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

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