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# Bol-4.0-invoicefromclients_LOC_CAD

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: 0.7295
- Precision: 0.5245
- Recall: 0.4697
- F1: 0.4956
- Accuracy: 0.8690

## 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        | 0.32  | 100  | 1.3578          | 0.3480    | 0.0304 | 0.0560 | 0.7636   |
| No log        | 0.63  | 200  | 1.0269          | 0.1777    | 0.0748 | 0.1052 | 0.7962   |
| No log        | 0.95  | 300  | 0.8968          | 0.3288    | 0.1869 | 0.2383 | 0.8180   |
| No log        | 1.27  | 400  | 0.8574          | 0.3945    | 0.2212 | 0.2835 | 0.8227   |
| 0.8908        | 1.58  | 500  | 0.7533          | 0.3144    | 0.2709 | 0.2910 | 0.8181   |
| 0.8908        | 1.9   | 600  | 0.7001          | 0.3913    | 0.3106 | 0.3463 | 0.8414   |
| 0.8908        | 2.22  | 700  | 0.6915          | 0.4998    | 0.3869 | 0.4361 | 0.8572   |
| 0.8908        | 2.53  | 800  | 0.7375          | 0.4331    | 0.3703 | 0.3993 | 0.8475   |
| 0.8908        | 2.85  | 900  | 0.6590          | 0.4682    | 0.3973 | 0.4299 | 0.8633   |
| 0.353         | 3.16  | 1000 | 0.7389          | 0.5479    | 0.4274 | 0.4802 | 0.8650   |
| 0.353         | 3.48  | 1100 | 0.7387          | 0.5568    | 0.4474 | 0.4962 | 0.8635   |
| 0.353         | 3.8   | 1200 | 0.6881          | 0.5011    | 0.4539 | 0.4763 | 0.8707   |
| 0.353         | 4.11  | 1300 | 0.6881          | 0.5159    | 0.4624 | 0.4877 | 0.8684   |
| 0.353         | 4.43  | 1400 | 0.7308          | 0.5532    | 0.4751 | 0.5112 | 0.8713   |
| 0.1947        | 4.75  | 1500 | 0.7295          | 0.5245    | 0.4697 | 0.4956 | 0.8690   |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.2.2
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Bol-4.0-invoicefromclients_LOC_CAD", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.524526678141136, "name": "Precision"}, {"type": "recall", "value": 0.4697495183044316, "name": "Recall"}, {"type": "f1", "value": 0.49562919292539137, "name": "F1"}, {"type": "accuracy", "value": 0.8690496168260894, "name": "Accuracy"}]}]}]}
transformers pytorch 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 #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Bol-4.0-invoicefromclients\_LOC\_CAD
====================================


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: 0.7295
* Precision: 0.5245
* Recall: 0.4697
* F1: 0.4956
* Accuracy: 0.8690


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 0.13.2



### 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 0.13.2
TAGS
#transformers #pytorch #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 0.13.2
TAGS
#transformers #pytorch #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 0.13.2