This is a derivative work. All credit for the original model goes to Vennify (Eric Fillion). This repository only provides a quantized ONNX conversion for easier deployment. We do not claim ownership of the model architecture or weights.

Original Model


Original Repository	vennify/t5-base-grammar-correction
Original Author	Vennify (Eric Fillion)
Architecture	T5-base (encoder-decoder)
Parameters	222.9M
Training Data	JFLEG
License	CC BY-NC-SA 4.0 (inherited from original — non-commercial use only)

What’s in this repo?

encoder_model.onnx — Encoder graph (INT8 quantized)
decoder_model.onnx — Decoder graph (INT8 quantized)
decoder_with_past_model.onnx — Decoder with KV-cache (INT8 quantized)
config.json, generation_config.json — Model configuration
tokenizer.json, tokenizer_config.json, special_tokens_map.json — Tokenizer files

Quantization Details

Step	Detail
Export	PyTorch → ONNX FP32 via HuggingFace Optimum (`ORTModelForSeq2SeqLM`)
Quantization	`onnxruntime.quantization.quantize_dynamic` with `QuantType.QInt8`
Validation	Verified ONNX FP32 output matches PyTorch FP32 exactly (0 delta) before quantizing

Benchmark Results — This Model

Benchmarked on a 50-sentence English grammar correction test set (2-thread CPU, Google Colab).

Quality

Format	BLEU	chrF++	Exact Match	Δ BLEU	Δ chrF++
PyTorch FP32 (baseline)	78.57	87.87	58%	—	—
ONNX INT8 (this repo)	77.24	86.74	56%	-1.33	-1.13

Speed & Size

Metric	PyTorch FP32	ONNX INT8 (this repo)
P50 Latency	539 ms	224 ms
P95 Latency	708 ms	459 ms
Disk Size	850 MB	426 MB
Throughput	—	17.21 sentences/sec

Quantization Stability

Metric	Value
Sentences with changed output (INT8 vs FP32)	6/50 sentences changed
Deployment Scorecard Verdict	GO

Highest baseline quality but large size (426 MB INT8). INT8 introduced some language-switching artifacts on edge cases.

Cross-Model Comparison (7 Models Benchmarked)

This model was benchmarked alongside 6 other grammar correction models. All models were evaluated on the same 50-sentence test set under identical conditions.

PyTorch FP32 Baseline (all models)

Model	Params	BLEU	chrF++	P50 (ms)	Size (MB)	Exact Match
prithivida	222.9M	76.88	86.40	602	850	58%
coedit-small	60.5M	77.42	86.84	226	231	60%
vennify-t5-base ←	222.9M	78.57	87.87	539	850	58%
aventiq-t5-small	60.5M	34.43	45.88	213	231	16%
visheratin-mini	31.2M	66.84	81.77	133	119	46%
visheratin-tiny	15.6M	66.08	82.69	85	59	48%
pszemraj-small	77.0M	57.11	77.13	363	294	28%

ONNX INT8 Quantized (all models)

Model	Params	BLEU (FP32)	BLEU (INT8)	chrF++ (INT8)	P50 (ms)	Disk (MB)	Exact Match	Scorecard
prithivida	222.9M	76.88	74.44	84.79	222	426	54%	NO-GO
coedit-small	60.5M	77.42	77.60	86.69	117	152	60%	GO
vennify-t5-base ← this model	222.9M	78.57	77.24	86.74	224	426	56%	GO
aventiq-t5-small	60.5M	34.43	40.66	51.95	120	152	22%	GO*
visheratin-mini	31.2M	66.84	66.65	82.67	78	93	46%	GO
visheratin-tiny	15.6M	66.08	64.51	82.28	60	55	44%	GO
pszemraj-small	77.0M	57.11	3.15	18.72	138	153	0%	NO-GO

Statistical Significance (Bootstrap, 1000 resamples)

Key cross-model comparisons (INT8 variants):

Model A	Model B	chrF++ A	chrF++ B	p-value	Significant?
coedit-small	vennify-t5-base	86.69	86.74	1.000	No (identical)
coedit-small	visheratin-mini	86.69	82.67	0.038	Yes
coedit-small	visheratin-tiny	86.69	82.28	0.010	Yes
coedit-small	prithivida	86.69	84.79	0.324	No
visheratin-mini	visheratin-tiny	82.67	82.28	0.824	No (identical)
prithivida	vennify-t5-base	84.79	86.74	0.324	No

Recommendation

Use Case	Recommended Model	Why
Best overall (desktop)	coedit-small INT8 (152 MB)	Highest INT8 quality (chrF++ 86.69), 117ms latency, only 1 regression
Smallest (mobile/edge)	visheratin-tiny INT8 (55 MB)	60ms latency, 55 MB disk, acceptable quality (chrF++ 82.28)
Highest baseline quality	vennify-t5-base INT8 (426 MB)	chrF++ 86.74, but 3x larger than coedit-small for no significant quality gain

Usage

python

from optimum.onnxruntime import ORTModelForSeq2SeqLM
from transformers import AutoTokenizer

model_id = "YOUR_USERNAME/vennify-t5-base-grammar-correction-onnx-int8"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = ORTModelForSeq2SeqLM.from_pretrained(model_id)

text = "grammar: She go to school yesterday"
inputs = tokenizer([text], return_tensors="pt", max_length=128, truncation=True)
outputs = model.generate(**inputs, max_new_tokens=128, num_beams=1, repetition_penalty=1.3)
corrected = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(corrected)

Input prefix: grammar:

Acknowledgments

All credit for the original model goes to Vennify (Eric Fillion). This repository only provides an ONNX INT8 quantized conversion to make the model easier to deploy in production environments (mobile, edge, browser).

Quantization and benchmarking performed as part of the Smart Desktop Keyboard Grammar Engine project.

Citation

If you use this model, please cite the original authors:

@misc{vennify_t5_base_onnx_int8,
  title = {vennify/t5-base-grammar-correction — ONNX INT8 Quantized},
  note = {Quantized version of vennify/t5-base-grammar-correction},
  url = {https://huggingface.co/vennify/t5-base-grammar-correction},
}

TonyRaju/vennify-t5-base-grammar-correction-onnx-int8

vennify/t5-base-grammar-correction — ONNX INT8 Quantized