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A local, open-source ticket-routing project reports 90.5% hierarchical intent accuracy on a fixed 200-ticket sample and 89.8% across the complete 3,080-ticket BANKING77 test split. The 10,003 tickets in the headline were training data—not the evaluation set. These are results reported by the project authors in 2026, not an independent replication.
What was trained—and what was tested?
The project, laya-triage, fine-tuned a local model to route English banking-style support tickets. Its training data came from BANKING77’s 10,003-ticket training split. Evaluation used separate tickets from BANKING77’s held-out test split: a fixed 200-ticket sample for one comparison and all 3,080 test tickets for the full-split result.
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The project’s published checkpoint has 421 million parameters and builds on the Laya System 1 decision engine. The training procedure turns each training ticket into two routing choices—a coarse cluster and a fine-grained intent—creating 20,006 training sequences. The project reports fine-tuning on two Tesla T4 GPUs in about 46 minutes.
Why route tickets in two stages?
BANKING77 contains 77 support intents. Rather than choose directly among all 77 labels, laya-triage first selects one of 12 clusters, then selects among the 3–10 intents belonging to that cluster. The project maps the intents to eight departments as well.
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This coarse-to-fine design reduces the choices the model must distinguish at the second stage. In its full-test analysis, the project attributes the hierarchy’s advantage over direct 77-way classification both to improved choices among similar intents within a cluster and to recovering tickets the flat model sent to the wrong cluster.
The system also reports urgency, frustration, churn-risk, and refund-request signals, and supports a confidence gate that can send uncertain routing decisions to a human with a recorded reason. Those signals should not be confused with the fine-tuning result: the project fine-tuned routing choices, while the auxiliary signal heads were inherited from the base model. BANKING77 does not supply labels for those signals; the project assessed them separately on a hand-labeled subset.
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What the accuracy results show
The project’s 2026 evaluation report compares direct flat classification, zero-shot hierarchical routing, and fine-tuned hierarchical routing. The 200-ticket comparison used the same fixed sample across configurations.
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| Configuration | 200-ticket sample: accuracy | 200-ticket sample: macro-F1 | Full 3,080-ticket test split: accuracy | Full split: macro-F1 |
|---|---|---|---|---|
| Flat 77-way classification | 36.5% | 0.284 | 37.4% | |
| Zero-shot hierarchical routing | 51.0% | 0.443 | 51.1% | |
| Fine-tuned hierarchical routing | 90.5% | 0.848 | 89.8% | 0.897 |
The headline improvement from 51.0% to 90.5% compares zero-shot and fine-tuned hierarchical routing on the 200-ticket sample. It is not a comparison between 51% on one dataset and 90.5% on the full test split. Across all 3,080 test tickets, the fine-tuned hierarchical model scored 89.8% accuracy.
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On the complete test split, hierarchical routing also exceeded the flat model by 13.7 percentage points; the report gives an approximate 95% interval of 11.4 to 16.0 points for that paired difference. This measures the hierarchy’s benefit over the flat baseline, not the additional effect of fine-tuning. All figures are specific to the project’s model, BANKING77 data, and evaluation setup.
How the confidence gate changes the result
Accuracy alone does not tell an operator how many tickets can be routed automatically. In the zero-shot 200-ticket evaluation, a confidence threshold of about 0.84 produced 75.2% accuracy on the 60.5% of tickets the system auto-handled; the rest were escalated. The report cautions that one fewer correct auto-handled ticket would have put the observed accuracy below its 75% target.
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For the fine-tuned model, the report gives 90.5% accuracy at 100% coverage on that sample with the threshold set to 0.00. That is a property of this test sample, not evidence that a production team should disable escalation. A deployment should select a threshold based on error consequences and measure accuracy and coverage together, along with the escalation rate.
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- General-domain performance: The checkpoint is intended for English banking-style customer-support routing. The project warns that out-of-domain tickets can be mapped to the nearest banking concept. In a small IT-operations smoke test, seven of eight tickets were escalated; the remaining SSO lockout was assigned to a banking identity-verification intent.
- Reliable multilingual routing: The project’s small multilingual and hand-labeled evaluations are not proof of deployment-ready multilingual quality. Its results vary by language, and the fine-tuned checkpoint does not resolve the base model’s multilingual limitations.
- Auxiliary-signal accuracy from the routing score: Urgency, frustration, churn-risk, and refund-request outputs were assessed separately. The 90.5% intent figure does not measure their quality.
- Production speed or cost: The project reports that its two-pass 200-ticket fine-tuned evaluation ran in 16.1 seconds on one Kaggle T4. That is a development measurement, not a deployment-target latency benchmark. The project says deployment p50/p95 latency and cost per 1,000 tickets remain to be measured.
- Superiority to every alternative: The project has not run a GPT-4o-mini baseline, so its reported comparisons do not establish how it performs against that model.
How to inspect or reproduce the project
The repository links to the evaluation artifacts, results report, Kaggle fine-tuning notebook, and model card. It documents an evaluation reproduction command and identifies the repository and checkpoint as Apache License 2.0. Check the project’s own setup instructions for dependencies and compute requirements before running it; the results described here have not been independently reproduced.
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The model card describes downloading the checkpoint for local inference, so that inference path does not require an API call or a per-ticket inference charge. Local execution still requires obtaining and running the model on suitable hardware; the project’s reported Kaggle timing is not a promise of performance on another machine.
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