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# layoutlmv3-finetuned-intellectai

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoice_layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3645
- Precision: 0.7054
- Recall: 0.8541
- F1: 0.7726
- Accuracy: 0.9625

## 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.79  | 50   | 1.7979          | 0.0228    | 0.0541 | 0.0321 | 0.1410   |
| No log        | 1.59  | 100  | 1.2400          | 0.0863    | 0.4216 | 0.1433 | 0.2616   |
| No log        | 2.38  | 150  | 0.8691          | 0.1279    | 0.6919 | 0.2159 | 0.4495   |
| No log        | 3.17  | 200  | 0.6001          | 0.2323    | 0.8162 | 0.3617 | 0.7570   |
| No log        | 3.97  | 250  | 0.4709          | 0.4660    | 0.7784 | 0.5830 | 0.9093   |
| No log        | 4.76  | 300  | 0.3986          | 0.5977    | 0.8270 | 0.6939 | 0.9472   |
| No log        | 5.56  | 350  | 0.3762          | 0.5714    | 0.8216 | 0.6741 | 0.9454   |
| No log        | 6.35  | 400  | 0.3763          | 0.7048    | 0.8649 | 0.7767 | 0.9636   |
| No log        | 7.14  | 450  | 0.3696          | 0.6639    | 0.8541 | 0.7470 | 0.9570   |
| 0.71          | 7.94  | 500  | 0.3645          | 0.7054    | 0.8541 | 0.7726 | 0.9625   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["invoice_layoutlmv3"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-intellectai", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "invoice_layoutlmv3", "type": "invoice_layoutlmv3", "config": "intellectai", "split": "validation", "args": "intellectai"}, "metrics": [{"type": "precision", "value": 0.7053571428571429, "name": "Precision"}, {"type": "recall", "value": 0.8540540540540541, "name": "Recall"}, {"type": "f1", "value": 0.7726161369193154, "name": "F1"}, {"type": "accuracy", "value": 0.9624772313296903, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:invoice_layoutlmv3 license:cc-by-nc-sa-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice_layoutlmv3 #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

layoutlmv3-finetuned-intellectai
================================


This model is a fine-tuned version of microsoft/layoutlmv3-base on the invoice\_layoutlmv3 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3645
* Precision: 0.7054
* Recall: 0.8541
* F1: 0.7726
* Accuracy: 0.9625


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: 500


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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: 500 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice_layoutlmv3 #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: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 500 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice_layoutlmv3 #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: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 500### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2