|
|
|
--- |
|
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). |
|
|
|
|
|
|
