| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - finance |
| - sentiment-analysis |
| - finbert |
| - trading |
| pipeline_tag: text-classification |
| --- |
| |
| # Bencode92/tradepulse-finbert-sentiment |
|
|
| ## Description |
| Fine-tuned FinBERT model for financial sentiment analysis in TradePulse. |
|
|
| **Task**: Sentiment Classification |
| **Target Column**: `label` |
| **Labels**: ['negative', 'neutral', 'positive'] |
|
|
| ## Performance |
|
|
| *Last training: 2026-04-20 17:22* |
| *Dataset: `base_reference.csv` (1797 samples)* |
|
|
| | Metric | Value | |
| |--------|-------| |
| | Loss | 0.0000 | |
| | Accuracy | 1.0000 | |
| | F1 Score | 1.0000 | |
|
|
| | F1 Macro | 1.0000 | |
|
|
| | Precision | 1.0000 | |
| | Recall | 1.0000 | |
|
|
| ## Training Details |
|
|
| - **Base Model**: Bencode92/tradepulse-finbert-sentiment |
| - **Training Mode**: Incremental |
| - **Epochs**: 2 |
| - **Learning Rate**: 1e-05 |
| - **Batch Size**: 4 |
| - **Class Balancing**: None |
|
|
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| tokenizer = AutoTokenizer.from_pretrained("Bencode92/tradepulse-finbert-sentiment") |
| model = AutoModelForSequenceClassification.from_pretrained("Bencode92/tradepulse-finbert-sentiment") |
| |
| # 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 2026-04-20 17:22:25 |
