onnx-community/layoutlmv3-base-cord-ONNX
By onnx-community · ↓ 2 · ♥ 0 · Updated 2025-11-17
What's in this repo
This page lists the files in onnx-community/layoutlmv3-base-cord-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 2025-11-17
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-base-cord (ONNX)
This is an ONNX version of mp-02/layoutlmv3-base-cord. 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-base-cord
This model is a fine-tuned version of microsoft/layoutlmv3-base on the mp-02/cord dataset. It achieves the following results on the evaluation set:
- Loss: 0.1517
- Precision: 0.9752
- Recall: 0.9785
- F1: 0.9768
- Accuracy: 0.9739
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 2.0 | 100 | 0.8667 | 0.7592 | 0.8202 | 0.7885 | 0.8097 |
| No log | 4.0 | 200 | 0.3443 | 0.9122 | 0.9387 | 0.9253 | 0.9222 |
| No log | 6.0 | 300 | 0.2128 | 0.9345 | 0.9569 | 0.9456 | 0.9579 |
| No log | 8.0 | 400 | 0.1745 | 0.9440 | 0.9635 | 0.9537 | 0.9629 |
| 0.6362 | 10.0 | 500 | 0.1594 | 0.9559 | 0.9702 | 0.9630 | 0.9684 |
| 0.6362 | 12.0 | 600 | 0.1720 | 0.9630 | 0.9693 | 0.9661 | 0.9629 |
| 0.6362 | 14.0 | 700 | 0.1528 | 0.9607 | 0.9710 | 0.9658 | 0.9675 |
| 0.6362 | 16.0 | 800 | 0.1460 | 0.9638 | 0.9718 | 0.9678 | 0.9680 |
| 0.6362 | 18.0 | 900 | 0.1609 | 0.9614 | 0.9702 | 0.9658 | 0.9648 |
| 0.0536 | 20.0 | 1000 | 0.1517 | 0.9752 | 0.9785 | 0.9768 | 0.9739 |
| 0.0536 | 22.0 | 1100 | 0.1901 | 0.9614 | 0.9693 | 0.9653 | 0.9657 |
| 0.0536 | 24.0 | 1200 | 0.1867 | 0.9638 | 0.9718 | 0.9678 | 0.9666 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
Model info
Created: 2025-11-17
Updated: 2025-11-17
View on Hugging Face