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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ metrics:
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+ - accuracy
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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: 0.84
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+ pipeline_tag: text-classification
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+ tags:
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+ - finance
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+ - sentiment
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  ---
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+
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+ # Mamba Finacial Headline Sentiment
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+
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+ Score 0.84 on accuracy for the finacial phrasebank dataset. A completely huggingface capitable implementation of sequence classification with mamba using: <https://github.com/getorca/mamba_for_sequence_classification>.
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+
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+ ## Inference:
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+
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+ ```
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+ from transformers import pipeline
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+
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+
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+ model_path = 'winddude/mamba_finacial_phrasebank_sentiment'
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+
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+ classifier = pipeline("text-classification", model=model_path, trust_remote_code=True)
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+
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+ text = "Finnish retail software developer Aldata Solution Oyj reported a net loss of 11.7 mln euro $ 17.2 mln for 2007 versus a net profit of 2.5 mln euro $ 3.7 mln for 2006 ."
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+
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+ classifier(text)
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+ ```
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+ gives:
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+ `[{'label': 'NEGATIVE', 'score': 0.8793253302574158}]`