---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MRR-NER-08-09-Layoutlmv3
  results: []
---

<!-- 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. -->

# MRR-NER-08-09-Layoutlmv3

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0175
- Precision: 0.8367
- Recall: 0.9111
- F1: 0.8723
- Accuracy: 0.9960

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 8.33  | 100  | 0.2585          | 0.1667    | 0.0222 | 0.0392 | 0.9607   |
| No log        | 16.67 | 200  | 0.1281          | 0.4783    | 0.2444 | 0.3235 | 0.9727   |
| No log        | 25.0  | 300  | 0.0821          | 0.3696    | 0.3778 | 0.3736 | 0.9767   |
| No log        | 33.33 | 400  | 0.0493          | 0.5111    | 0.5111 | 0.5111 | 0.9813   |
| 0.2244        | 41.67 | 500  | 0.0330          | 0.625     | 0.7778 | 0.6931 | 0.9913   |
| 0.2244        | 50.0  | 600  | 0.0272          | 0.6909    | 0.8444 | 0.7600 | 0.9927   |
| 0.2244        | 58.33 | 700  | 0.0218          | 0.7843    | 0.8889 | 0.8333 | 0.9953   |
| 0.2244        | 66.67 | 800  | 0.0190          | 0.7547    | 0.8889 | 0.8163 | 0.9947   |
| 0.2244        | 75.0  | 900  | 0.0158          | 0.8936    | 0.9333 | 0.9130 | 0.9973   |
| 0.038         | 83.33 | 1000 | 0.0175          | 0.8367    | 0.9111 | 0.8723 | 0.9960   |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3