ONNX Model Zoo

onnx-community/layoutlmv3-finetuned-sroie-ONNX

By onnx-community · ↓ 0 · ♥ 0 · Updated 2026-01-20

What's in this repo

This page lists the files in onnx-community/layoutlmv3-finetuned-sroie-ONNX, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.

  • 8 ONNX models (.onnx files)
  • 18 files, including 8 .onnx, 7 .json, 1 .md, 1 .txt
  • Last updated 2026-01-20

ONNX files

File Size Download
onnx/model.onnx — Download
onnx/model_bnb4.onnx — Download
onnx/model_fp16.onnx — Download
onnx/model_int8.onnx — Download
onnx/model_q4.onnx — Download
onnx/model_q4f16.onnx — Download
onnx/model_quantized.onnx — Download
onnx/model_uint8.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

layoutlmv3-finetuned-sroie (ONNX)

This is an ONNX version of Theivaprakasham/layoutlmv3-finetuned-sroie. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for token-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TokenClassificationPipeline

layoutlmv3-finetuned-sroie

This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0426
  • Precision: 0.9371
  • Recall: 0.9438
  • F1: 0.9404
  • Accuracy: 0.9945

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 5000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 0.32 100 0.1127 0.6466 0.6102 0.6279 0.9729
No log 0.64 200 0.0663 0.8215 0.7428 0.7802 0.9821
No log 0.96 300 0.0563 0.8051 0.8718 0.8371 0.9855
No log 1.28 400 0.0470 0.8766 0.8595 0.8680 0.9895
0.1328 1.6 500 0.0419 0.8613 0.9128 0.8863 0.9906
0.1328 1.92 600 0.0338 0.8888 0.9099 0.8993 0.9926
0.1328 2.24 700 0.0320 0.8690 0.9467 0.9062 0.9929
0.1328 2.56 800 0.0348 0.8960 0.9438 0.9193 0.9931
0.1328 2.88 900 0.0300 0.9169 0.9460 0.9312 0.9942
0.029 3.19 1000 0.0281 0.9080 0.9452 0.9262 0.9942
0.029 3.51 1100 0.0259 0.9174 0.9438 0.9304 0.9945
0.029 3.83 1200 0.0309 0.9207 0.9532 0.9366 0.9944
0.029 4.15 1300 0.0366 0.9195 0.9388 0.9291 0.9940
0.029 4.47 1400 0.0302 0.9343 0.9424 0.9383 0.9949
0.0174 4.79 1500 0.0349 0.9142 0.9517 0.9326 0.9939
0.0174 5.11 1600 0.0327 0.9322 0.9510 0.9415 0.9950
0.0174 5.43 1700 0.0317 0.9215 0.9561 0.9385 0.9938
0.0174 5.75 1800 0.0385 0.9282 0.9316 0.9299 0.9940
0.0174 6.07 1900 0.0342 0.9235 0.9481 0.9357 0.9944
0.0117 6.39 2000 0.0344 0.9287 0.9474 0.9379 0.9944
0.0117 6.71 2100 0.0388 0.9232 0.9445 0.9338 0.9941
0.0117 7.03 2200 0.0325 0.9269 0.9496 0.9381 0.9949
0.0117 7.35 2300 0.0343 0.9225 0.9438 0.9330 0.9941
0.0117 7.67 2400 0.0372 0.9216 0.9481 0.9347 0.9944
0.0081 7.99 2500 0.0385 0.9192 0.9589 0.9386 0.9944
0.0081 8.31 2600 0.0376 0.9293 0.9467 0.9379 0.9944
0.0081 8.63 2700 0.0425 0.9261 0.9474 0.9366 0.9941
0.0081 8.95 2800 0.0407 0.9266 0.9452 0.9358 0.9941
0.0081 9.27 2900 0.0403 0.9280 0.9467 0.9372 0.9941
0.0055 9.58 3000 0.0364 0.9287 0.9474 0.9379 0.9948
0.0055 9.9 3100 0.0427 0.9122 0.9510 0.9312 0.9941
0.0055 10.22 3200 0.0394 0.9223 0.9488 0.9354 0.9943
0.0055 10.54 3300 0.0393 0.9247 0.9561 0.9401 0.9945
0.0055 10.86 3400 0.0413 0.9334 0.9496 0.9414 0.9945
0.0049 11.18 3500 0.0400 0.9290 0.9517 0.9402 0.9945
0.0049 11.5 3600 0.0412 0.9317 0.9539 0.9427 0.9945
0.0049 11.82 3700 0.0419 0.9314 0.9481 0.9397 0.9947
0.0049 12.14 3800 0.0452 0.9243 0.9503 0.9371 0.9941
0.0049 12.46 3900 0.0412 0.9334 0.9496 0.9414 0.9947
0.0039 12.78 4000 0.0438 0.9294 0.9481 0.9387 0.9941
0.0039 13.1 4100 0.0416 0.9326 0.9467 0.9396 0.9944
0.0039 13.42 4200 0.0418 0.9327 0.9488 0.9407 0.9948
0.0039 13.74 4300 0.0423 0.9345 0.9460 0.9402 0.9946
0.0039 14.06 4400 0.0419 0.9286 0.9467 0.9376 0.9947
0.0022 14.38 4500 0.0426 0.9371 0.9438 0.9404 0.9945
0.0022 14.7 4600 0.0424 0.9371 0.9445 0.9408 0.9947
0.0022 15.02 4700 0.0427 0.9372 0.9467 0.9419 0.9947
0.0022 15.34 4800 0.0431 0.9339 0.9460 0.9399 0.9945
0.0022 15.65 4900 0.0431 0.9346 0.9467 0.9406 0.9946
0.0015 15.97 5000 0.0434 0.9324 0.9445 0.9384 0.9945

Framework versions

  • Transformers 4.20.0.dev0
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1

Model info

token-classification transformers.js
transformers.js onnx layoutlmv3 token-classification generated_from_trainer dataset:sroie base_model:Theivaprakasham/layoutlmv3-finetuned-sroie base_model:quantized:Theivaprakasham/layoutlmv3-finetuned-sroie model-index region:us
↓ 0 ♥ 0

Created: 2026-01-20

Updated: 2026-01-20

View on Hugging Face

Files (18)

.gitattributes
README.md
config.json
merges.txt
onnx/model.onnx ONNX
onnx/model_bnb4.onnx ONNX
onnx/model_fp16.onnx ONNX
onnx/model_int8.onnx ONNX
onnx/model_q4.onnx ONNX
onnx/model_q4f16.onnx ONNX
onnx/model_quantized.onnx ONNX
onnx/model_uint8.onnx ONNX
preprocessor_config.json
quantize_config.json
special_tokens_map.json
tokenizer.json
tokenizer_config.json
vocab.json

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