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# layoutlmv3-finetuned-cord_100

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2163
- Precision: 0.9480
- Recall: 0.9551
- F1: 0.9515
- Accuracy: 0.9563

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.12  | 250  | 0.8807          | 0.7910    | 0.8301 | 0.8101 | 0.8247   |
| 1.2597        | 6.25  | 500  | 0.4375          | 0.8808    | 0.9072 | 0.8938 | 0.9117   |
| 1.2597        | 9.38  | 750  | 0.3119          | 0.9185    | 0.9364 | 0.9274 | 0.9423   |
| 0.2869        | 12.5  | 1000 | 0.2700          | 0.9340    | 0.9424 | 0.9382 | 0.9452   |
| 0.2869        | 15.62 | 1250 | 0.2401          | 0.9429    | 0.9513 | 0.9471 | 0.9559   |
| 0.1378        | 18.75 | 1500 | 0.2295          | 0.9415    | 0.9521 | 0.9468 | 0.9550   |
| 0.1378        | 21.88 | 1750 | 0.2183          | 0.9523    | 0.9566 | 0.9544 | 0.9580   |
| 0.0866        | 25.0  | 2000 | 0.2190          | 0.9523    | 0.9566 | 0.9544 | 0.9576   |
| 0.0866        | 28.12 | 2250 | 0.2168          | 0.9508    | 0.9551 | 0.9529 | 0.9567   |
| 0.066         | 31.25 | 2500 | 0.2163          | 0.9480    | 0.9551 | 0.9515 | 0.9563   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["cord-layoutlmv3"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-cord_100", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "cord-layoutlmv3", "type": "cord-layoutlmv3", "config": "cord", "split": "test", "args": "cord"}, "metrics": [{"type": "precision", "value": 0.9479940564635958, "name": "Precision"}, {"type": "recall", "value": 0.9550898203592815, "name": "Recall"}, {"type": "f1", "value": 0.9515287099179717, "name": "F1"}, {"type": "accuracy", "value": 0.9562818336162988, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:cord-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-cord-layoutlmv3 #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

layoutlmv3-finetuned-cord\_100
==============================


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


* Loss: 0.2163
* Precision: 0.9480
* Recall: 0.9551
* F1: 0.9515
* Accuracy: 0.9563


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: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-cord-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: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-cord-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: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2