| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - finance |
| - sentiment-analysis |
| - finbert |
| - trading |
| - multi-label |
| pipeline_tag: text-classification |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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 |
