Update README.md

README.md

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----
-title: Emotion Intensity Prediction using Transformer Based Models
-emoji: 🤩
-colorFrom: purple
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.x.x
-app_file: app.py
-pinned: false
----
-
-# Multitask Emotion Prediction Space
-
-This Hugging Face Space hosts a deep learning model that predicts emotions and their intensities from text.
-It utilizes a BERT-based architecture combined with lexicon features for enhanced performance.
-
-**Features:**
-- BERT-based text understanding.
-- Integration of NRC VAD, NRC Emotion Lexicon, and NRC Hashtag Emotion Lexicon.
-- Multi-task learning for emotion classification (joy, sadness, anger, fear) and intensity regression.
-
-**How to use:**
-Enter your text in the input box below and click "Predict Emotions" to see the model's output.
-
-**Model Details:**
-- Trained on dataset SemEval-2018 El-reg
-- Uses `bert-base-uncased` from Hugging Face.
-- `lex_dim`: 21 (number of combined lexicon features)
-
-**Files included:**
-- `app.py`: The Streamlit application code.
-- `best_multitask_multilabel_model.pth`: Trained model weights.
-- `*_scaler.pkl`: Joblib-saved feature scalers for lexicon features.
-- `NRC-*.txt`: Lexicon data files.
-
----
+---
+title: Emotion Intensity Prediction using Transformer Based Models
+emoji: 🤩
+colorFrom: purple
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.46.1
+app_file: app.py
+pinned: false
+---
+
+# Multitask Emotion Prediction Space
+
+This Hugging Face Space hosts a deep learning model that predicts emotions and their intensities from text.
+It utilizes a BERT-based architecture combined with lexicon features for enhanced performance.
+
+**Features:**
+- BERT-based text understanding.
+- Integration of NRC VAD, NRC Emotion Lexicon, and NRC Hashtag Emotion Lexicon.
+- Multi-task learning for emotion classification (joy, sadness, anger, fear) and intensity regression.
+
+**How to use:**
+Enter your text in the input box below and click "Predict Emotions" to see the model's output.
+
+**Model Details:**
+- Trained on dataset SemEval-2018 El-reg
+- Uses `bert-base-uncased` from Hugging Face.
+- `lex_dim`: 21 (number of combined lexicon features)
+
+**Files included:**
+- `app.py`: The Streamlit application code.
+- `best_multitask_multilabel_model.pth`: Trained model weights.
+- `*_scaler.pkl`: Joblib-saved feature scalers for lexicon features.
+- `NRC-*.txt`: Lexicon data files.
+
+---
 Feel free to duplicate this Space and experiment!