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# perioli_vgm_v4.4

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0314
- Precision: 0.9085
- Recall: 0.9021
- F1: 0.9053
- Accuracy: 0.9968

## 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: 1200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.81  | 100  | 0.0901          | 0.5385    | 0.2937 | 0.3801 | 0.9800   |
| No log        | 1.63  | 200  | 0.0517          | 0.6879    | 0.6783 | 0.6831 | 0.9896   |
| No log        | 2.44  | 300  | 0.0479          | 0.8169    | 0.8112 | 0.8140 | 0.9938   |
| No log        | 3.25  | 400  | 0.0354          | 0.8219    | 0.8392 | 0.8304 | 0.9951   |
| 0.0712        | 4.07  | 500  | 0.0278          | 0.84      | 0.8811 | 0.8601 | 0.9951   |
| 0.0712        | 4.88  | 600  | 0.0326          | 0.8299    | 0.8531 | 0.8414 | 0.9953   |
| 0.0712        | 5.69  | 700  | 0.0323          | 0.8493    | 0.8671 | 0.8581 | 0.9955   |
| 0.0712        | 6.5   | 800  | 0.0264          | 0.8819    | 0.8881 | 0.8850 | 0.9960   |
| 0.0712        | 7.32  | 900  | 0.0297          | 0.9085    | 0.9021 | 0.9053 | 0.9968   |
| 0.0085        | 8.13  | 1000 | 0.0335          | 0.8873    | 0.8811 | 0.8842 | 0.9965   |
| 0.0085        | 8.94  | 1100 | 0.0319          | 0.9014    | 0.8951 | 0.8982 | 0.9965   |
| 0.0085        | 9.76  | 1200 | 0.0314          | 0.9085    | 0.9021 | 0.9053 | 0.9968   |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "perioli_vgm_v4.4", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9084507042253521, "name": "Precision"}, {"type": "recall", "value": 0.9020979020979021, "name": "Recall"}, {"type": "f1", "value": 0.9052631578947369, "name": "F1"}, {"type": "accuracy", "value": 0.9967829745112596, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:sroie 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-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

perioli\_vgm\_v4.4
==================


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


* Loss: 0.0314
* Precision: 0.9085
* Recall: 0.9021
* F1: 0.9053
* Accuracy: 0.9968


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: 1200


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* 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: 1200 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #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: 1200 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #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: 1200### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3