Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
DOI:
License:
| # For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 | |
| # Doc / guide: https://huggingface.co/docs/hub/datasets-cards | |
| pretty_name: Synthetic Multi-Turn Customer Support Tickets (Consumer Electronics) | |
| language: | |
| - en | |
| license: mit | |
| task_categories: | |
| - text-generation | |
| tags: | |
| - synthetic | |
| - customer-support | |
| - multi-turn | |
| - conversational | |
| - dialogue | |
| size_categories: | |
| - 10K<n<100K | |
| annotations_creators: | |
| - machine-generated | |
| language_creators: | |
| - machine-generated | |
| source_datasets: | |
| - original | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: consumer-electronics-support.jsonl | |
| # Dataset Card for Synthetic Multi-Turn Customer Support Tickets (Consumer Electronics) | |
| 99,930 wholly synthetic multi-turn customer-support conversations in a consumer-electronics | |
| retail domain, each with assigned ticket metadata, a machine coherence score, and provenance | |
| linking it to the run that produced it. | |
| ## Dataset Details | |
| ### Dataset Description | |
| Every conversation is fabricated by a language model from a committed domain prompt. No real | |
| support transcript, customer, or agent is involved at any point, and none was used as source | |
| material. The conversations are between a customer of a mid-sized online retailer selling | |
| consumer electronics — headphones, smart speakers, wearables, small home devices — and a support | |
| agent employed by that retailer, covering billing, technical, account, shipping, product and | |
| miscellaneous issues. | |
| The dataset exists to support work that needs support-ticket-shaped text at scale — dialogue | |
| modelling, routing and triage experiments, evaluation harnesses, pipeline load testing — without | |
| the privacy exposure that real support archives carry. | |
| Each record is one complete ticket: an ordered sequence of turns alternating customer and agent, | |
| starting with the customer, plus the metadata the conversation was generated to fit. | |
| Composition is **assigned before generation rather than measured after it**. Each record's | |
| category, priority, channel, resolution status, turn count, subdomain and timestamps are seeded | |
| choices made from the run seed and the record's position, so the distribution is a property of the | |
| request rather than an accident of what the model happened to produce. | |
| - **Curated by:** Robert Bates (arpieb) | |
| - **Funded by [optional]:** Unfunded; personal project | |
| - **Shared by [optional]:** Robert Bates (arpieb) | |
| - **Language(s) (NLP):** en | |
| - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
| ### Dataset Sources [optional] | |
| - **Repository:** https://github.com/arpieb/ticket-dataset-generator | |
| - **Paper [optional]:** None | |
| - **Demo [optional]:** None | |
| ## Uses | |
| ### Direct Use | |
| Suitable for: | |
| - fine-tuning or evaluating multi-turn dialogue models on support-style exchanges — at ~100k | |
| records and ~100M characters of conversational content this is large enough to train a small | |
| model or adapt a larger one; | |
| - ticket classification, routing, priority prediction and summarisation, where the label is known | |
| by construction rather than inferred — every record carries its category, priority, channel, | |
| resolution status and subdomain as assigned inputs, not as post-hoc annotation; | |
| - stratified experiments: the 20 declared subdomains are balanced to within ~±2% of each other | |
| (4,890–5,121 records each), so per-subdomain slices are directly comparable; | |
| - testing and benchmarking data pipelines that consume conversational JSONL, since every record is | |
| schema-validated and carries stable identifiers; | |
| - teaching and demonstration, where a real support archive would be inappropriate to distribute. | |
| Conversations are short by construction — 4 to 8 turns, mean 6.0, mean 1,003 characters of turn | |
| content per record. The corpus is **not** suitable for work on long-horizon dialogue, multi-issue | |
