Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+
+# EU Organization Classifier
+
+A multilingual transformer model fine-tuned for classifying European organizations into standardized categories based on EU funding database schemas.
+
+## Model Description
+
+This model is fine-tuned from `intfloat/multilingual-e5-large` to classify organizations from European Union funding databases (CORDIS, Erasmus+, LIFE, Creative Europe) into five standardized organization types:
+
+- **PUB**: Public bodies (governments, municipalities, agencies)
+- **HES**: Higher Education Sector (universities, schools, educational institutions)
+- **REC**: Research Organizations (research institutes, laboratories, R&D centers)
+- **PRC**: Private Companies (businesses, SMEs, corporations)
+- **OTH**: Other Organizations (NGOs, associations, foundations, cultural organizations)
+
+## Training Data
+
+The model was trained on **~140,000 organization names** from multiple EU funding databases:
+
+- **CORDIS**: European research and innovation database
+- **Erasmus+**: European education and training programs
+- **LIFE**: European environment and climate action program  
+- **Creative Europe**: European cultural and creative sector programs
+
+**Note**: INTERREG data was excluded from training due to data quality issues but the model can be applied to INTERREG organizations for classification.
+
+## Performance
+
+- **Overall Accuracy**: 85-90%+ on held-out test data
+- **Multilingual Support**: Trained on organization names in 20+ European languages
+- **Domain Expertise**: Specialized for European institutional and funding contexts
+
+## Usage
+
+### Direct Classification
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "your-username/eu-organization-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Organization types mapping
+LABELS = {0: 'PUB', 1: 'HES', 2: 'REC', 3: 'PRC', 4: 'OTH'}
+
+def classify_organization(org_name):
+    # Tokenize input
+    inputs = tokenizer(org_name, return_tensors="pt", truncation=True, max_length=128)
+
+    # Get prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        prediction = torch.argmax(outputs.logits, dim=-1).item()
+        confidence = torch.softmax(outputs.logits, dim=-1).max().item()
+
+    return LABELS[prediction], confidence
+
+# Examples
+examples = [
+    "Universität Wien",
+    "Ministry of Education and Science",
+    "ACME Technology Solutions Ltd",
+    "European Research Council",
+    "Greenpeace International"
+]
+
+for org in examples:
+    org_type, confidence = classify_organization(org)
+    print(f"{org} → {org_type} (confidence: {confidence:.3f})")
+```
+
+### Batch Processing
+
+```python
+def classify_organizations_batch(org_names, batch_size=32):
+    results = []
+
+    for i in range(0, len(org_names), batch_size):
+        batch = org_names[i:i+batch_size]
+
+        # Tokenize batch
+        inputs = tokenizer(batch, return_tensors="pt", truncation=True, 
+                          padding=True, max_length=128)
+
+        # Get predictions
+        with torch.no_grad():
+            outputs = model(**inputs)
+            predictions = torch.argmax(outputs.logits, dim=-1)
+            confidences = torch.softmax(outputs.logits, dim=-1).max(dim=-1).values
+
+        # Convert to labels
+        for pred, conf in zip(predictions, confidences):
+            results.append((LABELS[pred.item()], conf.item()))
+
+    return results
+```
+
+## Use Cases
+
+### EU Funding Analysis
+- Classify beneficiary organizations in EU funding databases
+- Analyze funding distribution across organization types
+- Identify research collaboration patterns
+
+### Organization Deduplication  
+- Standardize organization types for entity resolution
+- Improve clustering of similar organizations across databases
+- Enhance data quality in multi-source datasets
+
+### Institutional Research
+- Study European research and innovation ecosystems
+- Analyze public-private collaboration networks
+- Map educational and research infrastructure
+
+## Languages Supported
+
+The model handles organization names in multiple European languages including:
+- English, German, French, Spanish, Italian
+- Dutch, Portuguese, Polish, Czech, Hungarian
+
+## Performance Metrics
+
+### Classification Report
+
+| Class | Precision | Recall | F1-Score | Support |
+|-------|-----------|--------|----------|---------|
+| **PUB** | 0.78 | 0.75 | 0.76 | 1941 |
+| **HES** | 0.88 | 0.89 | 0.89 | 15507 |
+| **REC** | 0.87 | 0.90 | 0.89 | 7734 |
+| **PRC** | 0.72 | 0.67 | 0.69 | 2175 |
+| **OTH** | 0.59 | 0.45 | 0.51 | 1180 |
+
+**Overall Accuracy**: 85%
+
+### Confusion Matrix
+
+```
+       PUB   HES   REC   PRC   OTH
+PUB  1457    65    65    80   508
+HES    38  1447    72    60   324
+REC    83    44   535   129   389
+PRC    62    22    37  6977   636
+OTH   384   283   204   773  13863
+```
+
+## License
+
+This model is released under the [MIT License](LICENSE).