---
license: apache-2.0
library_name: transformers
tags:
- laya
- system-one
- calibrated-decisions
- rlcd
- structured-decisions
- typed-decisions
- benchmark
- glendesk
- call-analysis
metrics:
- accuracy
- brier_score
model-index:
- name: laya-glendesk-issues
  results:
  - task:
      type: text-classification
      name: System One Decision Benchmark
    dataset:
      type: LocalLLaMA/typed-decisions
      name: Typed Decisions
    metrics:
    - type: accuracy
      value: 0.357
    - type: brier_score
      value: 0.280
---

# Laya (Fine-Tuned for GlenDesk Call Analysis)

This is **Laya** fine-tuned on GlenDesk's call analysis clusters (11 issue types, 711 examples, 4,977 training sequences) derived from the [LocalLLaMA/typed-decisions](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) benchmark format.

On the GlenDesk test set (400 cases), it achieves **0.357 Accuracy**, trailing TypeSafe Jev 1.13.0 (0.727) and the benchmark's Teacher Self-Agreement ceiling (0.735).

## Head-to-Head Benchmark Results

| Model | Kind | Accuracy | Soft Acc | Brier Score | ECE | Score MAE | Within 1 Level | Latency (p50) | Cost/Case |
|---|---|---|---|---|---|---|---|---|---|
| **Laya (Ours)** | **fine-tuned** | **0.357** | **0.362** | **0.280** | **0.057** | **0.683** | **0.756** | **130.6 ms** | **$0.00 (Self-Hosted)** |
| TypeSafe Jev 1.13.0 | general | 0.727 | 0.580 | 0.148 | 0.144 | 0.391 | 0.952 | 710 ms | $0.0004 (API) |
| ModernBERT-base (149M) | specialist | 0.646 | 0.542 | 0.119 | 0.179 | 0.444 | 0.931 | 349 ms | $0.00 |
| Teacher Self-Agreement | ceiling | 0.735 | - | - | - | - | - | - | -

## Intended Use
Classify GlenDesk call transcripts for issue clusters:
- `hallucination_beauty_phrase`, `non_english_char_leak`, `midcall_response_glitch`
- `status_asked_false_positive`, `banned_filler_gap_definitely_help`
- `customer_lookup_no_match_on_number`, `abrupt_hangup_pattern`
- `summary_fabrication_vehicle_details`, `repeated_transfer_reason_loop`
- `digit_transposition_caller_id`, `transfer_failure_retry`

## Installation & Quickstart

```bash
pip install laya
```

```python
import laya
agent = laya.load("fcelabs/laya-glendesk-issues")
result = agent.predict(transcript, questions)
print(result["answers"])
```

## License
Apache 2.0. Fine-tuned by GlenDesk/FCE Labs.