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# LOC1.0

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: 0.3003
- Precision: 0.6609
- Recall: 0.7170
- F1: 0.6878
- Accuracy: 0.9565

## 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        | 3.7   | 100  | 0.3041          | 0.7009    | 0.7075 | 0.7042 | 0.9585   |
| No log        | 7.41  | 200  | 0.3411          | 0.5682    | 0.7075 | 0.6303 | 0.9406   |
| No log        | 11.11 | 300  | 0.1978          | 0.664     | 0.7830 | 0.7186 | 0.9606   |
| No log        | 14.81 | 400  | 0.2985          | 0.6667    | 0.7170 | 0.6909 | 0.9585   |
| 0.0194        | 18.52 | 500  | 0.2931          | 0.6239    | 0.6415 | 0.6326 | 0.9575   |
| 0.0194        | 22.22 | 600  | 0.2981          | 0.6697    | 0.6887 | 0.6791 | 0.9544   |
| 0.0194        | 25.93 | 700  | 0.3242          | 0.6757    | 0.7075 | 0.6912 | 0.9565   |
| 0.0194        | 29.63 | 800  | 0.2913          | 0.7255    | 0.6981 | 0.7115 | 0.9565   |
| 0.0194        | 33.33 | 900  | 0.2944          | 0.592     | 0.6981 | 0.6407 | 0.9503   |
| 0.012         | 37.04 | 1000 | 0.2923          | 0.7404    | 0.7264 | 0.7333 | 0.9549   |
| 0.012         | 40.74 | 1100 | 0.3083          | 0.7451    | 0.7170 | 0.7308 | 0.9539   |
| 0.012         | 44.44 | 1200 | 0.3142          | 0.6667    | 0.7170 | 0.6909 | 0.9513   |
| 0.012         | 48.15 | 1300 | 0.2950          | 0.6909    | 0.7170 | 0.7037 | 0.9539   |
| 0.012         | 51.85 | 1400 | 0.2958          | 0.6852    | 0.6981 | 0.6916 | 0.9534   |
| 0.0105        | 55.56 | 1500 | 0.2882          | 0.7       | 0.7264 | 0.7130 | 0.9590   |
| 0.0105        | 59.26 | 1600 | 0.2954          | 0.7476    | 0.7264 | 0.7368 | 0.9600   |
| 0.0105        | 62.96 | 1700 | 0.2975          | 0.6696    | 0.7264 | 0.6968 | 0.9580   |
| 0.0105        | 66.67 | 1800 | 0.2986          | 0.6522    | 0.7075 | 0.6787 | 0.9565   |
| 0.0105        | 70.37 | 1900 | 0.3011          | 0.6609    | 0.7170 | 0.6878 | 0.9565   |
| 0.0081        | 74.07 | 2000 | 0.3003          | 0.6609    | 0.7170 | 0.6878 | 0.9565   |


### 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": "LOC1.0", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.6608695652173913, "name": "Precision"}, {"type": "recall", "value": 0.7169811320754716, "name": "Recall"}, {"type": "f1", "value": 0.6877828054298641, "name": "F1"}, {"type": "accuracy", "value": 0.9564549180327869, "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 

LOC1.0
======


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: 0.3003
* Precision: 0.6609
* Recall: 0.7170
* F1: 0.6878
* Accuracy: 0.9565


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