README.md

metadata

license: mit
language: en
library_name: transformers
pipeline_tag: translation
base_model: google/t5-efficient-tiny
tags:
  - cron
  - scheduling
  - t5
  - transformers.js
  - onnx
  - cronlm
  - text2text-generation

cronlm

Tiny natural-language to cron model. Pluggable into @huggingface/transformers.

• Base: google/t5-efficient-tiny (~16M params)
• Shipped: INT8 ONNX (encoder + decoder + decoder-with-past), ~36 MB total
• Accuracy: 99.2% on 4 000 fresh synthetic samples · 97.3% on a hand-crafted adversarial set
• Validity: 100% of outputs are parseable 5-field cron
• Latency: ~3 ms / item on a laptop CPU

Usage

Browser / Node — transformers.js

import { pipeline } from "@huggingface/transformers";
const cron = await pipeline("text2text-generation", "pavstev/cronlm");
const out = await cron("every weekday at 9am".toLowerCase());
console.log(out[0].generated_text); // "0 9 * * 1-5"

Lowercase the input before passing — bumps accuracy on Title-Case inputs by ~2.5 pp.

Python — transformers

from transformers import pipeline
cron = pipeline("text2text-generation", model="pavstev/cronlm")
print(cron("every weekday at 9am")[0]["generated_text"])  # "0 9 * * 1-5"

Examples

Input	Output
every 5 minutes	*/5 * * * *
every weekday at 9am	0 9 * * 1-5
at 10:30 PM on Sundays	30 22 * * 0
every 15 minutes between 9 and 17	*/15 9-17 * * *
every Mon and Wed at noon	0 12 * * 1,3
on the 1st of every month	0 0 1 * *
every quarter	0 0 1 1,4,7,10 *
at midnight	0 0 * * *
@daily	0 0 * * *
MWF at 9am	0 9 * * 1,3,5
every December 25	0 0 25 12 *
from the 1st to the 5th at noon	0 12 1-5 * *
every 30 minutes during business hours	*/30 9-17 * * 1-5

Validation

A pure-Python validator/fixer is included in the cronlm package — wrap model output to catch any rare invalid generation:

from cronlm import fix
fix("0 25 * * 1")  # "0 23 * * 1"

Training

Source, dataset generator, and training scripts: https://github.com/pavstev/cronlm

License

MIT