---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv2-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. -->

# layoutlmv2-cord-ner

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Precision: 0.9639
- Recall: 0.9741
- F1: 0.9690
- Accuracy: 0.9911

## 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  | 0.5962          | 0.8714    | 0.8973 | 0.8842 | 0.9405   |
| No log        | 2.0   | 226  | 0.4064          | 0.8713    | 0.9098 | 0.8901 | 0.9511   |
| No log        | 3.0   | 339  | 0.2687          | 0.9314    | 0.9386 | 0.9350 | 0.9754   |
| No log        | 4.0   | 452  | 0.2007          | 0.9355    | 0.9472 | 0.9413 | 0.9792   |
| 0.4677        | 5.0   | 565  | 0.1625          | 0.9497    | 0.9597 | 0.9547 | 0.9834   |
| 0.4677        | 6.0   | 678  | 0.1326          | 0.9526    | 0.9645 | 0.9585 | 0.9868   |
| 0.4677        | 7.0   | 791  | 0.1212          | 0.9508    | 0.9645 | 0.9576 | 0.9851   |
| 0.4677        | 8.0   | 904  | 0.1019          | 0.9675    | 0.9712 | 0.9693 | 0.9911   |
| 0.1131        | 9.0   | 1017 | 0.1029          | 0.9545    | 0.9664 | 0.9604 | 0.9881   |
| 0.1131        | 10.0  | 1130 | 0.0952          | 0.9639    | 0.9741 | 0.9690 | 0.9911   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.9.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6