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README.md
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@@ -12,7 +12,7 @@ metrics:
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- precision
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- recall
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widget:
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-
- text: "
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example_title: "Example 1"
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- text: "Rusza Black Friday. Lista promocji w sklepach."
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example_title: "Example 2"
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@@ -35,7 +35,7 @@ You can use this model directly with a pipeline for sentiment-analysis:
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from transformers import pipeline
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nlp = pipeline("sentiment-analysis", model="bardsai/FinanceSentimentPL-fast")
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nlp("
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```
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```bash
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[{'label': 'positive', 'score': 0.9999998807907104}]
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- precision
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- recall
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widget:
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- text: "Sprzedaż netto spadła o 30% do 36 mln EUR."
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example_title: "Example 1"
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- text: "Rusza Black Friday. Lista promocji w sklepach."
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example_title: "Example 2"
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from transformers import pipeline
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nlp = pipeline("sentiment-analysis", model="bardsai/FinanceSentimentPL-fast")
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nlp("Sprzedaż netto wzrosła o 30% do 36 mln EUR.")
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```
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```bash
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[{'label': 'positive', 'score': 0.9999998807907104}]
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