| --- |
| 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. |
|
|
