Bencode92/tradepulse-finbert-correlations

Description

Fine-tuned FinBERT model for financial correlations analysis in TradePulse.

Task: Correlations Classification
Target Column: correlations
Multi-Label: Yes (61 labels)

Performance

Last training: 2025-07-30 12:03
Dataset: base_reference.csv (708 samples)

Metric	Value
Loss	0.1960
Subset Accuracy	0.0000
F1 Score	0.0000
F1 Micro	0.0000
F1 Macro	0.0000
Hamming Score	0.9799
Precision	0.0000
Recall	0.0000

Training Details

Base Model: Bencode92/tradepulse-finbert-correlations
Training Mode: Incremental
Epochs: 2
Learning Rate: 1e-05
Batch Size: 4
Class Balancing: None
Problem Type: Multi-Label Classification

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Bencode92/tradepulse-finbert-correlations")
model = AutoModelForSequenceClassification.from_pretrained("Bencode92/tradepulse-finbert-correlations")

# Example prediction
text = "Apple reported strong quarterly earnings beating expectations"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
# Multi-label: apply sigmoid and threshold
predictions = torch.sigmoid(outputs.logits).squeeze() > 0.5

Model Card Authors

TradePulse ML Team
Auto-generated on 2025-07-30 12:03:53