
# Mamba Financial Headline Sentiment Classifier

A sentment classifier for finacial headlines using mamba 2.8b as the base model. 

Text is classified into 1 of 3 labels; positive, neutral, or negative.

## Prompt Format:

```
prompt = f"""Classify the setiment of the following news headlines as either `positive`, `neutral`, or `negative`.\n
  Headline: {headline}\n
  Classification:"""
```

where `headline` is the text you want to be classified.
{"language": ["en"], "license": "apache-2.0", "tags": ["finance"], "datasets": ["winddude/finacial_pharsebank_66agree_split", "financial_phrasebank"], "metrics": ["accuracy", "f1", "recall", "precission"], "base_model": ["state-spaces/mamba-2.8b"], "pipeline_tag": "text-classification", "model-index": [{"name": "financial-sentiment-analysis", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_phrasebank", "type": "financial_phrasebank", "args": "sentences_66agree"}, "metrics": [{"type": "accuracy", "value": 0.82, "name": "Accuracy"}, {"type": "percision", "value": 0.82, "name": "Percision"}, {"type": "recall", "value": 0.82, "name": "recall"}, {"type": "f1", "value": 0.82, "name": "F1"}]}]}]}
transformers pytorch finance text-classification en dataset:winddude/finacial_pharsebank_66agree_split dataset:financial_phrasebank base_model:state-spaces/mamba-2.8b license:apache-2.0 model-index endpoints_compatible region:us