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license: mit
tags:
- text-classification
- finance
- financial-news
- bert
- topic-classification
- transformers
- safetensors
- pytorch
- financial
- news
model-index:
- name: finnews-topic-single-classify
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: zeroshot/twitter-financial-news-topic
type: finance
metrics:
- type: accuracy
name: accuracy
value: 0.907943
- type: f1
name: F1
value: 0.899527
---
# Financial News Topic Classifier
This model is a fine-tuned BERT-based classifier for financial news topic classification based on [fuchenru/Trading-Hero-LLM](https://huggingface.co/fuchenru/Trading-Hero-LLM/blob/main/README.md), supporting 20 distinct financial topics. It is designed for use in financial NLP applications, news analytics, and automated trading systems.
## Model Description
- **Architecture:** BERT (for sequence classification)
- **Framework:** PyTorch, Transformers
- **Topics:** 20 financial news categories (see below)
- **License:** MIT
## Intended Uses & Limitations
- **Intended Use:**
- Classify financial news headlines or short texts into one of 20 financial topics.
- Use in financial analytics, news monitoring, and trading agent pipelines.
- **Limitations:**
- Trained on zeroshot/twitter-financial-news-topic; may not generalize to all financial news sources.
- Not suitable for non-financial or long-form text.
## Topics
| ID | Topic |
|----|------------------------------|
| 0 | Analyst Update |
| 1 | Fed \| Central Banks |
| 2 | Company \| Product News |
| 3 | Treasuries \| Corporate Debt |
| 4 | Dividend |
| 5 | Earnings |
| 6 | Energy \| Oil |
| 7 | Financials |
| 8 | Currencies |
| 9 | General News \| Opinion |
| 10 | Gold \| Metals \| Materials |
| 11 | IPO |
| 12 | Legal \| Regulation |
| 13 | M&A \| Investments |
| 14 | Macro |
| 15 | Markets |
| 16 | Politics |
| 17 | Personnel Change |
| 18 | Stock Commentary |
| 19 | Stock Movement |
## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("leonas5555/finnews-topic-single-classify")
model = AutoModelForSequenceClassification.from_pretrained("leonas5555/finnews-topic-single-classify")
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Example text
text = "LIVE: ECB surprises with 50bps hike, ending its negative rate era. President Christine Lagarde is taking questions"
result = nlp(text)
print(result)
# Output: [{'label': 'Fed | Central Banks', 'score': 0.98}]
```
## Example Inputs & Outputs
| Example Text | Predicted Topic |
|----------------------------------------------------------------------------------------------------------------------|-------------------------------|
| "Here are Thursday's biggest analyst calls: Apple, Amazon, Tesla, Palantir, DocuSign, Exxon & more" | Analyst Update |
| "LIVE: ECB surprises with 50bps hike, ending its negative rate era." | Fed \| Central Banks |
| "Goldman Sachs traders countered the industry's underwriting slump with revenue gains that raced past analysts' estimates." | Company \| Product News |
| "China Evergrande Group's onshore bond holders rejected a plan by the distressed developer to further extend a bond payment." | Treasuries \| Corporate Debt |
| "Investing Club: Morgan Stanley's dividend, buyback pay us for our patience after quarterly missteps" | Dividend |
## Training Data
- **Dataset:** zeroshot/twitter-financial-news-topic
- **Size:** 21 107 samples
- **Class Distribution:** Unbalanced; class weights used during training.
## Training Procedure
- **Framework:** HuggingFace Transformers (Trainer API)
- **Arguments:**
- **num_train_epochs:** 10
- **per_device_train_batch_size:** 32
- **per_device_eval_batch_size:** 32
- **gradient_accumulation_steps:** 1
- **learning_rate:** 2e-5
- **fp16:** True (Native AMP mixed precision)
- **warmup_ratio:** 0.1
- **label_smoothing_factor:** 0.05
- **max_grad_norm:** 1.0
- **max_length:** 256
- **evaluation_strategy:** "steps"
- **save_strategy:** "steps"
- **save_total_limit:** 3
- **load_best_model_at_end:** True
- **metric_for_best_model:** "f1"
- **run_name:** "topic_classifier"
- **seed:** 42
- **Early Stopping:** Patience of 2 evaluation steps (via `EarlyStoppingCallback`)
- **Optimizer:** Adam (betas=(0.9, 0.999), epsilon=1e-08)
- **Scheduler:** Linear
- **Metrics:** F1 (for best model selection), plus accuracy, precision, recall
## Evaluation Results
| Step | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 |
|------|---------------|----------------|----------|-----------|--------|------|
| 530 | 1.965800 | 0.917674 | 0.805684 | 0.743887 | 0.691372 | 0.696721 |
| 1060 | 0.733100 | 0.684078 | 0.876366 | 0.815078 | 0.823771 | 0.817982 |
| 1590 | 0.512200 | 0.638335 | 0.895312 | 0.895471 | 0.893691 | 0.893341 |
| 2120 | 0.418200 | 0.682780 | 0.894826 | 0.880995 | 0.885067 | 0.880227 |
| 2650 | 0.380200 | 0.683890 | 0.902113 | 0.890379 | 0.901867 | 0.894882 |
| 3180 | 0.359500 | 0.696923 | 0.902599 | 0.881292 | 0.902299 | 0.888526 |
| 3710 | 0.348800 | 0.691665 | 0.906000 | 0.891074 | 0.902236 | 0.895001 |
| 4240 | 0.342900 | 0.687194 | 0.906728 | 0.896421 | 0.900574 | 0.896865 |
| 4770 | 0.339900 | 0.705139 | 0.904785 | 0.892559 | 0.903573 | 0.896804 |
| 5300 | 0.337400 | 0.697512 | 0.907943 | 0.897653 | 0.903964 | 0.899527 |
## ONNX Export
An ONNX version of this model {TBD} for use with high-performance inference engines such as Infinity.
optimum-cli export onnx -m leonas5555/finnews-topic-single-classify
## License
MIT
## Inspired by:
- [nickmuchi/finbert-tone-finetuned-finance-topic-classification](https://huggingface.co/nickmuchi/finbert-tone-finetuned-finance-topic-classification/blob/main/README.md)
---
**References:**
- [fuchenru/Trading-Hero-LLM](https://huggingface.co/fuchenru/Trading-Hero-LLM/blob/main/README.md) |