---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv1-er-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. -->

# layoutlmv1-er-ner

This model is a fine-tuned version of [renjithks/layoutlmv1-cord-ner](https://huggingface.co/renjithks/layoutlmv1-cord-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2936
- Precision: 0.6097
- Recall: 0.6192
- F1: 0.6144
- Accuracy: 0.9479

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 22   | 0.3856          | 0.3047    | 0.1453 | 0.1968 | 0.8885   |
| No log        | 2.0   | 44   | 0.2637          | 0.3725    | 0.3625 | 0.3674 | 0.9197   |
| No log        | 3.0   | 66   | 0.2184          | 0.5117    | 0.4612 | 0.4852 | 0.9361   |
| No log        | 4.0   | 88   | 0.2321          | 0.4714    | 0.5585 | 0.5113 | 0.9361   |
| No log        | 5.0   | 110  | 0.2183          | 0.5453    | 0.5853 | 0.5646 | 0.9440   |
| No log        | 6.0   | 132  | 0.2243          | 0.5977    | 0.5867 | 0.5922 | 0.9459   |
| No log        | 7.0   | 154  | 0.2451          | 0.5716    | 0.5910 | 0.5811 | 0.9410   |
| No log        | 8.0   | 176  | 0.2387          | 0.5881    | 0.5839 | 0.5860 | 0.9474   |
| No log        | 9.0   | 198  | 0.2702          | 0.5794    | 0.6023 | 0.5906 | 0.9430   |
| No log        | 10.0  | 220  | 0.2450          | 0.5920    | 0.6079 | 0.5999 | 0.9480   |
| No log        | 11.0  | 242  | 0.2697          | 0.6151    | 0.5994 | 0.6071 | 0.9467   |
| No log        | 12.0  | 264  | 0.2607          | 0.6022    | 0.6234 | 0.6126 | 0.9497   |
| No log        | 13.0  | 286  | 0.2737          | 0.6172    | 0.6276 | 0.6224 | 0.9488   |
| No log        | 14.0  | 308  | 0.2840          | 0.6117    | 0.6333 | 0.6223 | 0.9474   |
| No log        | 15.0  | 330  | 0.2833          | 0.6030    | 0.6192 | 0.6110 | 0.9476   |
| No log        | 16.0  | 352  | 0.3009          | 0.6161    | 0.6135 | 0.6148 | 0.9449   |
| No log        | 17.0  | 374  | 0.2920          | 0.6098    | 0.6150 | 0.6124 | 0.9473   |
| No log        | 18.0  | 396  | 0.2931          | 0.6017    | 0.6135 | 0.6075 | 0.9471   |
| No log        | 19.0  | 418  | 0.2935          | 0.6103    | 0.6206 | 0.6154 | 0.9476   |
| No log        | 20.0  | 440  | 0.2936          | 0.6097    | 0.6192 | 0.6144 | 0.9479   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1