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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 funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0068
- Precision: 0.7998
- Recall: 0.8376
- F1: 0.8182
- Accuracy: 0.8261

## 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: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.33  | 250  | 0.5828          | 0.7015    | 0.8033 | 0.7490 | 0.8022   |
| 0.6702        | 6.67  | 500  | 0.5765          | 0.7499    | 0.8073 | 0.7775 | 0.8253   |
| 0.6702        | 10.0  | 750  | 0.7082          | 0.7755    | 0.8236 | 0.7988 | 0.8160   |
| 0.1797        | 13.33 | 1000 | 0.7819          | 0.7807    | 0.8366 | 0.8077 | 0.8256   |
| 0.1797        | 16.67 | 1250 | 0.8199          | 0.7997    | 0.8311 | 0.8151 | 0.8227   |
| 0.0745        | 20.0  | 1500 | 0.9025          | 0.7943    | 0.8286 | 0.8111 | 0.8231   |
| 0.0745        | 23.33 | 1750 | 0.9159          | 0.7941    | 0.8470 | 0.8197 | 0.8248   |
| 0.041         | 26.67 | 2000 | 1.0012          | 0.7989    | 0.8385 | 0.8182 | 0.8210   |
| 0.041         | 30.0  | 2250 | 0.9852          | 0.8024    | 0.8450 | 0.8231 | 0.8301   |
| 0.0246        | 33.33 | 2500 | 1.0068          | 0.7998    | 0.8376 | 0.8182 | 0.8261   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["funsd"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-funsd", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "funsd", "type": "funsd", "config": "funsd", "split": "test", "args": "funsd"}, "metrics": [{"type": "precision", "value": 0.7998102466793169, "name": "Precision"}, {"type": "recall", "value": 0.8375558867362146, "name": "Recall"}, {"type": "f1", "value": 0.8182479980587235, "name": "F1"}, {"type": "accuracy", "value": 0.826102460477832, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:funsd 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-funsd #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

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


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


* Loss: 1.0068
* Precision: 0.7998
* Recall: 0.8376
* F1: 0.8182
* Accuracy: 0.8261


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: 2500


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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: 2500 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-funsd #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: 2500 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-funsd #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: 2500### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3