Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+tags:
+- federated-learning
+- finance
+- sentiment-analysis
+- bert
+- finbert
+- fedavg
+library_name: transformers
+pipeline_tag: text-classification
+authors: 
+- Harsh Prasad
+- Sai Dhole
+---
+
+## FinBERT–FedAvg: Federated Averaging for Financial Sentiment Analysis
+
+---
+
+### 📌 Model Summary
+
+This model is a **federated version of FinBERT** fine-tuned for
+**financial sentiment classification (Positive / Negative / Neutral)**.
+
+Training is performed across **three clients**:
+
+* Financial Twitter posts  
+* Financial news headlines  
+* Financial reports & statements  
+
+This model is trained using the **Federated Averaging (FedAvg)** algorithm,
+where each client trains locally on its own data and only **model weights** are shared.
+No raw data is exchanged, supporting privacy-preserving learning.
+
+This model is part of a research project comparing:
+
+* FedAvg  
+* FedProx  
+* Adaptive Aggregation  
+
+for federated financial NLP.
+
+---
+
+### 🧠 Intended Use
+
+Designed for:
+
+* Financial sentiment research  
+* Risk & market analytics  
+* Academic exploration of federated learning  
+
+Not intended for automated trading without expert oversight.
+
+---
+
+### 🏗 Model Architecture
+
+Base Model:
+
+```
+
+ProsusAI/finbert
+
+```
+
+Task:
+
+```
+
+Sequence classification — 3 classes
+
+```
+
+Training Setup:
+
+```
+
+3 federation clients
+10 global rounds
+3 local epochs
+FedAvg aggregation
+
+````
+
+---
+
+### 📊 Client Data Sources
+
+| Client   | Data Type         |
+| -------- | ----------------- |
+| Client-1 | Financial Twitter |
+| Client-2 | Financial News    |
+| Client-3 | Financial Reports |
+
+No raw data is shared between clients.
+
+---
+
+### 🔐 Privacy Advantage
+
+Only model updates are exchanged — not text data.  
+This supports data governance and privacy-aware ML.
+
+---
+
+### 📈 Performance (Validation)
+
+| Method | Final Avg F1-Score |
+| ------ | ------------------ |
+| FedAvg | **0.846**          |
+
+FedAvg provided **strong and stable global performance**
+across heterogeneous financial text sources.
+
+---
+
+### 🚀 Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    "harshprasad03/FinBERT-FedAvg"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "harshprasad03/FinBERT-FedAvg"
+)
+
+text = "Tech stocks fell after negative earnings guidance."
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+prob = torch.softmax(outputs.logits, dim=1)
+print(prob)
+````
+
+---
+
+### ⚠️ Limitations
+
+* Trained only on finance-domain text
+* Sentiment ≠ market prediction
+* Model may inherit dataset biases
+* Designed for research use
+
+---
+
+### 📚 Citation
+
+```
+Harsh Prasad, Sai Dhole (2025).
+FedAvg-based Federated FinBERT for Financial Sentiment Analysis.
+```
+
+---
+
+### 👨‍💻 Authors
+
+**Harsh Prasad**
+AI and ML Research
+
+**Sai Dhole**
+AI and ML Research
+
+---