Upload README.md

README.md

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+---
+base_model: albert-base-v2
+tags:
+- safety
+- occupational-safety
+- albert
+- domain-adaptation
+- memory-efficient
+---
+
+# SafetyALBERT
+
+SafetyALBERT is a memory-efficient ALBERT model fine-tuned on occupational safety data. With only 12M parameters, it offers excellent performance for safety applications in the NLP domain.
+
+## Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
+model = AutoModelForMaskedLM.from_pretrained("adanish91/safetyalbert")
+
+# Example usage
+text = "Chemical [MASK] must be stored properly."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+## Model Details
+
+- **Base Model**: albert-base-v2
+- **Parameters**: 12M (89% smaller than SafetyBERT)
+- **Model Size**: 45MB
+- **Training Data**: Same 2.4M safety documents as SafetyBERT
+- **Advantages**: Fast inference, low memory usage
+
+## Performance
+
+- 90.3% improvement in pseudo-perplexity over ALBERT-base
+- Competitive with SafetyBERT despite 9x fewer parameters
+- Ideal for production deployment and edge devices
+
+## Applications
+
+- Occupational safety-related downstream applications
+- Resource-constrained environments