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README.md

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+---
+license: mit
+base_model: distilbert-base-uncased
+tags:
+- text-classification
+- tabular-classification
+- multimodal
+- erp
+- accounting
+- pytorch
+pipeline_tag: text-classification
+widget:
+- text: "[VENDOR] Amazon Services [DESC] Dell UltraSharp 27 USB-C Monitor"
+---
+
+# ERP GL Account Classifier
+
+Multimodal classifier for ERP GL account classification. Achieves **90.8% test accuracy** by learning from:
+- Text: vendor + description
+- Numerical: transaction amount + log(amount)
+- Categorical: department embeddings
+
+## Model Details
+
+- **Base model**: DistilBERT (distilbert-base-uncased)
+- **Architecture**: 3-branch fusion (text 768-d + numerical 64-d + categorical 32-d)
+- **Parameters**: 66.6M (all trainable)
+- **Training data**: 659 samples (synthetic ERP transactions)
+- **Test accuracy**: 90.8%
+- **Test F1**: 90.6%
+
+## Usage
+
+```python
+import json
+import numpy as np
+import torch
+from pathlib import Path
+from transformers import AutoTokenizer, AutoConfig
+from huggingface_hub import hf_hub_download
+
+# Download auxiliary files
+scaler_path = hf_hub_download(repo_id="EHTB/erp-classifier-dataset", filename="scaler.json", repo_type="dataset")
+vocab_path = hf_hub_download(repo_id="EHTB/erp-classifier-dataset", filename="dept_vocab.json", repo_type="dataset")
+
+with open(scaler_path) as f:
+    scaler_params = json.load(f)
+    scaler_mean = np.array(scaler_params['mean'])
+    scaler_scale = np.array(scaler_params['scale'])
+
+with open(vocab_path) as f:
+    dept_vocab = json.load(f)['dept2id']
+
+# Load model (requires custom ERPClassifierModel class)
+# See: https://github.com/yourusername/erp-classifier/blob/main/src/model.py
+
+tokenizer = AutoTokenizer.from_pretrained("EHTB/erp-classifier")
+config = AutoConfig.from_pretrained("EHTB/erp-classifier")
+
+# Note: This model uses a custom architecture (ERPClassifierModel).
+# You need to import the model class from the repository to load weights.
+```
+
+## Training Procedure
+
+- **Optimizer**: AdamW (lr=3e-5, weight_decay=0.01)
+- **Warmup**: 10% of training steps
+- **Batch size**: 16
+- **Epochs**: 10 (early stopping patience=5)
+- **Loss**: CrossEntropyLoss
+- **Training time**: ~3 minutes (Apple M1/M2)
+
+## Performance
+
+| GL Account | F1 Score | Support |
+|------------|----------|---------|
+| GL_6410_FACILITY_PANTRY | 100% | 18 |
+| GL_6450_CLEANING_MAINTENANCE | 100% | 19 |
+| GL_6400_OFFICE_CONSUMABLES | 96.8% | 15 |
+| GL_6250_TECH_BOOKS_TRAINING | 95.0% | 19 |
+| GL_6150_MARKETING_PROMO | 94.1% | 18 |
+| GL_6310_WORKPLACE_SAFETY_ERGONOMICS | 91.9% | 19 |
+| GL_1400_IT_HARDWARE_CAPEX | 79.1% | 19 |
+| GL_6220_IT_PERIPHERALS_OPEX | 64.0% | 15 |
+
+## Limitations
+
+- Trained on synthetic data (942 samples)
+- GL_6220 (IT peripherals < 800 EUR) has lower accuracy due to limited training examples
+- Requires custom model class (not compatible with standard AutoModelForSequenceClassification)
+
+## Citation
+
+```bibtex
+@misc{erp-classifier,
+  title={ERP GL Account Classifier},
+  author={Your Name},
+  year={2024},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/EHTB/erp-classifier}}
+}
+```