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# layoutlmv3-finetuned-funsd

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1164
- Precision: 0.9026
- Recall: 0.913
- F1: 0.9078
- Accuracy: 0.8330

The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 10.0  | 100  | 0.5238          | 0.8366    | 0.886  | 0.8606 | 0.8410   |
| No log        | 20.0  | 200  | 0.6930          | 0.8751    | 0.8965 | 0.8857 | 0.8322   |
| No log        | 30.0  | 300  | 0.7784          | 0.8902    | 0.908  | 0.8990 | 0.8414   |
| No log        | 40.0  | 400  | 0.9056          | 0.8916    | 0.905  | 0.8983 | 0.8364   |
| 0.2429        | 50.0  | 500  | 1.0016          | 0.8954    | 0.9075 | 0.9014 | 0.8298   |
| 0.2429        | 60.0  | 600  | 1.0097          | 0.8899    | 0.897  | 0.8934 | 0.8294   |
| 0.2429        | 70.0  | 700  | 1.0722          | 0.9035    | 0.9085 | 0.9060 | 0.8315   |
| 0.2429        | 80.0  | 800  | 1.0884          | 0.8905    | 0.9105 | 0.9004 | 0.8269   |
| 0.2429        | 90.0  | 900  | 1.1292          | 0.8938    | 0.909  | 0.9013 | 0.8279   |
| 0.0098        | 100.0 | 1000 | 1.1164          | 0.9026    | 0.913  | 0.9078 | 0.8330   |


### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6

{"tags": ["generated_from_trainer"], "datasets": ["nielsr/funsd-layoutlmv3"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/layoutlmv3-base", "model-index": [{"name": "layoutlmv3-finetuned-funsd", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "nielsr/funsd-layoutlmv3", "type": "nielsr/funsd-layoutlmv3", "args": "funsd"}, "metrics": [{"type": "precision", "value": 0.9026198714780029, "name": "Precision"}, {"type": "recall", "value": 0.913, "name": "Recall"}, {"type": "f1", "value": 0.9077802634849614, "name": "F1"}, {"type": "accuracy", "value": 0.8330271015158475, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors layoutlmv3 token-classification generated_from_trainer dataset:nielsr/funsd-layoutlmv3 base_model:microsoft/layoutlmv3-base model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-nielsr/funsd-layoutlmv3 #base_model-microsoft/layoutlmv3-base #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

layoutlmv3-finetuned-funsd
==========================


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


* Loss: 1.1164
* Precision: 0.9026
* Recall: 0.913
* F1: 0.9078
* Accuracy: 0.8330


The script for training can be found here: URL


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: 16
* eval\_batch\_size: 16
* 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 4.19.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.19.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-nielsr/funsd-layoutlmv3 #base_model-microsoft/layoutlmv3-base #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.19.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-nielsr/funsd-layoutlmv3 #base_model-microsoft/layoutlmv3-base #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.19.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.0.0
* Tokenizers 0.11.6