| tickets, or conversations that span sessions; none of those exist in it. | |
| ### Out-of-Scope Use | |
| - **Not a sample of real support traffic.** Nothing here was observed. Frequencies, phrasing, and | |
| problem distributions reflect a prompt, a requested composition and a model, not any real | |
| customer population. The composition was *specified*, so it cannot be evidence of anything. Do | |
| not use it to estimate how often anything happens in the world. | |
| - **Not a benchmark, and the coherence scores are not ground truth.** They were produced by the | |
| same model that wrote the conversations, against a rubric that has no calibration record (see | |
| Limitations). Treat `coherence_score` as a filtering artifact of this run. | |
| - **Not a privacy-safe stand-in for de-identification research.** Synthetic-by-construction is not | |
| the same problem as de-identified-after-the-fact, and this dataset cannot be used to validate a | |
| de-identification method. | |
| - **Not a basis for consequential decisions about real people.** Do not train systems that triage, | |
| prioritise or deny real customer requests on this corpus alone; the priority and resolution | |
| labels encode a requested distribution, not a policy anyone validated. | |
| ## Dataset Structure | |
| One JSON object per line (JSONL, UTF-8). 99,930 records, 196,658,051 bytes. A single `train` | |
| split; no test split is provided, because splitting is the consumer's decision and depends on the | |
| task. Records are grouped by nothing in particular, so a random split is sound; a | |
| subdomain-stratified split is straightforward from the `subdomain` field. | |
| | Field | Type | Notes | | |
| |---|---|---| | |
| | `record_id` | string (UUIDv5) | Stable, derived from the run and position | | |
| | `record_index` | int | Position in the run, ascending, 0–99,999 — **with 70 gaps, see below** | | |
| | `run_id` | string (UUID) | The generation run; matches the manifest. One value across the corpus | | |
| | `schema_version` | string | `1.0.0` | | |
| | `source_id` | string | Domain prompt identity: `consumer-electronics-support.md@8d67eaf56b0a` | | |
| | `subdomain` | string | Seeded choice from the prompt's declared list | | |
| | `scenario` | string | The specific situation the model elaborated | | |
| | `metadata` | object | `category`, `priority`, `channel`, `resolution_status`, `created_at`, `resolved_at` | | |
| | `turns` | array | `{role: "customer"｜"agent", content: string}`, customer first, strictly alternating | | |
| | `quality` | object | `coherence_score` (0–1), `rubric_id` | | |
| | `generation` | object | `model_id`, `judge_model_id` | | |
| Verified across all 99,930 records: every conversation starts with the customer and strictly | |
| alternates roles, and `resolved_at` is present when and only when `resolution_status` is | |
| `resolved`. | |
| **`record_index` is not contiguous.** The run requested 100,000 records and wrote 99,930. Indices | |
| run 0–99,999 with 70 positions absent. The manifest accounts for 55 of these as | |
| `attempts_exhausted`; the remaining 15 are not accounted for by any recorded discard reason. Do | |
| not use `record_index` as a row number or assume `max(record_index) + 1 == len(corpus)`. | |
| **Achieved composition**, read from `composition_achieved` in the manifest and confirmed by | |
| counting the corpus. Worst-member drift from the requested distribution is **0.022pp** | |
| (`category.billing`, 24.9785% against 25%): | |
| | Dimension | Distribution | | |
| |---|---| | |
| | category | technical 25.01% (24,992), billing 24.98% (24,961), account 20.01% (19,992), shipping 15.00% (14,991), product 10.00% (9,996), other 5.00% (4,998) | | |
| | priority | normal 50.00% (49,967), low 20.00% (19,988), high 19.99% (19,979), urgent 10.00% (9,996) | | |
| | channel | email 40.00% (39,970), chat 35.00% (34,980), phone 15.00% (14,987), web_form 10.00% (9,993) | | |
| | resolution_status | resolved 70.02% (69,970), escalated 14.99% (14,981), unresolved 10.00% (9,993), abandoned 4.99% (4,986) | | |
| (The `composition_drift_pp` block in the run report is miscomputed — it reports each member's drift | |
