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# invoice_clients_CAD.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: 0.1746
- Precision: 0.6680
- Recall: 0.5923
- F1: 0.6279
- Accuracy: 0.9624

## 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        | 0.78  | 100  | 0.4639          | 0.3014    | 0.0383 | 0.0680 | 0.8999   |
| No log        | 1.55  | 200  | 0.3762          | 0.2624    | 0.0645 | 0.1035 | 0.9078   |
| No log        | 2.33  | 300  | 0.3180          | 0.3872    | 0.2840 | 0.3276 | 0.9303   |
| No log        | 3.1   | 400  | 0.2577          | 0.5460    | 0.3310 | 0.4121 | 0.9414   |
| 0.4395        | 3.88  | 500  | 0.2410          | 0.5443    | 0.3641 | 0.4363 | 0.9477   |
| 0.4395        | 4.65  | 600  | 0.2205          | 0.6449    | 0.4303 | 0.5162 | 0.9525   |
| 0.4395        | 5.43  | 700  | 0.2154          | 0.5960    | 0.4164 | 0.4903 | 0.9508   |
| 0.4395        | 6.2   | 800  | 0.2252          | 0.5841    | 0.4233 | 0.4909 | 0.9532   |
| 0.4395        | 6.98  | 900  | 0.2039          | 0.6397    | 0.4826 | 0.5501 | 0.9582   |
| 0.1677        | 7.75  | 1000 | 0.1888          | 0.6080    | 0.5052 | 0.5519 | 0.9593   |
| 0.1677        | 8.53  | 1100 | 0.1869          | 0.5599    | 0.4965 | 0.5263 | 0.9577   |
| 0.1677        | 9.3   | 1200 | 0.1701          | 0.5833    | 0.5366 | 0.5590 | 0.9594   |
| 0.1677        | 10.08 | 1300 | 0.1786          | 0.6292    | 0.5557 | 0.5902 | 0.9612   |
| 0.1677        | 10.85 | 1400 | 0.1745          | 0.6356    | 0.5470 | 0.5880 | 0.9600   |
| 0.0902        | 11.63 | 1500 | 0.1746          | 0.6346    | 0.5627 | 0.5965 | 0.9623   |
| 0.0902        | 12.4  | 1600 | 0.1687          | 0.5975    | 0.5819 | 0.5896 | 0.9606   |
| 0.0902        | 13.18 | 1700 | 0.1736          | 0.6594    | 0.5836 | 0.6192 | 0.9631   |
| 0.0902        | 13.95 | 1800 | 0.1766          | 0.6530    | 0.5836 | 0.6164 | 0.9624   |
| 0.0902        | 14.73 | 1900 | 0.1712          | 0.6737    | 0.6080 | 0.6392 | 0.9623   |
| 0.0584        | 15.5  | 2000 | 0.1746          | 0.6680    | 0.5923 | 0.6279 | 0.9624   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+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": "invoice_clients_CAD.0", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.6679764243614931, "name": "Precision"}, {"type": "recall", "value": 0.5923344947735192, "name": "Recall"}, {"type": "f1", "value": 0.6278855032317636, "name": "F1"}, {"type": "accuracy", "value": 0.9623935666982024, "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 has_space 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 #has_space #region-us 

invoice\_clients\_CAD.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: 0.1746
* Precision: 0.6680
* Recall: 0.5923
* F1: 0.6279
* Accuracy: 0.9624


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 2.0.1+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 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3