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# layoutlmv3-testing-v0

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6958
- Precision: 0.2244
- Recall: 0.2148
- F1: 0.2195
- Accuracy: 0.3239

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.59  | 10   | 3.1329          | 0.0147    | 0.0143 | 0.0145 | 0.1690   |
| No log        | 1.18  | 20   | 2.9569          | 0.1115    | 0.0716 | 0.0872 | 0.2069   |
| No log        | 1.76  | 30   | 2.8145          | 0.2026    | 0.1480 | 0.1710 | 0.2482   |
| No log        | 2.35  | 40   | 2.7292          | 0.2158    | 0.1957 | 0.2053 | 0.3014   |
| No log        | 2.94  | 50   | 2.6958          | 0.2244    | 0.2148 | 0.2195 | 0.3239   |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-testing-v0", "results": []}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

layoutlmv3-testing-v0
=====================


This model is a fine-tuned version of microsoft/layoutlmv3-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.6958
* Precision: 0.2244
* Recall: 0.2148
* F1: 0.2195
* Accuracy: 0.3239


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 50


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.8.0+cu101
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 50 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.8.0+cu101
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 50 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.8.0+cu101
* Datasets 2.12.0
* Tokenizers 0.13.3