| as the negative of its requested share, as though the achieved share were zero. The drift above | |
| was recomputed from `composition_achieved` against `composition_requested`. See Limitations.) | |
| Turn counts range 4–8, mean 6.006, near-uniform: 4 turns 19,725; 5 turns 20,013; 6 turns 20,009; | |
| 7 turns 20,328; 8 turns 19,855. | |
| All 20 of the domain prompt's declared subdomains appear, between 4,890 | |
| (`technical-device-wont-power-on`) and 5,121 (`shipping-damaged-on-arrival`) records each. | |
| `metadata.created_at` spans 2025-07-05T00:05:58Z to 2025-12-31T23:59:45Z, distributed across the | |
| six months (14,960–17,330 records per month; July is short because the window opens on the 5th). | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| Real support tickets are among the densest sources of personal data that exist, which makes them | |
| both valuable and largely undistributable. Scrubbing them after the fact is unreliable and | |
| irreversible once published. This dataset takes the other route: admit no real identifiers at any | |
| point, so there is nothing to scrub. Consumer-electronics support is a good domain for a first | |
| release at scale — the issues are mundane, the stakes are low, and nothing in the domain invites | |
| the model toward medical, financial or legal content that would carry risks a card like this one | |
| could not discharge. | |
| The generator is the deliverable; this corpus is one output of it. The pipeline enforces | |
| reproducibility, provenance, and a blocking privacy scan as structural properties rather than | |
| review-time checks. | |
| ### Source Data | |
| #### Data Collection and Processing | |
| Generated by `ollama_chat/gpt-oss:20b`, self-hosted, from a committed domain prompt document | |
| (`consumer-electronics-support.md@8d67eaf56b0a`) declaring 20 subdomains. | |
| Per record: the pipeline assigns metadata from the seed, prompts the model for a conversation | |
| fitting that assignment, then applies four gates in order — structural validation, a blocking | |
| privacy scan, coherence judging, and schema validation. A record failing any gate is discarded and | |
| counted by reason; the slot is retried up to 3 times. Output is written to staging and moved to | |
| the release path only after the privacy floor is demonstrated against known canaries. | |
| **This run:** 105,236 generated, 99,930 written — a 5.04% discard rate. Discards by reason: | |
| 2,144 `coherence_below_threshold`, 1,776 `structural_invalid`, 761 `privacy_finding`, 552 | |
| `turn_count_out_of_range`, 55 `attempts_exhausted`, 18 `unjudgeable` (5,306 total, which | |
| reconciles exactly with generated minus written). Zero duplicates. Zero records reached the output | |
| with a blocking privacy finding. The run was resumed 3 times and its recorded outcome is | |
| `completed`, verdict `pass`. | |
| Reproduction inputs: run `8c56469b-f094-4686-90dc-29715bab4228`, seed `42`, code revision | |
| `ab247fa42a66`, corpus SHA-256 | |
| `a49bcc9ca0d228d456ae6bd371e8517940629ecd9e97140d65c45cd8c56c755b` (verified against the file). | |
| **The code revision was dirty.** `code_revision.dirty` is `true` in the manifest: this corpus was | |
| generated from a working tree with uncommitted changes, so it cannot be reproduced from any | |
| commit. `ab247fa42a66` names the nearest ancestor commit, not the code that ran. | |
| Reproduction is in any case **structural, not textual**. No model sampling seed was set | |
| (`models.generator.sampling_seed` is `null`), so a rerun with the same seed, config, prompt and | |
| rubric lands every record on the same composition, subdomain, turn count and timestamps, but the | |
| conversation text differs on every run. This is a property of the method, not a defect of the run. | |
| The run's resolved configuration is recorded in the manifest and is **not** the repository's | |
| `configs/samples/release.toml`: it used `turns.max = 8` (not 12), `time_window.start = 2025-07-05` | |
| (not 2025-07-01), `max_concurrency = 4`, `checkpoint_interval = 100`, and no budget ceiling. Use | |
| `config-from-manifest` rather than the sample config to recover what actually ran. | |
