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--- |
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license: apache-2.0 |
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datasets: |
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- winddude/finacial_pharsebank_66agree_split |
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- financial_phrasebank |
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language: |
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- en |
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base_model: |
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- state-spaces/mamba-2.8b |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precission |
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model-index: |
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- name: financial-sentiment-analysis |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: financial_phrasebank |
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type: financial_phrasebank |
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args: sentences_66agree |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.82 |
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- name: Percision |
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type: percision |
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value: 0.82 |
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- name: recall |
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type: recall |
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value: 0.82 |
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- name: F1 |
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type: f1 |
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value: 0.82 |
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pipeline_tag: text-classification |
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tags: |
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- finance |
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--- |
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# Mamba Financial Headline Sentiment Classifier |
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A sentment classifier for finacial headlines using mamba 2.8b as the base model. |
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Text is classified into 1 of 3 labels; positive, neutral, or negative. |
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## Prompt Format: |
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``` |
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prompt = f"""Classify the setiment of the following news headlines as either `positive`, `neutral`, or `negative`.\n |
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Headline: {headline}\n |
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Classification:""" |
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``` |
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where `headline` is the text you want to be classified. |
