| --- |
| 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}} |
| } |
| ``` |
|
|
