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LiquidAI/LFM2.5-Encoder-350M-Spellchecker

sourceHugging Faceotherupdated 2mo agoView on Hugging Face

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LFM2.5-Encoder-350-Spellchecker

A full fine-tune of LFM2.5-Encoder-350M with a subword-level GECToR-style grammatical-error-correction tagger. It covers grammar, spelling, punctuation, and casing in English.

Find more details about our encoders in our blog post.

[!NOTE] 💻 Demos: Try this fine-tuned model running in a CPU-only Hugging Face space: [Spell checking](https://huggingface.co/spaces/LiquidAI/spellchecker) — correct misspellings token by token.

Usage

⚠️ Loads custom code via trust_remote_code=True (the model wraps a trust_remote_code encoder).

Install the required packages:

bash

pip install torch transformers

Run spell checking:

python

from transformers import AutoModel

model_id = "LiquidAI/LFM2.5-Encoder-350-Spellchecker"

model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
).float().eval()

print(model.correct(["She go to school every day ."]))
# ['She goes to school every day .']

correct() accepts a string or a list; tune precision with min_error_prob (higher → fewer edits) and max_iter (refinement passes). Input should be whitespace-tokenized (punctuation separated by spaces), matching the training data.

Evaluation

Fixed inference setting: max_iter=4, precision knobs off. Headline ERRANT F0.5:

MASTER composite (selection metric): 64.24

Benchmark	Precision	Recall	F0.5
LOCNESS native (ERRANT)	53.77	34.53	48.38
BEA-dev (ERRANT)	56.48	28.96	47.46
CoNLL-14 (ERRANT)	67.54	18.91	44.59
FCE-test (ERRANT)	57.31	32.63	49.78
Robustness (ERRANT)	91.96	87.98	91.14
Multilingual dev (F0.5)	—	—	—

Examples

Input	Correction
`She go to school every day .`	`She goes to school every day .`
`I has went to the stor yesterday .`	`I went to the store yesterday .`
`Their are many reason to study hard .`	`There are many reasons to study hard .`
`He don't like coffee but he like tea .`	`He does n't like coffee , but he likes tea .`

📬 Contact

—Got questions or want to connect? Join our Discord community
—If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

bibtex

@article{liquidAI2026Encoders,
  author = {Liquid AI},
  title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2-5-encoders},
}