---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-cord-ner
  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. -->

# layoutlmv3-cord-ner

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: 0.1215
- Precision: 0.9448
- Recall: 0.9520
- F1: 0.9484
- Accuracy: 0.9762

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 113  | 0.1771          | 0.8485    | 0.8925 | 0.8700 | 0.9393   |
| No log        | 2.0   | 226  | 0.1584          | 0.8915    | 0.9146 | 0.9029 | 0.9524   |
| No log        | 3.0   | 339  | 0.1153          | 0.9160    | 0.9309 | 0.9234 | 0.9686   |
| No log        | 4.0   | 452  | 0.1477          | 0.9110    | 0.9136 | 0.9123 | 0.9592   |
| 0.1562        | 5.0   | 565  | 0.0861          | 0.9363    | 0.9443 | 0.9403 | 0.9741   |
| 0.1562        | 6.0   | 678  | 0.1165          | 0.9109    | 0.9415 | 0.9259 | 0.9673   |
| 0.1562        | 7.0   | 791  | 0.1280          | 0.9278    | 0.9367 | 0.9322 | 0.9707   |
| 0.1562        | 8.0   | 904  | 0.1122          | 0.9462    | 0.9453 | 0.9458 | 0.9762   |
| 0.0224        | 9.0   | 1017 | 0.1265          | 0.9431    | 0.9539 | 0.9485 | 0.9771   |
| 0.0224        | 10.0  | 1130 | 0.1215          | 0.9448    | 0.9520 | 0.9484 | 0.9762   |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1