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pretty_name: Intent and Domain Routing — Jev 130K
license: other
language:
- en
task_categories:
- text-classification
- text-ranking
- reinforcement-learning
tags:
- agents
- tool-calling
- structured-prediction
- preference-data
- reward-modeling
- jev
- typesafe
configs:
- config_name: default
data_files:
- split: train
path: "train-*.parquet"
- split: validation
path: "validation-*.parquet"
- split: test
path: "test-*.parquet"
---
# Intent and Domain Routing — Jev 130K
**130,000 validated typed decisions for modern AI systems.**
Intent, conversation-domain, RAG-task, and agent-workload routing decisions.
> **General-purpose dataset:** this release contains no clinical or medical
> data. Jev outputs are model predictions, not ground truth.
Each row pairs an upstream reference label with a pinned `jev-1.13.0`
prediction, confidence, structured answer, and provenance. The flat Parquet
layout works directly with Hugging Face `datasets`, DuckDB, Polars, or pandas.
## What it is good for
- intent and domain classifiers
- model/workload routers
- RAG task routing
- multilingual routing failure analysis
## Release summary
| Item | Value |
| --- | ---: |
| Rows / unique row IDs | 130,000 |
| Unique decision groups | 129,976 |
| Unique upstream source rows | 130,000 |
| Train | 119,467 |
| Validation | 2,829 |
| Test | 7,704 |
| Jev model | `jev-1.13.0` |
| Jev input tokens | 242,740,155 |
| Jev output tokens | 16,016,902 |
| Recorded estimated input cost | $10.195086510 |
| Rows with a Jev error | 0 |
## Quick start
```python
from datasets import load_dataset
dataset = load_dataset("MaziyarPanahi/Jev-Intent-Domain-Routing-130K")
row = dataset["train"][0]
print(row["state_preview"])
print(row["question_text"])
print(row["jev_prediction"], row["jev_confidence"])
```
For a private repository, authenticate first with `hf auth login`. Once the
repository is public, the same snippet works without authentication.
## How to read a row
| Need | Columns |
| --- | --- |
| Stable identity and split | `row_id`, `group_id`, `output_split` |
| Decision task | `priority`, `task_family`, `primitive` |
| Model input | `state_json`, `state_preview`, `question_text`, `criteria_json` |
| Upstream reference | `gold_label`, `gold_score`, `gold_json`, `label_source` |
| Jev result | `jev_prediction`, `jev_confidence`, `jev_probabilities_json`, `jev_answer_json` |
| Full receipt | `request_json`, `jev_raw_response_json`, token usage, latency, request ID |
| Provenance | source dataset, pinned revision, row ID, license, URL, transformation |
Decision primitives are intentionally simple:
- `choice`: one categorical label plus a probability distribution;
- `noul`: a bounded value from 0 to 1;
- `score`: a structured numeric assessment.
## Task mix
| Task family | Rows |
| --- | ---: |
| `agent_workload_routing` | 74,030 |
| `conversation_domain_routing` | 25,097 |
| `rag_task_routing` | 17,790 |
| `customer_intent_routing` | 13,083 |
## Decision primitives
| Primitive | Rows |
| --- | ---: |
| `choice` | 130,000 |
## Source mix
| Source | Rows |
| --- | ---: |
| `nvidia/Nemotron-SFT-Agentic-v2` | 74,030 |
| `nvidia/HelpSteer3` | 25,097 |
| `wandb/RAGTruth-processed` | 17,790 |
| `PolyAI/banking77` | 13,083 |
Every row retains the pinned source revision, source license, source row ID,
transformation, group ID, original label, complete Jev request, complete raw
response, normalized prediction, probability distribution or score, usage, and
latency.
## Descriptive agreement audit
Across the 130,000 rows with directly comparable source
labels, Jev/source-label agreement is **92.52%**.
This is a behavior audit, not an accuracy or benchmark claim; upstream labels
can be noisy or underspecified.
| Task family | Evaluated | Agreement | High-level interpretation |
| --- | ---: | ---: | --- |
| `agent_workload_routing` | 74,030 | 100.00% | Descriptive Jev/source-label agreement only |
| `conversation_domain_routing` | 25,097 | 72.65% | Descriptive Jev/source-label agreement only |
| `customer_intent_routing` | 13,083 | 78.56% | Descriptive Jev/source-label agreement only |
| `rag_task_routing` | 17,790 | 99.67% | Descriptive Jev/source-label agreement only |
High-confidence (`jev_confidence >= 0.90`) disagreements: **3,498**.
## Validation and splitting
The release uses repaired component-safe splits. Its independent audit verifies:
- exactly 130,000 rows and unique row IDs;
- one priority only: `intent_domain_model_router`;
- no decision group or upstream source row crosses splits;
- nonempty model, prediction, answer, raw response, completion, and provenance fields;
- zero `jev_error` values;
- valid choice probability distributions and in-schema predictions;
- matching normalized and raw Jev answers;
- pinned source revisions and flat, viewer-friendly Parquet shards.
## Important limitations
- The source/domain taxonomy is heterogeneous across upstream datasets.
- The HelpSteer3 preview showed weak multilingual recall for Jev routing.
- Confidence values are uncalibrated model outputs.
- Exact-request duplicates are retained where they reflect repeated or conflicting upstream evidence; consumers may aggregate or deduplicate by `request_json`, `group_id`, or source identity.
- The card declares `license: other`: every row records source-specific license and provenance, and users are responsible for reviewing the applicable upstream and provider terms.
- This is a general decision-modeling dataset, not a clinical dataset.
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