card: correct corpus count (5,280) + dataset link

README.md

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+---
+license: apache-2.0
+base_model: convaiinnovations/laya
+language: [en]
+tags:
+  - laya
+  - decision-model
+  - text-classification
+  - agent-routing
+  - system-1
+library_name: laya
+pipeline_tag: text-classification
+---
+
+# agent-task-classifier
+
+*(laya loop-taxonomy v1)*
+
+A fine-tuned [laya](https://huggingface.co/convaiinnovations/laya) checkpoint that classifies a
+user request to a personal-assistant agent by **the agent-loop shape it demands** — the skeleton
+of the high-level process required to fulfil it — rather than by topic.
+
+Same interface as every laya checkpoint: text in, one typed `choice` decision out, one forward
+pass, no generation. English, ~0.3 s/query on CPU.
+
+## The eleven labels
+
+| label | the loop it demands |
+|---|---|
+| `conversation` | answer/act directly — the default, safe fallback |
+| `diagnose` | find the cause of a specific malfunction or anomaly |
+| `research` | assemble understanding from investigation (multi-source, synthesis) |
+| `arrange` | commit a real-world arrangement — book, reserve, schedule, hire |
+| `acquire` | obtain a purchasable good, and verify it arrived |
+| `execute` | complete a known, externally-defined procedure (renew, file, claim) |
+| `make` | produce an artifact judged against a standard (tests, spec, taste) |
+| `tend` | review/maintain a standing concern (budget, health, home, wardrobe) |
+| `learn` | build the user's own ability over sessions (practice, tutoring) |
+| `companion` | inner-life support with no outcome in the world (reflection, therapy) |
+| `react` | urgent/deteriorating situation — act before full understanding |
+
+Boundary rule: two requests share a label iff they demand the same loop skeleton — same phases,
+same iteration driver, same exit criterion. Topic is irrelevant (`conversation` spans finance,
+wardrobe, memories; `make` spans code, letters, meal plans).
+
+## Usage
+
+```python
+import laya
+
+agent = laya.load("kumarabd1996/agent-task-classifier", device="cpu")
+questions = {
+    "loop": {
+        "type": "choice",
+        "instructions": "What does this request (or the request it continues) demand?",
+        "criteria": { ... },  # shipped as questions.json in this repo
+    }
+}
+out = agent.predict("the user's message", questions)
+out["answers"]["loop"]["choice"]         # e.g. "arrange"
+out["answers"]["loop"]["probabilities"]  # calibrated (temperatures fitted at train time)
+```
+
+Continuations: pass the request being continued in the state, the format the model was trained on:
+
+```
+Earlier request: What are my biggest categories?
+New request: Continue the bulk update
+```
+
+## Training
+
+- **Base**: `convaiinnovations/laya` (English checkpoint; ModernBERT-large + decision head), Apache-2.0.
+- **Corpus**: 5,280 items, 8 sources — MultiWOZ 2.1 booking utterances (~990, arrange), MASSIVE
+  en-US slices (~920, acquire/arrange/conversation), OASST2 prompts + continuations (~1,060,
+  teacher-labeled), synthetic items (~2,315) covering all eleven shapes (labels by construction,
+  incl. 34 hand-written boundary cases), and 1,056 continuation-form items
+  (`Earlier request: … / New request: …`). Per-class caps keep the largest class ≤ ~28%.
+  Released as a dataset: `kumarabd1996/agent-task-classifier-dataset`.
+- **Hyperparameters**: 4 epochs, effective batch 64 (micro 4 × accum 16), encoder lr 2.5e-5 /
+  head lr 1e-4, RLCD objective (default), option-order augmentation, max-len 256.
+- **Hardware**: one Mac, CPU only (~2 hours). A frozen-encoder variant failed to learn the
+  taxonomy (37% in-distribution); encoder adaptation is required — matching laya's own findings.
+- Temperatures fitted automatically on a held-out calibration slice (choice temperature ≈ 3.82).
+
+## Evaluation
+
+Boundary drills: 24 held-out hand-written cases targeting the taxonomy's hard boundaries
+(info-vs-procedure, understanding-vs-commitment, urgency-vs-worry, artifact-vs-investigation).
+
+| | accuracy | ECE | mean confidence |
+|---|---|---|---|
+| base `convaiinnovations/laya` (zero-shot) | 0.29 (7/24) | 0.54 | 0.83 (miscalibrated) |
+| **this checkpoint** | **0.83 (20/24)** | **0.13** | 0.96 |
+
+The 4 remaining misses, all at genuine boundaries: "Is Bali worth visiting in December?"
+(research, predicted arrange), "What does renewing a passport require?" (conversation,
+predicted execute), "Why have I been so tired lately?" (diagnose, predicted conversation),
+"Sign me up for the best EV lease deal you find." (arrange, predicted conversation).
+
+## Limitations
+
+- Trained largely on synthetic + public chat corpora; real user traffic is under-represented.
+- English only (the base multilingual checkpoint was not used).
+- The eleven labels are fixed; requests outside them resolve to `conversation` by design.
+- Continuations require the caller to supply the continued request in the state.
+- Not a safety classifier — do not use for guardrails or moderation.
+
+## Training data provenance
+
+MultiWOZ 2.1 (research use, MIT-licensed distribution) · MASSIVE en-US (CC-BY-4.0, Amazon) ·
+OASST2 (Apache-2.0, OpenAssistant) · synthetic/generated items (Apache-2.0 with this release).
+No user/private data was used in training.
+
+## Files
+
+`model.safetensors` (weights, incl. encoder) · `encoder/config.json` · `tokenizer/` ·
+`questions.json` (the taxonomy question, ready to pass to `predict`) ·
+`rl_agent_config.json` (fitted temperatures, training config) · `train_report.json`
+(before/after metrics).