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---
tags:
- finance
- finbert
- market
- financial
- generated_from_trainer
- financial
- stocks
- sentiment
- text-classification
widget:
- text: "Asian Stocks Set to Decline Amidst Growth Worries"
output:
- label: POSITIVE
score: 0.14
- label: INDECISIVE
score: 0.25
- label: NEGATIVE
score: 0.61
- text: "High inflation expectations becoming part of the American consumers behavioral norm"
output:
- label: POSITIVE
score: 0.49
- label: INDECISIVE
score: 0.30
- label: NEGATIVE
score: 0.21
datasets:
- FinBERT_market_based/autotrain-data
---
# Model Card for Finetuned FinBERT on Market-Based Facts
**<font color="orange">This LLM is fine-tuned on market reactions to events. By utilizing market-based data, it avoids human biases present in traditional annotation methods.</font>**
Our FinBERT model, finetuned on impactful news headlines about global equity markets, has shown significant performance improvements over standard models.
Its training on real-world market impact rather than subjective financial expert opinions sets a new standard for unbiased financial sentiment analysis. 📈
The dataset is uploaded on HuggingFace [here](https://huggingface.co/datasets/baptle/financial_headlines_market_based).
**Outperforms FinBERT**
- 🎯 +25% precision
- 🚀 +18% recall
**Outperforms DistilRoBERTa finetuned for finance**
- 🎯 +22% precision
- 🚀 +15% recall
**Outperforms GPT-4 zero-shot learning**
- 🎯 +15% precision
- 🚀 +8.2% recall
## Validation Metrics
| Metric | Value |
|--------------------|-----------------------|
| loss | 0.9176467061042786 |
| f1_macro | 0.49749240436690023 |
| f1_micro | 0.5627105467737756 |
| f1_weighted | 0.5279720746084178 |
| precision_macro | 0.5386355574899088 |
| precision_micro | 0.5627105467737756 |
| precision_weighted | 0.5462149036191247 |
| recall_macro | 0.517542664344306 |
| recall_micro | 0.5627105467737756 |
| recall_weighted | 0.5627105467737756 |
| accuracy | 0.5627105467737756 |
This model has been developed after publishing in the Risk Forum 2024 conference a paper that can be found here (https://arxiv.org/abs/2401.05447).
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