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# layoutlmv3-finetuned-DocLayNet

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the doc_lay_net-small dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5644
- Precision: 0.6179
- Recall: 0.7238
- F1: 0.6667
- Accuracy: 0.8720

## 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: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.3383        | 0.58  | 200  | 0.8358          | 0.3007    | 0.4381 | 0.3566 | 0.7724   |
| 0.8308        | 1.16  | 400  | 0.6735          | 0.4634    | 0.5429 | 0.5    | 0.8084   |
| 0.518         | 1.74  | 600  | 0.5706          | 0.5373    | 0.6857 | 0.6025 | 0.8399   |
| 0.3856        | 2.33  | 800  | 0.6303          | 0.6032    | 0.7238 | 0.6580 | 0.8648   |
| 0.2558        | 2.91  | 1000 | 0.5644          | 0.6179    | 0.7238 | 0.6667 | 0.8720   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2



### How to Train & Inference:

Check this out this repo: https://github.com/mit1280/Document-AI
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer", "layoutlmv3", "token_classifier", "layout_analysis"], "datasets": ["pierreguillou/DocLayNet-small"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification", "model-index": [{"name": "layoutlmv3-finetuned-DocLayNet", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "doc_lay_net-small", "type": "doc_lay_net-small", "config": "DocLayNet_2022.08_processed_on_2023.01", "split": "test", "args": "DocLayNet_2022.08_processed_on_2023.01"}, "metrics": [{"type": "precision", "value": 0.6178861788617886, "name": "Precision"}, {"type": "recall", "value": 0.7238095238095238, "name": "Recall"}, {"type": "f1", "value": 0.6666666666666667, "name": "F1"}, {"type": "accuracy", "value": 0.8719611021069692, "name": "Accuracy"}]}]}]}
transformers pytorch layoutlmv3 token-classification generated_from_trainer token_classifier layout_analysis en dataset:pierreguillou/DocLayNet-small license:cc-by-nc-sa-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #token_classifier #layout_analysis #en #dataset-pierreguillou/DocLayNet-small #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

layoutlmv3-finetuned-DocLayNet
==============================


This model is a fine-tuned version of microsoft/layoutlmv3-base on the doc\_lay\_net-small dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5644
* Precision: 0.6179
* Recall: 0.7238
* F1: 0.6667
* Accuracy: 0.8720


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: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2


### How to Train & Inference:


Check this out this repo: URL



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2 ### How to Train & Inference:


Check this out this repo: URL
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #token_classifier #layout_analysis #en #dataset-pierreguillou/DocLayNet-small #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2 ### How to Train & Inference:


Check this out this repo: URL
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #token_classifier #layout_analysis #en #dataset-pierreguillou/DocLayNet-small #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000### Training results### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2### How to Train & Inference:


Check this out this repo: URL