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---
language: en
license: apache-2.0
tags:
- finance
- sentiment-analysis
- finbert
- trading
pipeline_tag: text-classification
---
# Bencode92/tradepulse-finbert-importance
## Description
Fine-tuned FinBERT model for financial importance analysis in TradePulse.
**Task**: Importance Classification
**Target Column**: `importance`
**Labels**: ['générale', 'importante', 'critique']
## Performance
*Last training: 2025-09-09 16:49*
*Dataset: `base_reference.csv` (1797 samples)*
| Metric | Value |
|--------|-------|
| Loss | 0.6364 |
| Accuracy | 0.8044 |
| F1 Score | 0.7994 |
| F1 Macro | 0.7994 |
| Precision | 0.7989 |
| Recall | 0.8044 |
## Training Details
- **Base Model**: Bencode92/tradepulse-finbert-importance
- **Training Mode**: Incremental
- **Epochs**: 2
- **Learning Rate**: 1e-05
- **Batch Size**: 4
- **Class Balancing**: None
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("Bencode92/tradepulse-finbert-importance")
model = AutoModelForSequenceClassification.from_pretrained("Bencode92/tradepulse-finbert-importance")
# Example prediction
text = "Apple reported strong quarterly earnings beating expectations"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
predictions = outputs.logits.softmax(dim=-1)
```
## Model Card Authors
- TradePulse ML Team
- Auto-generated on 2025-09-09 16:49:13 |