|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- nielsr/funsd-layoutlmv3 |
|
pipeline_tag: object-detection |
|
widget: |
|
- src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" |
|
example_title: invoice |
|
- src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg" |
|
example_title: contract |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: layoutlmv3-finetuned-funsd |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: nielsr/funsd-layoutlmv3 |
|
type: nielsr/funsd-layoutlmv3 |
|
args: funsd |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9026198714780029 |
|
- name: Recall |
|
type: recall |
|
value: 0.913 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9077802634849614 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8330271015158475 |
|
duplicated_from: nielsr/layoutlmv3-finetuned-funsd |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# layoutlmv3-finetuned-funsd |
|
|
|
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.1164 |
|
- Precision: 0.9026 |
|
- Recall: 0.913 |
|
- F1: 0.9078 |
|
- Accuracy: 0.8330 |
|
|
|
The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3 |
|
|
|
## 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: 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: 1000 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 10.0 | 100 | 0.5238 | 0.8366 | 0.886 | 0.8606 | 0.8410 | |
|
| No log | 20.0 | 200 | 0.6930 | 0.8751 | 0.8965 | 0.8857 | 0.8322 | |
|
| No log | 30.0 | 300 | 0.7784 | 0.8902 | 0.908 | 0.8990 | 0.8414 | |
|
| No log | 40.0 | 400 | 0.9056 | 0.8916 | 0.905 | 0.8983 | 0.8364 | |
|
| 0.2429 | 50.0 | 500 | 1.0016 | 0.8954 | 0.9075 | 0.9014 | 0.8298 | |
|
| 0.2429 | 60.0 | 600 | 1.0097 | 0.8899 | 0.897 | 0.8934 | 0.8294 | |
|
| 0.2429 | 70.0 | 700 | 1.0722 | 0.9035 | 0.9085 | 0.9060 | 0.8315 | |
|
| 0.2429 | 80.0 | 800 | 1.0884 | 0.8905 | 0.9105 | 0.9004 | 0.8269 | |
|
| 0.2429 | 90.0 | 900 | 1.1292 | 0.8938 | 0.909 | 0.9013 | 0.8279 | |
|
| 0.0098 | 100.0 | 1000 | 1.1164 | 0.9026 | 0.913 | 0.9078 | 0.8330 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.19.0.dev0 |
|
- Pytorch 1.11.0+cu113 |
|
- Datasets 2.0.0 |
|
- Tokenizers 0.11.6 |
|
|
