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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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model_type: distilbert |
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tags: |
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- finance |
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- reconciliation |
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- distilbert |
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--- |
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# DistilBERT-Reconciler (v1) |
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Fine-tuned **DistilBERT** on 3.2 M labelled *post-trade break* descriptions + |
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resolution actions (ISO 20022 & proprietary logs). |
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| split | accuracy | micro-F1 | macro-F1 | |
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|-------|----------|----------|----------| |
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| hold-out (20 %) | **0.88** | **0.88** | **0.85** | |
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*Figure 1 – DistilBERT-Reconciler: end-to-end training & inference pipeline, showing fine-tuning loop (dashed) and production-time text-to-root-cause flow.* |
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## Intended use |
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Automated classification of reconciliation exceptions in fixed-income |
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settlement workflows (CUSIP/ISIN). Produces `label_id` then mapped to human |
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root-cause & recommended next action. *Not for retail investment advice.* |
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Not for retail investment advice. |
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## Training details |
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* **Base** : `distilbert-base-uncased` |
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* **Epochs** : 4 • lr = 3e-5 • max_len = 256 |
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* **Hardware** : 2× A100 40 GB |
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* **Loss curve & confusion matrix** : see `/training_artifacts/`. |
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### Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tok = AutoTokenizer.from_pretrained("kelvi23/DistilBERT-Reconciler") |
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mdl = AutoModelForSequenceClassification.from_pretrained("kelvi23/DistilBERT-Reconciler") |
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text = "COAF: partial collateral received awaiting tri-party" |
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inputs = tok(text, return_tensors="pt") |
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pred = mdl(**inputs).logits.argmax(-1).item() |
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``` |
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## Limitations & bias |
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Labels derived from North-American corporate-bond desks (2019–2025). May |
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under-perform on equities or non-USD/CAD repos without re-training. |
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## Citation |
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> Musodza, K. (2025). Bond Settlement Automated Exception Handling and Reconciliation. Zenodo. https://doi.org/10.5281/zenodo.16828730 |
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> |
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> ➡️ Technical white-paper & notebooks: https://github.com/Coreledger-tech/Exception-handling-reconciliation.git |
