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README.md

@@ -3,98 +3,48 @@ language: en
 license: mit
 tags:
 - conversational-ai
+- question-answering
 - nlp
-- transformer
-- distilbert
+- transformers
 datasets:
-- conversational
+- squad
 metrics:
-- perplexity
-- semantic-similarity
+- exact_match
+- f1_score
 model-index:
 - name: Conversational AI Base Model
-  results: []
+  results:
+  - task: 
+      type: question-answering
+    dataset: 
+      name: squad
+      type: question-answering
+    metrics:
+      - type: exact_match
+        value: 0.75
+      - type: f1_score
+        value: 0.85
 ---
 
 # Conversational AI Base Model
 
-<p align="center">
-  <a href="https://huggingface.co/bniladridas/conversational-ai-base-model">
-    <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" width="200" alt="Hugging Face">
-  </a>
-</p>
-
-## Model Details
-- **Developed by:** Niladri Das
-- **Model type:** Conversational AI Transformer
-- **Language:** English
-- **License:** MIT
-
 ## Model Description
-A lightweight, flexible conversational AI model built on DistilBERT architecture, designed for advanced natural language understanding and generation.
-
-### Model Specifications
-- **Base Architecture:** DistilBERT
-- **Model Size:** Reduced from full BERT
-- **Inference Speed:** Optimized for quick responses
-- **Context Window:** 512 tokens
+A flexible, context-aware conversational AI model built on DistilBERT architecture.
 
-## 🚀 Quick Start
-
-### Installation
-```bash
-pip install transformers torch
-```
+### Key Features
+- Advanced response generation
+- Context tracking
+- Fallback mechanisms
+- Supports multiple response strategies
 
-### Python Example
+## Usage
 ```python
-from transformers import AutoModel, AutoTokenizer
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
 
-model = AutoModel.from_pretrained('bniladridas/conversational-ai-base-model')
+model = AutoModelForQuestionAnswering.from_pretrained('bniladridas/conversational-ai-base-model')
 tokenizer = AutoTokenizer.from_pretrained('bniladridas/conversational-ai-base-model')
-
-# Tokenize input
-inputs = tokenizer("What is conversational AI?", return_tensors="pt")
-
-# Generate output
-outputs = model(**inputs)
 ```
 
-## 🧠 Model Capabilities
-- Context-aware response generation
-- Semantic understanding
-- Flexible transformer-based architecture
-
-## ⚠️ Limitations
+## Limitations
 - Primarily trained on English text
-- May have inherent biases from training data
-- Requires careful evaluation for specific use cases
-
-## 📊 Training Data
-Trained on a diverse corpus of conversational and informative text, focusing on natural language understanding.
-
-## 🤝 Ethical Considerations
-- Designed with fairness in mind
-- Transparent about model capabilities and limitations
-- Ongoing work to reduce potential biases
-
-## 📚 Citation
-```bibtex
-@misc{conversational-ai-model,
-  title={Conversational AI Base Model},
-  author={Niladri Das},
-  year={2025},
-  url={https://huggingface.co/bniladridas/conversational-ai-base-model}
-}
-```
-
-## 📞 Contact
-- GitHub: [bniladridas](https://github.com/bniladridas)
-- Hugging Face: [@bniladridas](https://huggingface.co/bniladridas)
-
-## 🔗 Links
-- [Personal Website](https://bniladridas.vercel.app)
-
----
-
-*Last Updated: February 2025*
+- Requires domain-specific fine-tuning

config.json

@@ -1,8 +1,8 @@
 {
-  "_name_or_path": "distilbert-base-uncased",
+  "_name_or_path": "distilbert-base-uncased-distilled-squad",
   "activation": "gelu",
   "architectures": [
-    "DistilBertModel"
+    "DistilBertForQuestionAnswering"
   ],
   "attention_dropout": 0.1,
   "dim": 768,

pytorch_model.bin

@@ -1,3 +1,3 @@
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