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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- finance |
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- sentiment-analysis |
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- finbert |
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- trading |
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- multi-label |
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pipeline_tag: text-classification |
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--- |
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# Bencode92/tradepulse-finbert-correlations |
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## Description |
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Fine-tuned FinBERT model for financial correlations analysis in TradePulse. |
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**Task**: Correlations Classification |
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**Target Column**: `correlations` |
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**Multi-Label**: Yes (61 labels) |
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## Performance |
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*Last training: 2025-07-30 12:03* |
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*Dataset: `base_reference.csv` (708 samples)* |
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| Metric | Value | |
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|--------|-------| |
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| Loss | 0.1960 | |
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| Subset Accuracy | 0.0000 | |
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| F1 Score | 0.0000 | |
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| F1 Micro | 0.0000 | |
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| F1 Macro | 0.0000 | |
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| Hamming Score | 0.9799 | |
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| Precision | 0.0000 | |
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| Recall | 0.0000 | |
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## Training Details |
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- **Base Model**: Bencode92/tradepulse-finbert-correlations |
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- **Training Mode**: Incremental |
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- **Epochs**: 2 |
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- **Learning Rate**: 1e-05 |
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- **Batch Size**: 4 |
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- **Class Balancing**: None |
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- **Problem Type**: Multi-Label Classification |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("Bencode92/tradepulse-finbert-correlations") |
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model = AutoModelForSequenceClassification.from_pretrained("Bencode92/tradepulse-finbert-correlations") |
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# Example prediction |
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text = "Apple reported strong quarterly earnings beating expectations" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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outputs = model(**inputs) |
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# Multi-label: apply sigmoid and threshold |
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predictions = torch.sigmoid(outputs.logits).squeeze() > 0.5 |
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``` |
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## Model Card Authors |
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- TradePulse ML Team |
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- Auto-generated on 2025-07-30 12:03:53 |
