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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: 0.3404
- Precision: 0.9296
- Recall: 0.9386
- F1: 0.9341
- Accuracy: 0.9304

## 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: 5
- eval_batch_size: 5
- 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        | 4.17  | 250  | 0.9987          | 0.7470    | 0.7957 | 0.7706 | 0.8043   |
| 1.3632        | 8.33  | 500  | 0.5299          | 0.8641    | 0.8855 | 0.8747 | 0.8829   |
| 1.3632        | 12.5  | 750  | 0.3861          | 0.8853    | 0.9124 | 0.8986 | 0.9126   |
| 0.3151        | 16.67 | 1000 | 0.3392          | 0.9154    | 0.9311 | 0.9232 | 0.9321   |
| 0.3151        | 20.83 | 1250 | 0.3382          | 0.9247    | 0.9371 | 0.9309 | 0.9308   |
| 0.1265        | 25.0  | 1500 | 0.3364          | 0.9225    | 0.9356 | 0.9290 | 0.9300   |
| 0.1265        | 29.17 | 1750 | 0.3333          | 0.9304    | 0.9401 | 0.9352 | 0.9321   |
| 0.0716        | 33.33 | 2000 | 0.3381          | 0.9296    | 0.9394 | 0.9345 | 0.9312   |
| 0.0716        | 37.5  | 2250 | 0.3474          | 0.9290    | 0.9409 | 0.9349 | 0.9321   |
| 0.0525        | 41.67 | 2500 | 0.3404          | 0.9296    | 0.9386 | 0.9341 | 0.9304   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- 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": 0.9295774647887324, "name": "Precision"}, {"type": "recall", "value": 0.938622754491018, "name": "Recall"}, {"type": "f1", "value": 0.9340782122905028, "name": "F1"}, {"type": "accuracy", "value": 0.9303904923599321, "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: 0.3404
* Precision: 0.9296
* Recall: 0.9386
* F1: 0.9341
* Accuracy: 0.9304


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: 5
* eval\_batch\_size: 5
* 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 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* 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 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* 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: 5
* eval\_batch\_size: 5
* 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 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* 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: 5
* eval\_batch\_size: 5
* 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 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2