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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](https://github.com/huggingface/transformers.js).
- 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
```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
```py
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`](https://github.com/pavstev/cronlm) package — wrap model output to catch any rare invalid generation:
```py
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
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