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# perioli_vgm_v5.0

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: 0.0111
- Precision: 0.9355
- Recall: 0.9667
- F1: 0.9508
- Accuracy: 0.9984

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.55  | 100  | 0.0929          | 0.5373    | 0.48   | 0.5070 | 0.9805   |
| No log        | 1.1   | 200  | 0.0480          | 0.7325    | 0.7667 | 0.7492 | 0.9897   |
| No log        | 1.65  | 300  | 0.0425          | 0.6536    | 0.78   | 0.7112 | 0.9910   |
| No log        | 2.2   | 400  | 0.0290          | 0.8543    | 0.86   | 0.8571 | 0.9953   |
| 0.0746        | 2.75  | 500  | 0.0210          | 0.8627    | 0.88   | 0.8713 | 0.9963   |
| 0.0746        | 3.3   | 600  | 0.0182          | 0.8792    | 0.8733 | 0.8763 | 0.9963   |
| 0.0746        | 3.85  | 700  | 0.0113          | 0.8926    | 0.8867 | 0.8896 | 0.9974   |
| 0.0746        | 4.4   | 800  | 0.0119          | 0.9231    | 0.96   | 0.9412 | 0.9982   |
| 0.0746        | 4.95  | 900  | 0.0101          | 0.9290    | 0.96   | 0.9443 | 0.9984   |
| 0.0114        | 5.49  | 1000 | 0.0110          | 0.9329    | 0.9267 | 0.9298 | 0.9976   |
| 0.0114        | 6.04  | 1100 | 0.0132          | 0.9172    | 0.96   | 0.9381 | 0.9984   |
| 0.0114        | 6.59  | 1200 | 0.0110          | 0.9338    | 0.94   | 0.9369 | 0.9979   |
| 0.0114        | 7.14  | 1300 | 0.0114          | 0.9542    | 0.9733 | 0.9637 | 0.9984   |
| 0.0114        | 7.69  | 1400 | 0.0134          | 0.9355    | 0.9667 | 0.9508 | 0.9982   |
| 0.0035        | 8.24  | 1500 | 0.0132          | 0.9295    | 0.9667 | 0.9477 | 0.9982   |
| 0.0035        | 8.79  | 1600 | 0.0112          | 0.9295    | 0.9667 | 0.9477 | 0.9982   |
| 0.0035        | 9.34  | 1700 | 0.0098          | 0.9295    | 0.9667 | 0.9477 | 0.9982   |
| 0.0035        | 9.89  | 1800 | 0.0094          | 0.9295    | 0.9667 | 0.9477 | 0.9987   |
| 0.0035        | 10.44 | 1900 | 0.0115          | 0.9355    | 0.9667 | 0.9508 | 0.9984   |
| 0.0018        | 10.99 | 2000 | 0.0111          | 0.9355    | 0.9667 | 0.9508 | 0.9984   |


### Framework versions

- Transformers 4.28.0
- 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_v5.0", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9354838709677419, "name": "Precision"}, {"type": "recall", "value": 0.9666666666666667, "name": "Recall"}, {"type": "f1", "value": 0.9508196721311476, "name": "F1"}, {"type": "accuracy", "value": 0.9984185556141276, "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\_v5.0
==================


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: 0.0111
* Precision: 0.9355
* Recall: 0.9667
* F1: 0.9508
* Accuracy: 0.9984


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: 2000


### Training results



### Framework versions


* Transformers 4.28.0
* 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: 2000 ### Training results ### Framework versions


* Transformers 4.28.0
* 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: 2000 ### Training results ### Framework versions


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