
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# perioli_manifesti_v5.5

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: 0.0275
- Precision: 0.9266
- Recall: 0.9524
- F1: 0.9393
- Accuracy: 0.9936

## 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: 3500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.47  | 100  | 0.1417          | 0.7345    | 0.8005 | 0.7661 | 0.9660   |
| No log        | 0.94  | 200  | 0.0495          | 0.8930    | 0.9218 | 0.9072 | 0.9884   |
| No log        | 1.42  | 300  | 0.0597          | 0.8416    | 0.9263 | 0.8820 | 0.9812   |
| No log        | 1.89  | 400  | 0.0368          | 0.9109    | 0.9551 | 0.9325 | 0.9912   |
| 0.137         | 2.36  | 500  | 0.0357          | 0.9051    | 0.9515 | 0.9277 | 0.9901   |
| 0.137         | 2.83  | 600  | 0.0297          | 0.9245    | 0.9578 | 0.9409 | 0.9926   |
| 0.137         | 3.3   | 700  | 0.0294          | 0.9157    | 0.9560 | 0.9354 | 0.9918   |
| 0.137         | 3.77  | 800  | 0.0314          | 0.9190    | 0.9587 | 0.9384 | 0.9922   |
| 0.137         | 4.25  | 900  | 0.0252          | 0.9348    | 0.9659 | 0.9501 | 0.9939   |
| 0.0226        | 4.72  | 1000 | 0.0300          | 0.9307    | 0.9659 | 0.9480 | 0.9934   |
| 0.0226        | 5.19  | 1100 | 0.0295          | 0.9213    | 0.9569 | 0.9387 | 0.9926   |
| 0.0226        | 5.66  | 1200 | 0.0279          | 0.9283    | 0.9650 | 0.9463 | 0.9934   |
| 0.0226        | 6.13  | 1300 | 0.0258          | 0.9234    | 0.9641 | 0.9433 | 0.9932   |
| 0.0226        | 6.6   | 1400 | 0.0255          | 0.9275    | 0.9650 | 0.9458 | 0.9939   |
| 0.0161        | 7.08  | 1500 | 0.0319          | 0.9257    | 0.9623 | 0.9436 | 0.9932   |
| 0.0161        | 7.55  | 1600 | 0.0235          | 0.9331    | 0.9650 | 0.9488 | 0.9944   |
| 0.0161        | 8.02  | 1700 | 0.0308          | 0.9198    | 0.9587 | 0.9388 | 0.9925   |
| 0.0161        | 8.49  | 1800 | 0.0292          | 0.9265    | 0.9632 | 0.9445 | 0.9930   |
| 0.0161        | 8.96  | 1900 | 0.0251          | 0.9282    | 0.9641 | 0.9458 | 0.9934   |
| 0.0106        | 9.43  | 2000 | 0.0223          | 0.9229    | 0.9578 | 0.9400 | 0.9932   |
| 0.0106        | 9.91  | 2100 | 0.0253          | 0.9223    | 0.9596 | 0.9406 | 0.9930   |
| 0.0106        | 10.38 | 2200 | 0.0299          | 0.9196    | 0.9560 | 0.9374 | 0.9929   |
| 0.0106        | 10.85 | 2300 | 0.0240          | 0.9235    | 0.9542 | 0.9386 | 0.9934   |
| 0.0106        | 11.32 | 2400 | 0.0289          | 0.9166    | 0.9479 | 0.9320 | 0.9922   |
| 0.0079        | 11.79 | 2500 | 0.0236          | 0.9196    | 0.9452 | 0.9322 | 0.9923   |
| 0.0079        | 12.26 | 2600 | 0.0271          | 0.9234    | 0.9533 | 0.9381 | 0.9930   |
| 0.0079        | 12.74 | 2700 | 0.0267          | 0.9337    | 0.9614 | 0.9473 | 0.9943   |
| 0.0079        | 13.21 | 2800 | 0.0277          | 0.9337    | 0.9614 | 0.9473 | 0.9943   |
| 0.0079        | 13.68 | 2900 | 0.0279          | 0.9350    | 0.9686 | 0.9515 | 0.9946   |
| 0.0058        | 14.15 | 3000 | 0.0282          | 0.9240    | 0.9497 | 0.9366 | 0.9925   |
| 0.0058        | 14.62 | 3100 | 0.0281          | 0.9260    | 0.9551 | 0.9403 | 0.9937   |
| 0.0058        | 15.09 | 3200 | 0.0258          | 0.9248    | 0.9506 | 0.9375 | 0.9934   |
| 0.0058        | 15.57 | 3300 | 0.0253          | 0.9300    | 0.9551 | 0.9424 | 0.9940   |
| 0.0058        | 16.04 | 3400 | 0.0271          | 0.9248    | 0.9506 | 0.9375 | 0.9934   |
| 0.0037        | 16.51 | 3500 | 0.0275          | 0.9266    | 0.9524 | 0.9393 | 0.9936   |


### 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": "perioli_manifesti_v5.5", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9265734265734266, "name": "Precision"}, {"type": "recall", "value": 0.9523809523809523, "name": "Recall"}, {"type": "f1", "value": 0.9392999556933984, "name": "F1"}, {"type": "accuracy", "value": 0.993588850174216, "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\_manifesti\_v5.5
========================


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: 0.0275
* Precision: 0.9266
* Recall: 0.9524
* F1: 0.9393
* Accuracy: 0.9936


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: 3500


### 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: 3500 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+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: 3500 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3