---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
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: test
      args: cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9428783382789317
    - name: Recall
      type: recall
      value: 0.9513473053892215
    - name: F1
      type: f1
      value: 0.9470938897168405
    - name: Accuracy
      type: accuracy
      value: 0.952037351443124
---

<!-- 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: 0.2267
- Precision: 0.9429
- Recall: 0.9513
- F1: 0.9471
- Accuracy: 0.9520

## 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  | 1.0513          | 0.6817    | 0.7597 | 0.7186 | 0.7806   |
| 1.4257        | 3.12  | 500  | 0.5744          | 0.8451    | 0.8660 | 0.8555 | 0.8697   |
| 1.4257        | 4.69  | 750  | 0.3979          | 0.8720    | 0.9027 | 0.8871 | 0.9062   |
| 0.4063        | 6.25  | 1000 | 0.3350          | 0.9107    | 0.9237 | 0.9171 | 0.9300   |
| 0.4063        | 7.81  | 1250 | 0.2638          | 0.9313    | 0.9431 | 0.9372 | 0.9402   |
| 0.2045        | 9.38  | 1500 | 0.2542          | 0.9205    | 0.9364 | 0.9284 | 0.9419   |
| 0.2045        | 10.94 | 1750 | 0.2417          | 0.9335    | 0.9454 | 0.9394 | 0.9469   |
| 0.1406        | 12.5  | 2000 | 0.2279          | 0.9371    | 0.9476 | 0.9423 | 0.9491   |
| 0.1406        | 14.06 | 2250 | 0.2267          | 0.9401    | 0.9513 | 0.9457 | 0.9550   |
| 0.1079        | 15.62 | 2500 | 0.2267          | 0.9429    | 0.9513 | 0.9471 | 0.9520   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0