arunabhachanda
/

supplychain-finbert

@@ -1,199 +1,106 @@
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 ---
+language: en
+license: apache-2.0
+tags:
+  - text-classification
+  - sentiment-analysis
+  - supply-chain
+  - geopolitical-risk
+  - finbert
+  - bert
+  - transfer-learning
+  - fine-tuning
+datasets:
+  - FinGPT/fingpt-sentiment-train
+  - zeroshot/twitter-financial-news-sentiment
+metrics:
+  - accuracy
+  - f1
 ---
+# supplychain-finbert
+Fine-tuned [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) for
+**supply chain geopolitical risk sentiment analysis**.
+Built for [SupplyGuard AI](https://github.com/arunabhachanda/supplyguard-ai) —
+a production-grade supply chain risk intelligence platform.
 ## Model Details
+| Property | Value |
+|---|---|
+| Base model | ProsusAI/finbert (BERT-base fine-tuned on Reuters/Bloomberg) |
+| Task | 3-class sentiment: negative / neutral / positive |
+| Fine-tuning strategy | Frozen layers 0–9, trainable layers 10–11 + pooler + head |
+| Training data | ~40,600 samples (FinGPT financial sentiment + Twitter Financial News + ~70 synthetic geopolitical headlines) |
+| Class balancing | Undersampling + weighted CrossEntropyLoss (neg=1.459, neu=1.060, pos=0.729) |
+| Test accuracy | 0.6393 |
+| Best val accuracy | 0.6454 |
+## Performance
+| Class | Precision | Recall | F1 |
+|---|---|---|---|
+| negative | 0.73 | 0.86 | 0.79 |
+| neutral | 0.52 | 0.75 | 0.62 |
+| positive | 0.74 | 0.45 | 0.56 |
+| **overall** | **0.67** | **0.64** | **0.63** |
+## Labels
+| ID | Label | Meaning |
+|---|---|---|
+| 0 | negative | Risk increasing — conflict, sanctions, disaster, supplier failure |
+| 1 | neutral | Routine updates, mixed signals, uncertainty |
+| 2 | positive | Risk decreasing — stability, trade agreements, recovery |
+## Usage
+```python
+from transformers import pipeline
+classifier = pipeline(
+    "text-classification",
+    model="arunabhachanda/supplychain-finbert",
+    return_all_scores=True,
+)
+result = classifier("Ceasefire in the region reopens key supply corridors")
+# → [{'label': 'negative', 'score': 0.04},
+#    {'label': 'neutral',  'score': 0.11},
+#    {'label': 'positive', 'score': 0.85}]
+# Polarity score used by SupplyGuard AI:
+polarity = result[2]['score'] - result[0]['score']   # P(positive) - P(negative)
+# → float in [-1.0, +1.0]  used as region_news_sentiment feature
+```
+## Transfer Learning Architecture
+```
+ProsusAI/finbert (pre-trained on financial news corpus)
+├── BERT Embeddings          [FROZEN]      ← vocabulary + positional encoding
+├── Transformer Layer 0–9    [FROZEN]      ← general language + financial knowledge
+├── Transformer Layer 10–11  [TRAINABLE]   ← adapted to supply-chain language
+├── Pooler                   [TRAINABLE]   ← [CLS] token representation
+└── Classifier Head (768→3)  [TRAINABLE]   ← new head for 3-class sentiment
+```
+**Trainable parameters:** 14,768,643 (13.5% of total)
+**Frozen parameters:** 94,715,904 (86.5% of total)
 ## Training Details
+- **Optimizer:** AdamW (lr=2e-5, weight_decay=0.01)
+- **Scheduler:** Linear warmup (10% steps) + linear decay
+- **Epochs:** 4
+- **Batch size:** 16
+- **Gradient clipping:** max_norm=1.0
+- **Class weights:** neg=1.459, neu=1.060, pos=0.729 (weighted CrossEntropyLoss)
+- **Split:** 80% train / 10% val / 10% test (stratified)
+## Built By
+Arunabha Kumar Chanda — M.Sc. Business Intelligence & Data Science, ISM Munich
+GitHub: [arunabhachanda](https://github.com/arunabhachanda)