact
πŸ”₯ Popularity (P) 0
πŸš€ Velocity (V) 0
πŸ›‘οΈ Credibility (C) 0
πŸ”§ Utility (U) 0
Nexus Verified Data

πŸ’¬ Why this score?

The Nexus Index for finbert aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.

πŸ”— Source Links (Click to verify)

πŸ“Š P: HuggingFace Stats πŸ“ˆ V: 7-Day Delta πŸ“„ C: Papers With Code πŸ”§ U: Deploy Score
Data Verified πŸ• Last Updated: Not calculated
Free2AI Nexus Index | Fair Β· Transparent Β· Explainable | Full Methodology
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Technical Deep Dive

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.

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
  • β€’ Source: Unknown
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AI Summary: Based on Hugging Face metadata. Not a recommendation.

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100% Data Disclosure Active

πŸ†” Identity & Source

id
hf-model--prosusai--finbert
source
huggingface
author
ProsusAI
tags
transformerspytorchtfjaxberttext-classificationfinancial-sentiment-analysissentiment-analysisenarxiv:1908.10063endpoints_compatibledeploy:azureregion:us

βš™οΈ Technical Specs

architecture
BertForSequenceClassification
params billions
null
context length
null
pipeline tag
text-classification

πŸ“Š Engagement & Metrics

likes
1,037
downloads
2,853,068

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