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# perioli_vgm_v4.2

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: 0.0247
- Precision: 0.8814
- Recall: 0.8595
- F1: 0.8703
- Accuracy: 0.9956

## 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        | 0.84  | 100  | 0.1047          | 0.4194    | 0.2149 | 0.2842 | 0.9764   |
| No log        | 1.68  | 200  | 0.0555          | 0.5441    | 0.6116 | 0.5759 | 0.9843   |
| No log        | 2.52  | 300  | 0.0445          | 0.5899    | 0.6777 | 0.6308 | 0.9879   |
| No log        | 3.36  | 400  | 0.0288          | 0.7402    | 0.7769 | 0.7581 | 0.9929   |
| 0.0777        | 4.2   | 500  | 0.0292          | 0.8033    | 0.8099 | 0.8066 | 0.9938   |
| 0.0777        | 5.04  | 600  | 0.0172          | 0.8321    | 0.9008 | 0.8651 | 0.9962   |
| 0.0777        | 5.88  | 700  | 0.0321          | 0.8067    | 0.7934 | 0.8    | 0.9932   |
| 0.0777        | 6.72  | 800  | 0.0165          | 0.8862    | 0.9008 | 0.8934 | 0.9967   |
| 0.0777        | 7.56  | 900  | 0.0318          | 0.8644    | 0.8430 | 0.8536 | 0.9953   |
| 0.0093        | 8.4   | 1000 | 0.0247          | 0.8814    | 0.8595 | 0.8703 | 0.9956   |
| 0.0093        | 9.24  | 1100 | 0.0220          | 0.8678    | 0.8678 | 0.8678 | 0.9962   |
| 0.0093        | 10.08 | 1200 | 0.0183          | 0.8607    | 0.8678 | 0.8642 | 0.9965   |
| 0.0093        | 10.92 | 1300 | 0.0269          | 0.8739    | 0.8595 | 0.8667 | 0.9959   |
| 0.0093        | 11.76 | 1400 | 0.0171          | 0.8387    | 0.8595 | 0.8490 | 0.9965   |
| 0.0035        | 12.61 | 1500 | 0.0201          | 0.8607    | 0.8678 | 0.8642 | 0.9965   |


### 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_v4.2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.8813559322033898, "name": "Precision"}, {"type": "recall", "value": 0.859504132231405, "name": "Recall"}, {"type": "f1", "value": 0.8702928870292886, "name": "F1"}, {"type": "accuracy", "value": 0.9955660656222288, "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\_v4.2
==================


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: 0.0247
* Precision: 0.8814
* Recall: 0.8595
* F1: 0.8703
* Accuracy: 0.9956


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