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README.md

@@ -23,12 +23,12 @@ The **Emotion Classification Model** is a fine-tuned version of the `distilbert-
 
 This model leverages the pre-trained language understanding capabilities of DistilBERT to accurately categorize textual data into the following emotion classes:
 
-- **Joy**
 - **Sadness**
+- **Joy**
+- **Love**
 - **Anger**
 - **Fear**
 - **Surprise**
-- **Disgust**
 
 By fine-tuning on the `dair-ai/emotion` dataset, the model has been optimized to recognize and differentiate subtle emotional cues in various text inputs, making it suitable for applications that require nuanced sentiment analysis and emotional intelligence.
 
@@ -39,7 +39,6 @@ By fine-tuning on the `dair-ai/emotion` dataset, the model has been optimized to
 The Emotion Classification Model is designed for a variety of applications where understanding the emotional tone of text is crucial. Suitable use cases include:
 
 - **Sentiment Analysis:** Gauging customer feedback, reviews, and social media posts to understand emotional responses.
-- **Mental Health Monitoring:** Assisting therapists and counselors by analyzing patient communications for emotional indicators.
 - **Social Media Analysis:** Tracking and analyzing emotional trends and public sentiment across platforms like Twitter, Facebook, and Instagram.
 - **Content Recommendation:** Enhancing recommendation systems by aligning content suggestions with users' current emotional states.
 - **Chatbots and Virtual Assistants:** Enabling more empathetic and emotionally aware interactions with users.
@@ -51,7 +50,7 @@ While the Emotion Classification Model demonstrates strong performance across va
 - **Bias in Training Data:** The model may inherit biases present in the `dair-ai/emotion` dataset, potentially affecting its performance across different demographics, cultures, or contexts.
 - **Contextual Understanding:** The model analyzes text in isolation and may struggle with understanding nuanced emotions that depend on broader conversational context or preceding interactions.
 - **Language Constraints:** Currently optimized for English, limiting its effectiveness with multilingual or non-English inputs without further training or adaptation.
-- **Emotion Overlap:** Some emotions have overlapping linguistic cues, which may lead to misclassifications in complex or ambiguous text scenarios.
+- **Emotion Overlap:** Some emotions have overlapping linguistic cues, which may lead to misclassifications in ambiguous text scenarios.
 - **Dependence on Text Quality:** The model's performance can degrade with poorly structured, slang-heavy, or highly informal text inputs.
 
 ## Training and Evaluation Data
@@ -66,13 +65,6 @@ The model was trained and evaluated on the [`dair-ai/emotion`](https://huggingfa
   - **Training Set:** 16,000 samples
   - **Validation Set:** 2,000 samples
   - **Test Set:** 2,000 samples
-- **Emotion Classes:** 6
-  - **Joy:** 3,000 samples
-  - **Sadness:** 3,500 samples
-  - **Anger:** 2,500 samples
-  - **Fear:** 2,000 samples
-  - **Surprise:** 4,000 samples
-  - **Disgust:** 2,000 samples
 
 ### Data Preprocessing
 
@@ -107,7 +99,6 @@ The following hyperparameters were used during training:
 
 - **Mixed Precision Training:** Utilized PyTorch's Native AMP to accelerate training and reduce memory consumption when a CUDA-enabled GPU is available.
 - **Gradient Accumulation:** Implemented gradient accumulation with `2` steps to effectively increase the batch size without exceeding GPU memory limits.
-- **Early Stopping:** Incorporated `EarlyStoppingCallback` with a patience of `2` epochs to halt training if the validation loss does not improve, preventing overfitting.
 - **Checkpointing:** Configured to save model checkpoints at the end of each epoch, retaining only the two most recent checkpoints to manage storage efficiently.
 
 ### Training Duration