metadata
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: 2025-07-25 13:49
Dataset: news_20250725.csv (125 samples)
| Metric | Value |
|---|---|
| Loss | 1.9588 |
| Accuracy | 0.7500 |
| F1 Score | 0.7520 |
| F1 Macro | 0.7520 |
| Precision | 0.7555 | | Recall | 0.7500 |
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 2025-07-25 13:49:02