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@@ -18,6 +18,7 @@ tags:
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  - text-classification
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  - bert
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  - emojis
 
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  - v1.0
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  - sentiment-analysis
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  - nlp
@@ -43,13 +44,13 @@ base_model:
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  ---
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- # 😊 BERT-Emoji Emotion Classifier
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  ## 🎯 Overview
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- A lightweight, emoji-powered emotion classification model fine-tuned for **short text sentiment/emotion detection** using BERT architecture.
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- Each input text is tagged with one of **13 rich emotional categories**, mapped to expressive emojis 🎭. This model is ideal for chatbot understanding, social media sentiment, and mental health analysis based on short messages.
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  ---
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@@ -93,7 +94,7 @@ A minimal example to predict the emotion behind a simple sentence.
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  from transformers import pipeline
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  # Load the fine-tuned BERT Mini sentiment analysis model
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- sentiment_analysis = pipeline("text-classification", model="boltuix/bert-emoji")
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  # Analyze sentiment
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  result = sentiment_analysis("i love you")
@@ -116,7 +117,7 @@ An enhanced version to map model output to human-readable emotion with emojis.
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  ```python
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  from transformers import pipeline
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- # Load the fine-tuned BERT-Emoji model
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  sentiment_analysis = pipeline("text-classification", model="boltuix/bert-emotion")
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  # Define label-to-emoji mapping
@@ -173,11 +174,11 @@ This version enhances readability and gives an expressive emoji for the predicte
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  ## 📦 Model Card Info
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- - **Name:** BERT-Emoji
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  - **Author:** Boltuix
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  - **License:** Apache-2.0
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  - **Version:** v1.0
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- - **Tags:** emotion, emoji, text-classification
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  - **Status:** Public + Inference-ready
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  ---
 
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  - text-classification
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  - bert
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  - emojis
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+ - emotions
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  - v1.0
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  - sentiment-analysis
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  - nlp
 
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  ---
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+ # 😊 BERT-Emotions Emotion Classifier
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  ## 🎯 Overview
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+ A lightweight, Emotions-powered emotion classification model fine-tuned for **short text sentiment/emotion detection** using BERT architecture.
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+ Each input text is tagged with one of **13 rich emotional categories**, mapped to expressive emotions 🎭. This model is ideal for chatbot understanding, social media sentiment, and mental health analysis based on short messages.
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  ---
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  from transformers import pipeline
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  # Load the fine-tuned BERT Mini sentiment analysis model
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+ sentiment_analysis = pipeline("text-classification", model="boltuix/bert-emotions")
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  # Analyze sentiment
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  result = sentiment_analysis("i love you")
 
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  ```python
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  from transformers import pipeline
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+ # Load the fine-tuned BERT-Emotions model
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  sentiment_analysis = pipeline("text-classification", model="boltuix/bert-emotion")
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  # Define label-to-emoji mapping
 
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  ## 📦 Model Card Info
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+ - **Name:** BERT-Emotions
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  - **Author:** Boltuix
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  - **License:** Apache-2.0
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  - **Version:** v1.0
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+ - **Tags:** emotion, emoji, emotions, text-classification
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  - **Status:** Public + Inference-ready
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  ---