| #### Who are the source data producers? | |
| A language model, prompted by an automated pipeline. No humans produced any conversational | |
| content, and no human-authored text was used as source material. | |
| ### Annotations [optional] | |
| #### Annotation process | |
| Each conversation is scored 0–1 for coherence by a second model call against a committed, versioned | |
| rubric (`coherence-v2`) with four weighted criteria: `single_issue` (0.30), `role_consistency` | |
| (0.25), `conversational_flow` (0.25), `metadata_fit` (0.20). The record's score is the weighted | |
| mean. Records below 0.8 are discarded before release; the score of every surviving record is | |
| retained. 2,144 records were discarded on this gate. | |
| Score distribution (computed from the corpus — the manifest's | |
| `coherence_score_distribution` field was not populated, see Limitations): mean 0.960, median 1.0, | |
| min 0.80, max 1.0, standard deviation 0.053, across 211 distinct values. By bucket — | |
| 0.80–0.85: 2,886 (2.89%); 0.85–0.90: 3,952 (3.95%); 0.90–0.95: 28,049 (28.07%); 0.95–1.00: 6,079 | |
| (6.08%); **exactly 1.00: 58,964 (59.01%)**. A further 26,018 records (26.04%) sit at exactly 0.90. | |
| #### Who are the annotators? | |
| `ollama_chat/gpt-oss:20b` — **the same model, at the same version, that generated the | |
| conversations.** See Limitations. | |
| **No human calibration exists for this rubric.** `calibration/` contains only its README; there is | |
| no `coherence-v2.calibration.json` and no record of any reviewer having read any sample of this | |
| corpus. The judge behind every score in this dataset is uncalibrated. The repository documents | |
| this as an accepted deferral (checklist item CHK063), not as an oversight — but a consumer of the | |
| scores should treat them accordingly. | |
| #### Personal and Sensitive Information | |
| The dataset contains no real personal data by construction. Identifier-shaped values are drawn | |
| from ranges reserved for fiction — RFC 2606 domains (`example.com`, `.test`, `.invalid`), NANP | |
| `555-0100`–`555-0199` phone numbers, published payment-card test numbers — so they cannot refer to | |
| anyone. The domain prompt makes this the primary control and forbids bare runs of nine or more | |
| digits, so that invented order and serial numbers cannot collide with government identifier or | |
| phone number shapes. | |
| Every record was scanned before admission by the `datafog-regex` detector over `turns[].content` | |
| and `scenario`, covering `CREDIT_CARD`, `EMAIL`, `IP_ADDRESS`, `PHONE` and `US_SSN`. **761 records | |
| were blocked and discarded on this gate**, and zero records with a blocking finding reached the | |
| output. Nothing was quarantined. No privacy exception has ever been approved for this project; | |
| `privacy/exceptions.json` does not exist. | |
| The run report's per-record privacy counters are unusable: `records_examined` and | |
| `fields_examined` are both `0` and `findings` is empty, which contradicts the 761 recorded privacy | |
| discards. The gate demonstrably ran and blocked; the accounting of what it examined did not | |
| survive into the report. This card therefore cannot state how many fields were scanned or how many | |
| findings were exempt by reserved range. See Limitations. | |
| The scan does not detect: non-US government identifiers, postal codes, full postal addresses, IBAN | |
| bank account numbers, or person names. These are declared gaps, restated here so a clean scan is | |
| not mistaken for coverage it does not provide. Person names in particular are present throughout | |
| and are fabricated. | |
| ## Bias, Risks, and Limitations | |
| **The judge shares a model with the generator.** `ollama_chat/gpt-oss:20b` wrote and scored every | |
| one of the 99,930 conversations. A model evaluating its own output is a known source of inflated | |
| agreement, and the score distribution is consistent with that. | |
| **The judge did not use the scale.** Rubric v2 exists precisely because v1 collapsed to three | |
| values, and it instructs the judge to reserve 1.0 for "no observable flaw of this kind". 59.01% of | |
