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# perioli_vgm_v4.1

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: 0.1084
- Precision: 0.6981
- Recall: 0.6789
- F1: 0.6884
- Accuracy: 0.9881

## 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: 1500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.98  | 100  | 0.0735          | 0.7849    | 0.6697 | 0.7228 | 0.9901   |
| No log        | 1.96  | 200  | 0.0688          | 0.6638    | 0.7064 | 0.6844 | 0.9905   |
| No log        | 2.94  | 300  | 0.0827          | 0.6636    | 0.6697 | 0.6667 | 0.9877   |
| No log        | 3.92  | 400  | 0.0717          | 0.7170    | 0.6972 | 0.7070 | 0.9901   |
| 0.0088        | 4.9   | 500  | 0.0715          | 0.6667    | 0.6422 | 0.6542 | 0.9881   |
| 0.0088        | 5.88  | 600  | 0.0868          | 0.7059    | 0.6606 | 0.6825 | 0.9877   |
| 0.0088        | 6.86  | 700  | 0.0947          | 0.6491    | 0.6789 | 0.6637 | 0.9881   |
| 0.0088        | 7.84  | 800  | 0.0796          | 0.7103    | 0.6972 | 0.7037 | 0.9894   |
| 0.0088        | 8.82  | 900  | 0.0777          | 0.7196    | 0.7064 | 0.7130 | 0.9905   |
| 0.0027        | 9.8   | 1000 | 0.0958          | 0.7075    | 0.6881 | 0.6977 | 0.9877   |
| 0.0027        | 10.78 | 1100 | 0.1044          | 0.71      | 0.6514 | 0.6794 | 0.9874   |
| 0.0027        | 11.76 | 1200 | 0.1140          | 0.6981    | 0.6789 | 0.6884 | 0.9874   |
| 0.0027        | 12.75 | 1300 | 0.1106          | 0.6981    | 0.6789 | 0.6884 | 0.9881   |
| 0.0027        | 13.73 | 1400 | 0.1069          | 0.6981    | 0.6789 | 0.6884 | 0.9881   |
| 0.0009        | 14.71 | 1500 | 0.1084          | 0.6981    | 0.6789 | 0.6884 | 0.9881   |


### 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_v4.1", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.6981132075471698, "name": "Precision"}, {"type": "recall", "value": 0.6788990825688074, "name": "Recall"}, {"type": "f1", "value": 0.6883720930232559, "name": "F1"}, {"type": "accuracy", "value": 0.9880871341048332, "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\_v4.1
==================


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: 0.1084
* Precision: 0.6981
* Recall: 0.6789
* F1: 0.6884
* Accuracy: 0.9881


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: 1500


### 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: 1500 ### 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: 1500 ### 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: 1500### Training results### Framework versions


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