README.md

---
license: apache-2.0
task_categories:
  - token-classification
language:
  - en
tags:
  - eye-grep
  - logs
  - log-analysis
  - synthetic
pretty_name: eye-grep log token-classification gold set
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: gold_train.jsonl
      - split: validation
        path: gold.jsonl
---

# eye-grep — log token-classification gold set

Token-level labels for **eye-grep**, a log colorizer that tags each content token of a
server-log line so a renderer can highlight ids, timestamps, IPs and repeated strings.
These are the sets used to train and evaluate the eye-grep taggers
([opsbr/eye-grep-deberta-v3-small](https://huggingface.co/opsbr/eye-grep-deberta-v3-small)
and the distilled [opsbr/eye-grep-electra-small](https://huggingface.co/opsbr/eye-grep-electra-small)).

**Fully synthetic and self-contained** — generated by a deterministic template engine
(`loop/synth_gold.py`), with **no third-party log data**. Every value is produced by a
typed slot generator, so the gold labels are exact by construction (no LLM labeling, no
alignment error).

## Splits

| split | rows | structures |
|---|---|---|
| `train` (`gold_train.jsonl`) | 2,943 | 93 templates across 58 system families |
| `validation` (`gold.jsonl`) | 146 | 18 **held-out** templates (disjoint structures → tests generalization) |

System families span web servers (Nginx, Apache, HAProxy), databases (PostgreSQL,
MySQL, Redis, MongoDB, Cassandra, ClickHouse), messaging (Kafka, RabbitMQ, NATS),
container/orchestration (Kubernetes, Docker, Envoy, Istio, Traefik), big-data
(Spark, Hadoop, HDFS, Flink), cloud (Lambda, CloudTrail), OS/syslog (Linux, Windows,
macOS, firewall, DNS, mail), and application logs (JSON, logfmt, Java, Go, Python,
Rails) — in formats from syslog and Apache-combined to JSON and key=value.

## Format

JSON Lines — one object per line:

```json
{"system": "Service", "line": "2026-03-16T22:58:29 INFO order-service request_id=tok_9fKd ...",
 "content_tokens": ["2026-03-16T22:58:29", "INFO", "order-service", ...],
 "gold": ["TIMESTAMP", "LEVEL", "WORD", ...]}
```

- `system` — synthetic source family (for diversity / the held-out split).
- `line` — the generated log line.
- `content_tokens` — content tokens of the line (separators dropped), per eye-grep's
  frozen tokenizer (`train/spec.py`).
- `gold` — one tag per content token, index-aligned to `content_tokens`.

## Label schema (11 tags)

`PUNCT  WORD  NUM  RAND  IP  DURATION  SIZE  TIMESTAMP  LEVEL  URL  PATH`

`RAND` = a high-entropy id (uuid / hash / token); `NUM`/`SIZE`/`DURATION` are numeric
values; `PUNCT` is reserved for separators (so content-token labels draw from the
other ten). `categories.json` carries the per-category evaluation weights and a note
on the minimal-set schema.

## How it was built

A template is literal text plus typed slots (`{TS}`, `{LEVEL}`, `{IP}`, `{RAND}`,
`{NUM}`, `{DURATION}`, `{SIZE}`, `{URL}`, `{PATH}`, `{WORD}`). The generator fills each
slot from a per-type value generator, **records the value's character span**, then
tokenizes the line and gives each content token the tag of the slot covering its start
char — so a multi-token value like `Jun 14 10:00:00` is entirely `TIMESTAMP`. Seeded and
reproducible (`python loop/synth_gold.py`). Validation draws from a disjoint template
set, so it measures generalization to unseen log structures.

## License

Apache-2.0. The data is generated by OpsBR's own template engine and contains no
third-party log content — see `LICENSE` / `NOTICE`.

## Usage

```python
from datasets import load_dataset
ds = load_dataset("opsbr/eye-grep")     # -> {"train": 2943, "validation": 146}
print(ds["validation"][0]["line"], ds["validation"][0]["gold"])
```