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README.md

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+---
+language:
+- en
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model:
+- microsoft/deberta-v2-xlarge
+pipeline_tag: text-classification
+tags:
+- conversation-ending
+- healthcare
+- chatbot
+- text-classification
+---
+
+# EndConvo-health-deberta-v2
+
+## Model Description
+The **EndConvo-health-deberta-v2** is a fine-tuned conversational AI model based on the **DeBERTa** architecture. It is designed for binary classification tasks to determine whether a conversation in a health-related chatbot has reached its endpoint or should continue. The model significantly improves efficiency by identifying conversation closure, especially in healthcare applications, where accurate and timely responses are crucial.
+
+---
+
+## Intended Use
+- **Primary Use Case:** End-of-conversation detection in health-related chatbot systems.
+- **Scope of Application:** Healthcare dialogues, customer support automation, or any domain requiring conversational flow control.
+- **Limitations:**
+  - Reduced recall for the "True" (conversation ending) class, which could affect performance in ambiguous scenarios.
+  - The model requires GPU support for efficient inference on large-scale data.
+
+---
+
+## Training Dataset
+- **Dataset Name:** Custom health-related conversational dataset (`conversation_dataset.csv`).
+- **Structure:** Binary classification dataset with labels:
+  - `0` for "Continue conversation"
+  - `1` for "End conversation."
+- **Size:** 4,000 training samples and 1,000 validation samples.
+- **Source:** Annotated conversational data designed for healthcare-related use cases.
+- **Preprocessing:**
+  - Tokenization using DeBERTa tokenizer.
+  - Maximum sequence length of 256 tokens.
+  - Truncation applied for longer conversations.
+
+---
+
+## Model Details
+- **Base Model:** DeBERTa-V2
+- **Training Framework:** Hugging Face Transformers
+- **Optimizer:** AdamW with weight decay
+- **Loss Function:** Cross-entropy loss
+- **Batch Size:** 16
+- **Epochs:** 3
+- **Learning Rate:** 5e-5
+- **Evaluation Metric:** Accuracy, Precision, Recall, F1-score
+
+---
+
+## Evaluation Metrics
+- **Overall Accuracy:** 86.6%
+- **Precision:** 86.7%
+- **Recall:** 58.0%
+- **F1-Score:** 69.5%
+- **Validation Loss:** 0.3729
+
+### Confusion Matrix
+- **True Negatives (TN):** 71.29%
+- **False Positives (FP):** 2.35%
+- **False Negatives (FN):** 11.06%
+- **True Positives (TP):** 15.29%
+
+### Detailed Report
+
+| Class                   | Precision | Recall | F1-Score | Support |
+|-------------------------|-----------|--------|----------|---------|
+| **False (Continue)**    | 0.87      | 0.97   | 0.91     | 313     |
+| **True (End)**          | 0.87      | 0.58   | 0.70     | 112     |
+| **Macro Average**       | 0.87      | 0.77   | 0.80     | -       |
+| **Weighted Average**    | 0.87      | 0.87   | 0.86     | -       |
+
+---
+
+## Pipeline and Usage
+- **Task Type:** Text classification for conversation flow.
+- **Pipeline:** Predicts whether a conversation should continue or end.
+
+### Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("MathewManoj/EndConvo-health-deberta-v2")
+model = AutoModelForSequenceClassification.from_pretrained("MathewManoj/EndConvo-health-deberta-v2")
+
+# Example text input
+text = "Thank you for your help. I don't have any more questions."
+
+# Tokenize the input
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+# Prediction
+prediction = outputs.logits.argmax(dim=-1).item()
+print("Prediction:", "End" if prediction == 1 else "Continue")
+```
+---
+
+## Performance Insights
+### Strengths:
+- High accuracy and precision indicate the model performs well in correctly identifying most "Continue" conversations.
+
+### Limitations:
+- Lower recall for "End" conversations suggests the need for additional data augmentation or fine-tuning to improve sensitivity.
+
+---
+
+## Environment and Dependencies
+- **Framework:** Hugging Face Transformers (v4.46.3)
+- **Python Version:** 3.8+
+- **Dependencies:**
+  - `torch`
+  - `transformers`
+  - `safetensors`
+  - `numpy`
+
+### Conda Environment Configuration
+
+```yaml
+name: huggingface-env
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+  - python=3.8
+  - pip
+  - pip:
+      - torch==2.4.1
+      - transformers==4.46.3
+      - safetensors
+```
+---
+
+## Model Limitations
+1. The model exhibits reduced recall for the **"End conversation"** class, which could impact its utility in edge cases.
+2. Requires labeled data for fine-tuning in other domains or applications.