| records scored exactly 1.00 and a further 26.04% exactly 0.90 — 85% of the corpus on two values, | |
| and only 211 distinct values across ~100k records. The rubric's own stated failure mode has | |
| recurred in weaker form. The scores separate the bottom few percent from everything else and | |
| should not be read as a quality ordering within the corpus. | |
| **Nobody has checked the judge.** There is no calibration record for `coherence-v2`. Every claim | |
| this corpus makes about quality rests on one uncalibrated model scoring its own output. This is | |
| the single largest gap in the dataset's provenance. | |
| **The provenance record has holes.** Beyond the dirty code revision, several manifest and report | |
| fields are empty or wrong: `coherence_score_distribution` is `{"_count": 0}`; | |
| `privacy.records_examined` and `fields_examined` are `0` with an empty `findings` list despite 761 | |
| privacy discards; `composition_drift_pp` reports each member's drift as the negative of its | |
| requested share; `budget.spent_model_calls` is `0` and `budget.spent_seconds` is `2.071` for a run | |
| that made on the order of 200,000 model calls; and the single recorded segment spans 4.6 seconds | |
| with `first_record_index` (100000) greater than `last_record_index` (99929). The corpus itself is | |
| internally consistent and its hash verifies, and the discard counts reconcile exactly with | |
| generated-minus-written — but a reader should not trust the manifest's timing, budget, coherence | |
| or privacy-volume counters, and every number in this card that could be recomputed from the | |
| records was recomputed rather than quoted. | |
| **Scenario repetition.** 98,239 distinct `scenario` strings cover 99,930 records: 1,002 scenario | |
| strings are used more than once, affecting 2,693 records (2.7%). The repeats cluster on a few | |
| attractors — "Customer received a Bluetooth speaker instead of the ordered noise-cancelling | |
| headphones" and variants appear dozens of times across `shipping-wrong-item-received`, and | |
| duplicate-charge-on-a-smart-speaker dominates `billing-duplicate-charge`. Exact-duplicate | |
| detection found zero duplicate *records*, so the conversations differ; the situations behind them | |
| are narrower than the scenario count suggests. | |
| **Single domain, single model, single prompt, single seed, single run.** One | |
| consumer-electronics prompt, one 20B model, seed 42, one run. Phrasing, product names, escalation | |
| language and problem framing will be markedly less varied than real traffic, and stylistic tics of | |
| one model run through the entire corpus. The size of the corpus does not mitigate this: 99,930 | |
| records from one model are not 99,930 independent samples of anything. | |
| **Short conversations only.** 4–8 turns by configuration. Real support threads that reopen, span | |
| channels, or involve a second agent have no analogue here. | |
| **Composition is prescribed, not observed.** Every distribution in this card was requested. That | |
| 70% of tickets are `resolved` and 10% are `urgent` says what the config asked for and nothing | |
| about consumer electronics support. | |
| **Timestamps are seeded, not causal.** `created_at` and `resolved_at` are drawn from the run | |
| window and a duration range; they do not encode business hours, weekday effects, seasonality, or | |
| any relationship between priority and resolution latency. Do not use this corpus for queueing or | |
| SLA modelling. | |
| **Fabricated products and companies.** Product names appearing in conversations are invented. Any | |
| resemblance to real products is incidental and not an endorsement or a description. | |
| **Privacy coverage is narrower than "scanned" implies.** Five entity types, two fields, one | |
| regex-based detector. Person names, addresses, postal codes, IBANs and non-US identifiers are | |
| outside it by declaration. | |
| ### Recommendations | |
| Users should be made aware of the risks, biases and limitations of the dataset. Specifically: | |
| - **Do not filter or rank on `coherence_score`.** Everything in the corpus already passed 0.8, and | |
