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@@ -21,8 +21,8 @@ It can classify financial text into **three categories**:
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  ## **📂 Dataset Used**
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  This model was trained on:
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- ✅ **Financial PhraseBank (All Agree)** - A widely used financial sentiment dataset.
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- ✅ **GitHub Generated Sentiment Dataset** - An additional dataset to improve generalization.
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  ## **⚙️ Training Parameters**
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  | Parameter | Value |
@@ -42,7 +42,7 @@ This model was trained on:
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  ## **🚀 Intended Use**
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  This model is designed for:
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- ✅ **Financial Analysts & Investors** to assess sentiment in earnings reports, news, and stock discussions.
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  ✅ **Financial Institutions** for NLP-based sentiment analysis in automated trading.
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  ✅ **AI Researchers** exploring financial NLP models.
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  ## **📂 Dataset Used**
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  This model was trained on:
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+ ✅ **Financial PhraseBank** - A widely used financial sentiment dataset.
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+ ✅ **GitHub Generated Sentiment Dataset** - An additional dataset to test the model.
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  ## **⚙️ Training Parameters**
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  | Parameter | Value |
 
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  ## **🚀 Intended Use**
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  This model is designed for:
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+ ✅ **Financial Analysts & Investors** to assess sentiment of financial sentences in ex. reports, news, and stock discussions.
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  ✅ **Financial Institutions** for NLP-based sentiment analysis in automated trading.
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  ✅ **AI Researchers** exploring financial NLP models.
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