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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: train
args: cord
metrics:
- name: Precision
type: precision
value: 0.9387001477104875
- name: Recall
type: recall
value: 0.9513473053892215
- name: F1
type: f1
value: 0.9449814126394053
- name: Accuracy
type: accuracy
value: 0.9567062818336163
---
<!-- 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-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2137
- Precision: 0.9387
- Recall: 0.9513
- F1: 0.9450
- Accuracy: 0.9567
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 1.0609 | 0.6596 | 0.7440 | 0.6993 | 0.7687 |
| 1.4193 | 3.12 | 500 | 0.5989 | 0.8403 | 0.8623 | 0.8511 | 0.8663 |
| 1.4193 | 4.69 | 750 | 0.4037 | 0.8795 | 0.9012 | 0.8902 | 0.9087 |
| 0.4182 | 6.25 | 1000 | 0.3264 | 0.8980 | 0.9162 | 0.9070 | 0.9257 |
| 0.4182 | 7.81 | 1250 | 0.2705 | 0.9190 | 0.9341 | 0.9265 | 0.9410 |
| 0.2258 | 9.38 | 1500 | 0.2450 | 0.9311 | 0.9401 | 0.9356 | 0.9461 |
| 0.2258 | 10.94 | 1750 | 0.2350 | 0.9341 | 0.9439 | 0.9389 | 0.9491 |
| 0.1576 | 12.5 | 2000 | 0.2219 | 0.9350 | 0.9476 | 0.9413 | 0.9508 |
| 0.1576 | 14.06 | 2250 | 0.2122 | 0.9373 | 0.9506 | 0.9439 | 0.9559 |
| 0.1207 | 15.62 | 2500 | 0.2137 | 0.9387 | 0.9513 | 0.9450 | 0.9567 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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