| 85% of it sits on two values from an uncalibrated judge scoring its own work. If you need a | |
| quality signal, score the conversations yourself with a different model, or draw a sample and | |
| read it. | |
| - **Do not treat any distribution as descriptive.** Composition, turn counts, subdomain balance | |
| and timestamps were all specified before generation. Use them as controlled variables, which is | |
| what they are good for, not as observations. | |
| - **Deduplicate on `scenario` if scenario diversity matters** to your task — dropping to one | |
| record per scenario string costs 1,691 records and removes the attractor clusters. | |
| - **Do not index on `record_index`.** It has 70 gaps. | |
| - **Do not rely on the privacy scan for entity types it declares out of scope.** If your use | |
| requires name- or address-level assurance, run your own detector before use. | |
| - **Expect one model's voice.** For anything where stylistic diversity matters, regenerate from | |
| the repository across several models, prompts and seeds and pool the results rather than | |
| resampling this corpus. | |
| - **Do not attempt textual reproduction.** No sampling seed was set and the tree was dirty; rerun | |
| from the manifest to reproduce the *structure*, and expect different conversations. | |
| ## Citation [optional] | |
| **BibTeX:** | |
| ```bibtex | |
| @misc{bates_synthetic_support_tickets_2026, | |
| author = {Bates, Robert}, | |
| title = {Synthetic Multi-Turn Customer Support Tickets (Consumer Electronics)}, | |
| year = {2026}, | |
| version = {1.0.0}, | |
| howpublished = {\url{https://github.com/arpieb/ticket-dataset-generator}} | |
| } | |
| ``` | |
| **APA:** | |
| Bates, R. (2026). *Synthetic Multi-Turn Customer Support Tickets (Consumer Electronics)* | |
| (Version 1.0.0) [Data set]. https://github.com/arpieb/ticket-dataset-generator | |
| ## Glossary [optional] | |
| - **Slot** — one unit of generation work. Its metadata is assigned before any model call, which is | |
| what makes the composition exact and lets a discarded record be retried without changing the | |
| corpus shape. | |
| - **Subdomain** — a category declared by the domain prompt document (e.g. | |
| `billing-invoice-discrepancy`). Chosen by seed before dispatch; the model elaborates a specific | |
| scenario within it. | |
| - **Scenario** — the specific situation the model invented within its assigned subdomain, stored | |
| on the record as free text. Not drawn from a fixed list, which is why it repeats only rarely. | |
| - **Exempt by range** — a privacy finding whose value comes from a range a standard reserves for | |
| fiction. Reported rather than hidden, so the scan never looks cleaner than it was. | |
| - **Structural reproduction** — same seeded choices at every position, different conversation text. | |
| What this pipeline guarantees when no model sampling seed is set. | |
| - **Blocking finding** — a privacy detection that is not exempt by reserved range. It discards the | |
| record; it never appears in the corpus. | |
| ## More Information [optional] | |
| The manifest (`8c56469b-f094-4686-90dc-29715bab4228.manifest.json`) and run report | |
| (`…report.json`) for this corpus are the authoritative provenance record: they carry the full | |
| resolved configuration, input hashes, code revision, and the complete filter accounting — subject | |
| to the counter defects noted in Limitations. | |
| `ticket-dataset-generator config-from-manifest` recovers the exact configuration from the manifest, and | |
| `ticket-dataset-generator generate --from-manifest` reproduces the run, refusing if any recorded input has | |
| since changed. Given the dirty code revision, a `--from-manifest` rerun reproduces the structure | |
| under whatever code is checked out, not under the code that produced this corpus. | |
| ## Dataset Card Authors [optional] | |
| Robert Bates (arpieb), with drafting assistance from Claude Code. Every figure was read from the | |
| run manifest, the run report, the domain prompt, the rubric, or recomputed from the corpus itself. | |
| ## Dataset Card Contact | |
| arpieb via the GitHub repository above. |