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# layoutlmv3-finetuned-cord

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the CORD dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1845
- Precision: 0.9620
- Recall: 0.9656
- F1: 0.9638
- Accuracy: 0.9682

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: 5e-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        | 2.0   | 100  | 0.5257          | 0.8223    | 0.8555 | 0.8386 | 0.8710   |
| No log        | 4.0   | 200  | 0.3200          | 0.9118    | 0.9281 | 0.9199 | 0.9317   |
| No log        | 6.0   | 300  | 0.2449          | 0.9298    | 0.9424 | 0.9361 | 0.9465   |
| No log        | 8.0   | 400  | 0.1923          | 0.9472    | 0.9536 | 0.9504 | 0.9597   |
| 0.4328        | 10.0  | 500  | 0.1857          | 0.9591    | 0.9656 | 0.9623 | 0.9682   |
| 0.4328        | 12.0  | 600  | 0.2073          | 0.9597    | 0.9618 | 0.9607 | 0.9656   |
| 0.4328        | 14.0  | 700  | 0.1804          | 0.9634    | 0.9663 | 0.9649 | 0.9703   |
| 0.4328        | 16.0  | 800  | 0.1882          | 0.9634    | 0.9648 | 0.9641 | 0.9665   |
| 0.4328        | 18.0  | 900  | 0.1800          | 0.9619    | 0.9648 | 0.9634 | 0.9677   |
| 0.0318        | 20.0  | 1000 | 0.1845          | 0.9620    | 0.9656 | 0.9638 | 0.9682   |


### 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": ["cord"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/layoutlmv3-base", "model-index": [{"name": "layoutlmv3-finetuned-cord", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "cord", "type": "cord", "args": "cord"}, "metrics": [{"type": "precision", "value": 0.9619686800894854, "name": "Precision"}, {"type": "recall", "value": 0.9655688622754491, "name": "Recall"}, {"type": "f1", "value": 0.9637654090399701, "name": "F1"}, {"type": "accuracy", "value": 0.9681663837011885, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors layoutlmv3 token-classification generated_from_trainer dataset:cord 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-cord #base_model-microsoft/layoutlmv3-base #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

layoutlmv3-finetuned-cord
=========================


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


* Loss: 0.1845
* Precision: 0.9620
* Recall: 0.9656
* F1: 0.9638
* Accuracy: 0.9682


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: 5e-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: 5e-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-cord #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: 5e-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-cord #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: 5e-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