Update README.md

README.md

@@ -1,22 +1,113 @@
 ---
+license: apache-2.0
 datasets:
-- google-research-datasets/go_emotions
-language:
-- en
-metrics:
-- accuracy
-- f1
-- precision
-- recall
-base_model:
-- FacebookAI/roberta-base
+  - GoEmotions
 library_name: transformers
+language:
+  - en
 tags:
-- Emotion
-- Mental_Health
-- Deep_learning
-- text-classification
-- emotion-detection
-- mental-health
-- fine-tuned
----
+  - text-classification
+  - emotion-detection
+  - mental-health
+  - fine-tuned
+model-index:
+  - name: Mental-Health-Chatbot-using-RoBERTa
+    results:
+      - task:
+          type: text-classification
+        dataset:
+          name: GoEmotions
+          type: emotions
+        metrics:
+          - name: AI2 Reasoning Challenge (25-Shot)
+            type: AI2 Reasoning Challenge (25-Shot)
+            value: 64.59
+        source:
+          name: Open LLM Leaderboard
+          url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
+---
+
+# Mental Health Chatbot using RoBERTa (Fine-Tuned on GoEmotions)
+
+## Model Description
+
+This model is a fine-tuned version of **RoBERTa-base**, specifically designed to perform multi-label emotion classification. It has been trained on the **GoEmotions** dataset, a comprehensive dataset containing 28 emotion categories from Reddit comments. This model is optimized for applications requiring nuanced emotion analysis, such as mental health chatbots, sentiment analysis, and customer interaction systems.
+
+Key Features:
+- Multi-label emotion classification covering 28 fine-grained categories.
+- Real-time inference capabilities for interactive applications.
+- High accuracy on detecting nuanced emotions like gratitude, joy, and sadness.
+
+---
+
+## Repository
+
+The full project, including the chatbot implementation and fine-tuning code, can be found at:
+[GitHub Repository](https://github.com/kashyaparun/Mental-Health-Chatbot-using-RoBERTa-fine-tuned-on-GoEmotion)
+
+---
+
+## Applications
+
+- **Mental Health Chatbots**: Understand user emotions and provide empathetic responses for emotional well-being.
+- **Sentiment Analysis**: Analyze social media posts, reviews, and comments to gauge public sentiment.
+- **Customer Support Systems**: Enhance customer interactions by detecting emotional states.
+
+---
+
+## Training and Evaluation
+
+### Training Configuration
+
+- **Base Model**: RoBERTa-base
+- **Dataset**: [GoEmotions](https://www.kaggle.com/datasets/google/goemotions)
+- **Batch Size**: 32
+- **Optimizer**: AdamW
+- **Learning Rate Scheduler**: Cosine Annealing
+- **Loss Function**: Binary Cross-Entropy for multi-label classification
+- **Epochs**: 5
+
+### Evaluation Results
+
+The model achieved the following performance metrics on the GoEmotions dataset:
+
+| Metric        | Value  |
+|---------------|--------|
+| Macro F1-Score | 0.74 |
+| ROC-AUC        | 0.95 |
+
+Additional Benchmark:
+- **AI2 Reasoning Challenge (25-Shot)**: 64.59  
+  [Source: Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+
+---
+
+## Model Files
+
+The repository includes:
+- **Tokenizer Configuration**: `tokenizer.json`, `tokenizer_config.json`, and `vocab.json`.
+- **Model Weights**: `model_weights.pth`.
+- **Special Tokens Map**: `special_tokens_map.json`.
+
+These files are essential for reproducing the model or deploying it into other systems.
+
+---
+
+## How to Use
+
+To load the model and tokenizer:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("kashyaparun/Mental-Health-Chatbot-using-RoBERTa")
+model = AutoModelForSequenceClassification.from_pretrained("kashyaparun/Mental-Health-Chatbot-using-RoBERTa")
+
+# Perform inference
+text = "I'm feeling so joyful today!"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+outputs = model(**inputs)
+
+# Emotion logits
+print(outputs.logits)